analysis of nonstationary characteristics for high-speed railway...

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Research Article Analysis of Nonstationary Characteristics for High-Speed Railway Scenarios Tao Zhou , 1,2 Cheng Tao , 1 and Kai Liu 1 1 Institute of Broadband Wireless Mobile Communications, Beijing Jiaotong University, Beijing 100044, China 2 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China Correspondence should be addressed to Tao Zhou; [email protected] Received 21 March 2018; Accepted 29 May 2018; Published 21 June 2018 Academic Editor: Tommi Jamsa Copyright © 2018 Tao Zhou et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is paper presents the analysis of nonstationary characteristics for high-speed railway (HSR) scenarios, according to passive long- term evolution- (LTE-) based channel measurements. e measurement data collected in three typical scenarios, rural, station, and suburban, are processed to obtain the channel impulse responses (CIRs). Based on the CIRs, the nonstationarity of the HSR channel is studied focusing on the stationarity interval, and a four-state Markov chain model is generated to describe the birth- death process of multipath components. e presented results will be useful in dynamic channel modeling for future HSR mobile communication systems. 1. Introduction With the rapid development of high-speed railways (HSRs), there appears a growth in demand for new railway com- munication services, for example, real-time monitoring, train multimedia dispatching, railway emergency commu- nications, railway Internet of ings (IoT), and broadband wireless access for train passengers [1]. To satisfy such ever- increasing requirements, broadband wireless communication systems for HSR have recently attracted much attention in the world. Since 2014, International Union of Railways (UIC) has considered replacing the current global system for mobile communications for railway (GSM-R) with the long- term evolution for railway (LTE-R) [2]. In China, fourth- generation (4G) networks have been deployed along most of HSRs, for a total of 15,000 km by 2014, and as HSRs continue to grow, the dedicated 4G networks that grow with them will exceed 30,000 km in 2020. For future fiſth-generation (5G) mobile communication system, it is reported that one of its aims is to provide high-data-rate access under high mobility scenarios [3]. Since the radio channel determines the performance of broadband wireless mobile communication systems, detailed knowledge and accurate characterization of its parameters in diverse scenarios are vital. e propagation characteristics are in disparity under various HSR environments. In this case, the characterization of HSR channels should consider the influence of scenarios. So far, a wide variety of the studies have concentrated on the long-term fading behavior, involving path loss and shadowing, in multiple HSR scenarios [4–8]. ere are a few research works on the short-term fading behavior, based on wideband channel measurements conducted on HSR. By contrast, the nonstationary behavior, which has been widely studied for vehicle-to-vehicle (V2V) channels [9, 10], is rarely investigated in HSR environments. To fill this research gap, we present the analysis of the nonstationary characteristics for multiple HSR scenarios. Passive channel measurements are conducted for three typical scenarios, rural, station, and suburban, in an HSR LTE network. Considering the interval of stationarity and the dynamic evolution of multipath com- ponents (MPCs), the nonstationary behavior is analyzed and compared in different scenarios. e remainder of this paper is outlined as follows. Section 2 reviews the related work focusing on short-term fading and nonstationary behaviors. In Section 3, our passive channel measurements based on LTE are introduced. en, the nonstationary characteristics are analyzed in Section 4, respectively. Finally, conclusions are drawn in Section 5. Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 1729121, 7 pages https://doi.org/10.1155/2018/1729121

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Page 1: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

Research ArticleAnalysis of Nonstationary Characteristics for High-SpeedRailway Scenarios

Tao Zhou 12 Cheng Tao 1 and Kai Liu1

1 Institute of Broadband Wireless Mobile Communications Beijing Jiaotong University Beijing 100044 China2National Mobile Communications Research Laboratory Southeast University Nanjing 210096 China

Correspondence should be addressed to Tao Zhou taozhouchinagmailcom

Received 21 March 2018 Accepted 29 May 2018 Published 21 June 2018

Academic Editor Tommi Jamsa

Copyright copy 2018 Tao Zhou et alThis is an open access article distributed under the Creative CommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

This paper presents the analysis of nonstationary characteristics for high-speed railway (HSR) scenarios according to passive long-term evolution- (LTE-) based channel measurements The measurement data collected in three typical scenarios rural stationand suburban are processed to obtain the channel impulse responses (CIRs) Based on the CIRs the nonstationarity of the HSRchannel is studied focusing on the stationarity interval and a four-state Markov chain model is generated to describe the birth-death process of multipath components The presented results will be useful in dynamic channel modeling for future HSR mobilecommunication systems

1 Introduction

With the rapid development of high-speed railways (HSRs)there appears a growth in demand for new railway com-munication services for example real-time monitoringtrain multimedia dispatching railway emergency commu-nications railway Internet of Things (IoT) and broadbandwireless access for train passengers [1] To satisfy such ever-increasing requirements broadbandwireless communicationsystems for HSR have recently attracted much attentionin the world Since 2014 International Union of Railways(UIC) has considered replacing the current global system formobile communications for railway (GSM-R) with the long-term evolution for railway (LTE-R) [2] In China fourth-generation (4G) networks have been deployed along most ofHSRs for a total of 15000 km by 2014 and as HSRs continueto grow the dedicated 4G networks that grow with them willexceed 30000 km in 2020 For future fifth-generation (5G)mobile communication system it is reported that one of itsaims is to provide high-data-rate access under high mobilityscenarios [3]

Since the radio channel determines the performance ofbroadband wireless mobile communication systems detailedknowledge and accurate characterization of its parameters indiverse scenarios are vitalThepropagation characteristics are

in disparity under various HSR environments In this casethe characterization of HSR channels should consider theinfluence of scenarios

So far a wide variety of the studies have concentratedon the long-term fading behavior involving path loss andshadowing in multiple HSR scenarios [4ndash8] There are afew research works on the short-term fading behavior basedon wideband channel measurements conducted on HSR Bycontrast the nonstationary behavior which has been widelystudied for vehicle-to-vehicle (V2V) channels [9 10] is rarelyinvestigated in HSR environments To fill this research gapwe present the analysis of the nonstationary characteristicsfor multiple HSR scenarios Passive channel measurementsare conducted for three typical scenarios rural station andsuburban in an HSR LTE network Considering the intervalof stationarity and the dynamic evolution of multipath com-ponents (MPCs) the nonstationary behavior is analyzed andcompared in different scenarios

The remainder of this paper is outlined as followsSection 2 reviews the related work focusing on short-termfading and nonstationary behaviors In Section 3 our passivechannel measurements based on LTE are introduced Thenthe nonstationary characteristics are analyzed in Section 4respectively Finally conclusions are drawn in Section 5

HindawiWireless Communications and Mobile ComputingVolume 2018 Article ID 1729121 7 pageshttpsdoiorg10115520181729121

2 Wireless Communications and Mobile Computing

Table 1 Summary of investigation on short-term fading and nonstationary behaviors in HSR environments

Scenario Short-term fading behavior Nonstationary behaviorFading severity Time dispersion Frequency dispersion Space dispersion Stationarity interval Birth-death process

Viaduct [11 12 16] [11] [11 12] [15] [25] [26]Cutting [13 14 17] [13 14] [13 14] [15] - -Rural [18] [18 19] [19] [18 19] - -Hilly terrain [20] [21] [21] - - -Tunnel [22] [22] [22] - - -Station [23 24] [24] - [24] -Suburbanurban - - - - - -

2 Related Work

Unique HSR scenarios such as viaduct cutting tunnelstation hilly terrain rural and suburban have a significantimpact on propagation characteristics In our previous workfading severity and time-frequency-space dispersion of HSRchannels in the viaduct and cutting scenarios were deeplycharacterized based on measurements using Propsound [11ndash15] In [15] spatial characteristics involving angle of arrival(AOA) root-mean-square (RMS) angle spread (AS) andspatial correlation (SC) were analyzed according to a so-called moving virtual antenna array (VAA) scheme Authorsin [16 17] proposed a statistic model for Ricean K-factorwhich investigated the impact of the viaduct height and thecutting width The WINNER II model [18] and COST 2100TD [19] provided some measurement results of short-termfading behavior for rural scenarios There were also a fewresults of K-factor RMS delay spread (DS) and Dopplerpower spectral density (DPSD) measured in the hilly terrainand tunnel scenarios [20ndash22] Authors in [23 24] presenteddetailed analysis of fading severity and time dispersion inopen-type and semiclosed station scenarios For the non-stationary behavior stationarity interval (SI) in the viaductscenario was investigated based on GSM-R measurementswhich showed that conventional channel models offered SImuch larger than the actualmeasured ones [25]We also triedto adopt a RUN testmethod to obtain the SI for the open-typestation scenario in [24] Additionally a four-state Markovchain was used to model the birth-death (B-D) process ofMPCs in the viaduct scenario [26]

Table 1 summarizes the existing measurement campaignsabout short-term fading and nonstationary behaviors indifferent HSR environments It can be found that thereare a few results of time-frequency-space characteristics insome HSR scenarios However the characterization of thenonstationary behavior is largely neglected inmost scenariosTherefore this paper aims to investigate the nonstationarybehavior in the rural station and suburban scenarios

3 Channel Measurements

31 Measurement Scenarios An LTE network deployed onBeijing to Tianjin (BT) HSR in China was chosen in ourmeasurements [27] It is a hybrid network composed of adedicated network and a common network The architectureof the dedicated network is completely different from the

Table 2 Measurement parameters in the BT HSR LTE network

Scenario Rural Station SuburbaneNB side

Frequency 189 GHz 2605 GHzBandwidth 18 MHz 18 MHzCRS power 122 dBm 122 dBmAntenna type Directional DirectionalAntenna gain 174 dBi 186 dBiHorizontal beamwidth 67 deg 60 degVertical beamwidth 66 deg 49 degElectric tilted angle 3 deg 3 deg

Sounder sideAntenna type Omnidirectional OmnidirectionalAntenna gain 85 dBi 85 dBiSpeed of HST 285 kmh 185 kmh

Network sideDistance between PSs 12 kmHeight of PS 30 mHeight of rail track 10 mDistance between PSand rail track 30 m

common network which adopts building baseband unit(BBU) plus remote radio unit (RRU) to achieve the specialnarrow-strip-shaped coverage instead of the cellular cover-age In this architecture one physical site (PS) deploys twoRRUs which transmit signals by directional antennas inopposite directions along the railway track The RRUs areconnected together via optical fiber and then to a BBU thatis in charge of radio frequency (RF) signal processing

The LTE sounder is placed on a high-speed train (HST)to collect the channel data which experiences multiplescenarios along the BT railway line such as rural stationand suburban In our measurement the rural and stationscenarios are within the coverage of dedicated network whilethe suburban scenario is covered by the common networkThe detailed measurement parameters for different scenariosin the network are listed in Table 2 The carrier frequencyis 189 GHz for the rural and station scenarios and 2605GHz for the suburban scenario When the HST moves into

Wireless Communications and Mobile Computing 3

(a) (b) (c)

Figure 1 Measurement scenarios (a) Rural (b) Station (c) Suburban

Local CRSGeneration

IFFT

Received CRSExtraction

CIRCellSearchBB

Figure 2 The procedure of data processing

the suburban area the speed is decreased from 285 kmh to185 kmh The specification of the directional eNB antennasuch as gain and beamwidth has slight difference in differentfrequencies At Rx side the LTE sounder is connected to atrain-mounted omnidirectional antennaThe average spacingbetween neighboring PSs is around 12 km The height anddistance difference between the PS and the rail track are about20 m and 30 m respectively

The measured scenarios are shown in Figure 1 As forthe rural scenario the transmit antenna is much higher thanthe surroundings which are light forests and a few buildingswith an average height of less than 10 m The link betweenthe transmitter (Tx) and Rx generally has a strong line-of-sight (LoS) component However after a certain distancethe impact of the sparse scatterers will be noticed at the Rxrepresented by non-LoS (NLoS) components With regardto the station scenario in the measurement the HST runsthrough the station without stopping The measured stationcan be regarded as an open-type station with two awningsthat only cover the platform supporting a clear free space overthe rail However the awnings can still produce lots of NLoScomponents to complicate the fading behavior The lengthof the station is 440 m the width of the awning is 14 mand the width of the gap between the two awnings is 9 mSuburban is a transition zone between the rural and urbanThe NLoS components in the suburban environment will bemuch richer than those in the rural environmentThe densityof the buildings in the suburban scenario is similar to that inthe urban scenario but the height of the buildings is lowerSince the measured suburban is close to the urban area someremote high buildings could affect the results

32 Data Processing Baseband data (BB) collected by theLTE sounder in the multilink regions are used for offlineprocessing The procedure of data processing is shown inFigure 2 Firstly cell search is implemented to determine the

cell identity and obtain synchronized frames for extractingreceived CRSs and generating local CRSs Then frequency-domain correlation is used to estimate channel frequencyresponses which can be subsequently transformed to theraw CIRs by inverse fast Fourier transform (IFFT) operation[28]

4 Results and Analysis

41 Stationarity Interval High mobility leads to the viola-tion of wide sense stationary (WSS) condition for wirelesschannels under HSR scenarios The stationarity interval (SI)is defined as the maximum time or distance duration overwhich the channel satisfies the WSS condition It is summa-rized by [29] that there are severalmetrics that can be used formeasuring the SI involving local region of stationarity (LRS)correlation matrix distance (CMD) and spectral divergence(SD) Besides some statistical tests for the WSS of a randomprocess can also be applied to the determination of SI suchas RUN test and reverse arrangement test The LRS methodhas been used to estimate the time interval ofHSR channels in[25]TheRUN test was applied to the RMSDS data to identifythe stationary distance in the station scenario [24] In thispaper we choose the classical LRS approach to characterizethe SI in the measured scenarios

The aim of the LRS method is to find the maximuminterval within which the correlation coefficient between twoconsecutive PDPs exceeds a predefined threshold 119888119905ℎ Thecorrelation coefficient between PDPs is defined as

119888 (119909119896 Δ119909) = 119875 (119909119896) 119875 (119909119896 + Δ119909)max 119875 (119909119896)2 119875 (119909119896 + Δ119909)2

(1)

where

119875 (119909119896) = 1119873119896+119873minus1sum119898=119896

1003816100381610038161003816ℎ (119909119898)10038161003816100381610038162 (2)

where 119873 is the window size and ℎ(119909119898) are the samples ofchannel impulse response

Then the SI can be estimated as

119868119896 = (119896max minus 119896min) Δ119909 (3)

where119896max = argmax

119896+1le119898le119871minus119873

119888 (119909119896 119898Δ119909) lt 119888119905ℎ (4)

119896min = argmin1le119898le119896minus1

119888 (119909119896 119898Δ119909) lt 119888119905ℎ (5)

4 Wireless Communications and Mobile Computing

Table 3 Analysis results of the SI in different scenarios

Scenario Method Statistics SI (m)119888119905ℎ = 07 119888119905ℎ = 08 119888119905ℎ = 09

Rural LRSMean value 143 646 28460 of CCDF 822 429 16580 of CCDF 474 264 099

Station LRSMean value 727 384 18760 of CCDF 502 255 11780 of CCDF 307 125 037

Suburban LRSMean value 762 374 13760 of CCDF 543 233 08680 of CCDF 354 125 035

Viaduct [26] LRS 60 of CCDF - 18 -80 of CCDF - 081 -

Standard models [26] LRS 60 of CCDF - 34 -Station [25] RUN test 80 of CCDF 4

5 10 15 20 25 30 350SI [m]

0

01

02

03

04

05

06

07

08

09

1

CCD

F

=NB=07 (Station)

=NB=07 (Rural)

=NB=08 (Station)

=NB=08 (Rural)=NB=09 (Rural)

=NB=07 (Suburban)=NB=08 (Suburban)=NB=09 (Suburban)

=NB=09 (Station)

Figure 3 CCDFs of SI in rural station and suburban scenarios

where 119871 is the length of used data Here we consider threetypical correlation threshold values 119888119905ℎ = 07 119888119905ℎ = 08 and119888119905ℎ = 09

Figure 3 compares the derived SI results for different 119888119905ℎand scenarios in terms of complementary CDF (CCDF)With the increase of 119888119905ℎ the SI is gradually decreasing For119888119905ℎ = 09 only slight difference can be observed in differentenvironments while there is an obvious deviation for 119888119905ℎ =07 and 119888119905ℎ = 08 Focusing on the cases of 119888119905ℎ = 07 and119888119905ℎ = 08 we find that the SI values in the rural scenario arelarger than those in the station or suburban scenarios Thisis understandable from the completely different scatteringenvironment Since LoS is dominant in the rural scenario

the MPCs have smaller dynamic changes over time andthus the stationary distance is longer When it comes to thestation and suburban scenarios due to the rich reflection andscattering components from the awnings or buildings thenonstationarity is more serious and thus the SI decreasesThis nonstationarity could be originated from a special physi-cal phenomena for example ldquoappearance anddisappearancerdquoor ldquobirth and deathrdquo of MPCs [26] which will be furtherinvestigated in the following subsection

The detailed statistical SI results including mean valueand 60 and 80 of CCDF for different scenarios are listedin Table 3 For 119888119905ℎ = 08 the mean value of SI is 646 m inthe rural scenario while those are 384 m and 374 m in thestation and suburban scenarios In 60 and 80 of cases thechannel could be stationary over a distance of 233-429mand125-264 m for the measured scenarios respectively Thesevalues are higher than the results of 18 m for 60 and 081 mfor 80 reported in [25] From [25] the calculated stationaryinterval for standard channel models is equal to 34 m for60 which is shorter than the one of 429 m for the ruralscenario but is longer than the ones of 255 m and 233 m forthe station and suburban scenarios It is also observed thatthe value of SI in 80 of the cases in the measured stationscenario is smaller than that of around 4 m in the stationscenario reported in [24] This variance could be due to theuse of different calculation methods

42 Birth-Death Process The nonstationarity of the channelis basically due to the dynamic evolution of MPCs when theRx is in motion for example appearance to disappearance orB-D To describe this B-D process a four-state Markov chainmodel (MCM) is used where each state is defined as follows[30]

(i) 1198780 no ldquobirthsrdquo or ldquodeathsrdquo(ii) 1198781 ldquobirthsrdquo only(iii) 1198782 ldquodeathsrdquo only(iv) 1198783 both ldquobirthsrdquo and ldquodeathsrdquo

Wireless Communications and Mobile Computing 5

Table 4 Analysis results of the B-D process in different scenarios

Scenario State transition probability matrix Steady-state probability

Rural

[[[[[[[[

03361 04025 01203 0141100497 00315 05348 0384101658 07148 00282 0091200332 00600 02505 06563

]]]]]]]]

[01017 02533 02530 03920]

Station

[[[[[[[[

02791 02093 01628 0348800224 00096 04936 0474400354 07235 00129 0228300081 00471 00905 08544

]]]]]]]]

[00189 01374 01366 07071]

Suburban

[[[[[[[[

01870 02439 01463 0422800557 00557 02256 0663000780 04150 00613 0445700151 00464 00690 08695

]]]]]]]]

[00287 00837 00837 08039]

Viaduct [17]

[[[[[[[[

02917 05208 01667 0020800200 01000 05200 0360002577 04330 00928 0216500673 02115 02788 04423

]]]]]]]]

[01371 02800 02857 02971]

000

003

002

001

011

010

020

030

031

032

022

023

021

012

013

03330 31 32 33

Figure 4 State transition diagram of the four-state MCM

Note that the state in the MCM only considers the varia-tion of MPCs from the current moment to the next momentWith the motion of the Rx the states can be transformed toeach other Figure 4 illustrates the state transition diagramof the four-state MCM [30] The probabilistic switchingprocess between states in the MCM is controlled by the statetransition probability matrix P given by

P = 119901119894119895 =[[[[[[

11990100 11990101 11990102 1199010311990110 11990111 11990112 1199011311990120 11990121 11990122 1199012311990130 11990131 11990132 11990133

]]]]]]

(6)

where 119894 and 119895 represent the state index while 119901119894119895 is thetransition probability from state 119878119894 to state 119878119895 Note that 119901119894119895must satisfy the following requirement

0 le 119901119894119895 le 1 119894 119895 = 0 1 119873 minus 1 (7)

119873minus1sum119895=0

119901119894119895 = 1 119894 = 0 1 119873 minus 1 (8)

where119873 is the number of states that is119873 = 4 in our caseThe steady-state probability can be expressed as

P119878 = [1198751198780 1198751198781 1198751198782 1198751198783] (9)

which satisfies 1198751198780 + 1198751198781 + 1198751198782 + 1198751198783 = 1 Each element in P119878indicates the overall state occupancy probability

The obtained results of the state transition probabilitymatrix in different scenarios are listed in Table 4 Here thestate transition matrix is derived from each CIR Since thesample rate of CIR is 2000 Hz and the velocity of train is79 ms the reference value for state transition matrix is 004m It is observed that in the suburban scenario the next stateis more likely to transit into the state 1198783 no matter what thecurrent state is 1198780 1198781 or 1198782 This means some new MPCs areborn and older MPCs die most of the time because of therich reflection and scattering components in the suburbanscenario For other scenarios in the case of 1198780 the next statecould be any one of the four states in the case of 1198781 1198782 or1198783 have the maximum probability to be the next state inthe case of 1198782 the next state is more likely to transit into1198781 in the case of 1198783 the next state is still likely to be 1198783The results show the nonsymmetric transition matrix whichmeans that the transition probability from state A to state Bhas no relationship with that from state B to state A

According to the state transition probability matrix thesteady-state probability can be derived as listed in Table 4It is found that 1198783 is the most likely state in the stationand suburban scenarios For the rural scenario either 1198781 1198782

6 Wireless Communications and Mobile Computing

or 1198783 could be the steady state It is worth noting that 1198781and 1198782 have approximately similar steady-state probabilityin any scenario This confirms that the appearance anddisappearance of MPCs are equivalent The above results inthe rural scenario are similar to those in the open viaductscenario reported in [26]The obtained results can be appliedfor ONOFF tapped delay line models that use the Markovchain to model the ONOFF process of MPCs [31]

5 Conclusion

This paper analyzes the nonstationary characteristics in typi-calHSR scenarios rural station and suburban depending onthe passive LTE-based channel measurements With regardto the nonstationary characteristics it is found that the SIis longest in the rural scenario Additionally a four-stateMCM is established to characterize the B-D process ofMPCsand the corresponding state transition probability matrix andsteady-state probability are provided These results show therealistic channel characteristics in the HSR communicationnetwork which will provide helpful information for nonsta-tionary channel modeling of HSR communication systems

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the Fundamental ResearchFunds for the Central Universities (Grant 2018JBZ102)the National Natural Science Foundation of China (Grant61701017) the Beijing Municipal Natural Science Foundation(Grant 4174102) and the Open Research Fund throughthe National Mobile Communications Research LaboratorySoutheast University (Grant 2018D11)

References

[1] R He B Ai G Wang et al ldquoHigh-Speed railway communi-cations from GSM-R to LTE-Rrdquo IEEE Vehicular TechnologyMagazine vol 11 no 3 pp 49ndash58 2016

[2] B Ai X Cheng T Kurner et al ldquoChallenges toward wirelesscommunications for high-speed railwayrdquo IEEE Transactions onIntelligent Transportation Systems vol 15 no 5 pp 2143ndash21582014

[3] White Paper for 5G High Mobility [Online] Available httpwwwfuture-forumorg

[4] H Wei Z Zhong K Guan and B Ai ldquoPath loss modelsin viaduct and plain scenarios of the High-speed Railwayrdquoin Proceedings of the 5th International ICST Conference onCommunications and Networking in China Beijing ChinaAugust 2010

[5] R He Z Zhong B Ai and J Ding ldquoAn empirical pathloss model and fading analysis for high-speed railway viaductscenariosrdquo IEEE Antennas andWireless Propagation Letters vol10 pp 808ndash812 2011

[6] K Guan Z Zhong B Ai and T Kurner ldquoEmpirical models forextra propagation loss of train stations on high-speed railwayrdquoIEEE Transactions on Antennas and Propagation vol 62 no 3pp 1395ndash1408 2014

[7] R He Z Zhong B Ai and C Oestges ldquoShadow fading corre-lation in high-speed railway environmentsrdquo IEEE Transactionson Vehicular Technology vol 64 no 7 pp 2762ndash2772 2015

[8] L Liu C Tao D W Matolak T Zhou and H Chen ldquoInvesti-gation of Shadowing Effects in Typical Propagation Scenariosfor High-Speed Railway at 2350MHzrdquo International Journal ofAntennas and Propagation vol 2016 pp 1ndash8 2016

[9] O Renaudin V-M Kolmonen P Vainikainen and C Oest-ges ldquoNon-stationary narrowband MIMO inter-vehicle channelcharacterization in the 5-GHz bandrdquo IEEE Transactions onVehicular Technology vol 59 no 4 pp 2007ndash2015 2010

[10] D W Matolak ldquoModeling the vehicle-to-vehicle propagationchannel A reviewrdquo Radio Science vol 49 no 9 pp 721ndash7362014

[11] L Liu C Tao J Qiu et al ldquoPosition-basedmodeling forwirelesschannel on high-speed railway under a viaduct at 235GHzrdquoIEEE Journal on Selected Areas in Communications vol 30 no4 pp 834ndash845 2012

[12] T Zhou C Tao L Liu and Z Tan ldquoA semiempirical MIMOchannel model in obstructed viaduct scenarios on high-speedrailwayrdquo International Journal of Antennas and Propagation vol2014 Article ID 287159 10 pages 2014

[13] T Zhou C Tao S Salous Z Tan L Liu and L TianldquoGraph-based stochastic model for high-speed railway cuttingscenariosrdquo IETMicrowaves Antennas amp Propagation vol 9 no15 pp 1691ndash1697 2015

[14] R Sun C Tao L Liu and Z Tan ldquoChannel Measurement andCharacterization for HSR U-Shape Groove Scenarios at 235GHzrdquo in Proceedings of the 2013 IEEE 78th Vehicular TechnologyConference (VTC Fall) pp 1ndash5 Las Vegas NV USA September2013

[15] T Zhou C Tao S Salous and L Liu ldquoSpatial Characterizationfor High-Speed Railway Channels Based on Moving VirtualArray Measurement Schemerdquo IEEE Antennas and WirelessPropagation Letters vol 16 pp 1423ndash1426 2017

[16] R He Z Zhong B Ai G Wang J Ding and A F MolischldquoMeasurements and analysis of propagation channels in high-speed railway viaductsrdquo IEEE Transactions onWireless Commu-nications vol 12 no 2 pp 794ndash805 2013

[17] R He Z Zhong B Ai J Ding Y Yang and A F MolischldquoShort-term fading behavior in high-speed railway cuttingscenario Measurements analysis and statistical modelsrdquo IEEETransactions on Antennas and Propagation vol 61 no 4 pp2209ndash2222 2013

[18] P Kyosti et al WINNER II channel models part II radiochannel measurement and analysis results vol ST-4-0277562007 WINNER II D112 v10

[19] R Parviainen P Ky Y Hsieh P Ting and J Chiou ldquoResultsof high speed train channel measurements inrdquo in Proc COST2100 TD France Lille 2008

[20] F Luan Y Zhang L Xiao C Zhou and S Zhou ldquoFadingcharacteristics of wireless channel on high-speed railway inhilly terrain scenariordquo International Journal of Antennas andPropagation vol 2013 Article ID 378407 9 pages 2013

Wireless Communications and Mobile Computing 7

[21] Y Zhang ZHeWZhang L Xiao and S Zhou ldquoMeasurement-based delay and doppler characterizations for high-speedrailway hilly scenariordquo International Journal of Antennas andPropagation vol 2014 Article ID 875345 8 pages 2014

[22] P Aikio R Gruber and P Vainikainen ldquoWideband radio chan-nel measurements for train tunnelsrdquo in Proceedings of the VTCrsquo98 48th IEEE Vehicular Technology Conference Pathway to aGlobalWireless Revolution pp 460ndash464 Ottawa Ont Canada

[23] KGuan Z Zhong B Ai andT Kurner ldquoPropagationmeasure-ments and modeling of crossing bridges on high-speed railwayat 930 MHzrdquo IEEE Transactions on Vehicular Technology vol63 no 8 pp 3349ndash3516 2014

[24] T Zhou C Tao S Salous L Liu and Z Tan ldquoChannelcharacterization in high-speed railway station environments at189 GHzrdquo Radio Science vol 50 no 11 pp 1176ndash1186 2015

[25] B Chen Z Zhong and B Ai ldquoStationarity intervals of time-variant channel in high speed railway scenariordquo China Commu-nications vol 9 no 8 pp 64ndash70 2012

[26] L Liu C Tao J Qiu T Zhou R Sun and H Chen ldquoThedynamic evolution of multipath components in High-SpeedRailway in viaduct scenarios From the birth-death processpoint of viewrdquo in Proceedings of the 2012 IEEE 23rd InternationalSymposium on Personal Indoor and Mobile Radio Commu-nications - (PIMRC 2012) pp 1774ndash1778 Sydney AustraliaSeptember 2012

[27] T Zhou C Tao S Salous L Liu andZ Tan ldquoChannel soundingfor high-speed railway communication systemsrdquo IEEE Commu-nications Magazine vol 53 no 10 pp 70ndash77 2015

[28] T Zhou C Tao S Salous L Liu and Z Tan ldquoImplementationof an LTE-based channel measurement method for high-speedrailway scenariosrdquo IEEE Transactions on Instrumentation andMeasurement vol 65 no 1 pp 25ndash36 2016

[29] R He O Renaudin V-M Kolmonen et al ldquoCharacterizationof quasi-stationarity regions for vehicle-to-vehicle radio chan-nelsrdquo IEEE Transactions on Antennas and Propagation vol 63no 5 pp 2237ndash2251 2015

[30] C-C Chong C-M Tan D I Laurenson S McLaughlin MA Beach andA RNix ldquoA novel wideband dynamic directionalindoor channel model based on a Markov processrdquo IEEETransactions on Wireless Communications vol 4 no 4 pp1539ndash1552 2005

[31] I Sen and D Matolak ldquoVehicle channel models for the 5-ghzbandrdquo IEEET Intell Transp vol 9 no 2 pp 235ndash245 June 2008

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Hindawiwwwhindawicom Volume 2018

Shock and Vibration

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Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

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Journal ofEngineeringVolume 2018

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Hindawiwwwhindawicom Volume 2018

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Hindawiwwwhindawicom Volume 2018

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wwwhindawicom Volume 2018

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Submit your manuscripts atwwwhindawicom

Page 2: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

2 Wireless Communications and Mobile Computing

Table 1 Summary of investigation on short-term fading and nonstationary behaviors in HSR environments

Scenario Short-term fading behavior Nonstationary behaviorFading severity Time dispersion Frequency dispersion Space dispersion Stationarity interval Birth-death process

Viaduct [11 12 16] [11] [11 12] [15] [25] [26]Cutting [13 14 17] [13 14] [13 14] [15] - -Rural [18] [18 19] [19] [18 19] - -Hilly terrain [20] [21] [21] - - -Tunnel [22] [22] [22] - - -Station [23 24] [24] - [24] -Suburbanurban - - - - - -

2 Related Work

Unique HSR scenarios such as viaduct cutting tunnelstation hilly terrain rural and suburban have a significantimpact on propagation characteristics In our previous workfading severity and time-frequency-space dispersion of HSRchannels in the viaduct and cutting scenarios were deeplycharacterized based on measurements using Propsound [11ndash15] In [15] spatial characteristics involving angle of arrival(AOA) root-mean-square (RMS) angle spread (AS) andspatial correlation (SC) were analyzed according to a so-called moving virtual antenna array (VAA) scheme Authorsin [16 17] proposed a statistic model for Ricean K-factorwhich investigated the impact of the viaduct height and thecutting width The WINNER II model [18] and COST 2100TD [19] provided some measurement results of short-termfading behavior for rural scenarios There were also a fewresults of K-factor RMS delay spread (DS) and Dopplerpower spectral density (DPSD) measured in the hilly terrainand tunnel scenarios [20ndash22] Authors in [23 24] presenteddetailed analysis of fading severity and time dispersion inopen-type and semiclosed station scenarios For the non-stationary behavior stationarity interval (SI) in the viaductscenario was investigated based on GSM-R measurementswhich showed that conventional channel models offered SImuch larger than the actualmeasured ones [25]We also triedto adopt a RUN testmethod to obtain the SI for the open-typestation scenario in [24] Additionally a four-state Markovchain was used to model the birth-death (B-D) process ofMPCs in the viaduct scenario [26]

Table 1 summarizes the existing measurement campaignsabout short-term fading and nonstationary behaviors indifferent HSR environments It can be found that thereare a few results of time-frequency-space characteristics insome HSR scenarios However the characterization of thenonstationary behavior is largely neglected inmost scenariosTherefore this paper aims to investigate the nonstationarybehavior in the rural station and suburban scenarios

3 Channel Measurements

31 Measurement Scenarios An LTE network deployed onBeijing to Tianjin (BT) HSR in China was chosen in ourmeasurements [27] It is a hybrid network composed of adedicated network and a common network The architectureof the dedicated network is completely different from the

Table 2 Measurement parameters in the BT HSR LTE network

Scenario Rural Station SuburbaneNB side

Frequency 189 GHz 2605 GHzBandwidth 18 MHz 18 MHzCRS power 122 dBm 122 dBmAntenna type Directional DirectionalAntenna gain 174 dBi 186 dBiHorizontal beamwidth 67 deg 60 degVertical beamwidth 66 deg 49 degElectric tilted angle 3 deg 3 deg

Sounder sideAntenna type Omnidirectional OmnidirectionalAntenna gain 85 dBi 85 dBiSpeed of HST 285 kmh 185 kmh

Network sideDistance between PSs 12 kmHeight of PS 30 mHeight of rail track 10 mDistance between PSand rail track 30 m

common network which adopts building baseband unit(BBU) plus remote radio unit (RRU) to achieve the specialnarrow-strip-shaped coverage instead of the cellular cover-age In this architecture one physical site (PS) deploys twoRRUs which transmit signals by directional antennas inopposite directions along the railway track The RRUs areconnected together via optical fiber and then to a BBU thatis in charge of radio frequency (RF) signal processing

The LTE sounder is placed on a high-speed train (HST)to collect the channel data which experiences multiplescenarios along the BT railway line such as rural stationand suburban In our measurement the rural and stationscenarios are within the coverage of dedicated network whilethe suburban scenario is covered by the common networkThe detailed measurement parameters for different scenariosin the network are listed in Table 2 The carrier frequencyis 189 GHz for the rural and station scenarios and 2605GHz for the suburban scenario When the HST moves into

Wireless Communications and Mobile Computing 3

(a) (b) (c)

Figure 1 Measurement scenarios (a) Rural (b) Station (c) Suburban

Local CRSGeneration

IFFT

Received CRSExtraction

CIRCellSearchBB

Figure 2 The procedure of data processing

the suburban area the speed is decreased from 285 kmh to185 kmh The specification of the directional eNB antennasuch as gain and beamwidth has slight difference in differentfrequencies At Rx side the LTE sounder is connected to atrain-mounted omnidirectional antennaThe average spacingbetween neighboring PSs is around 12 km The height anddistance difference between the PS and the rail track are about20 m and 30 m respectively

The measured scenarios are shown in Figure 1 As forthe rural scenario the transmit antenna is much higher thanthe surroundings which are light forests and a few buildingswith an average height of less than 10 m The link betweenthe transmitter (Tx) and Rx generally has a strong line-of-sight (LoS) component However after a certain distancethe impact of the sparse scatterers will be noticed at the Rxrepresented by non-LoS (NLoS) components With regardto the station scenario in the measurement the HST runsthrough the station without stopping The measured stationcan be regarded as an open-type station with two awningsthat only cover the platform supporting a clear free space overthe rail However the awnings can still produce lots of NLoScomponents to complicate the fading behavior The lengthof the station is 440 m the width of the awning is 14 mand the width of the gap between the two awnings is 9 mSuburban is a transition zone between the rural and urbanThe NLoS components in the suburban environment will bemuch richer than those in the rural environmentThe densityof the buildings in the suburban scenario is similar to that inthe urban scenario but the height of the buildings is lowerSince the measured suburban is close to the urban area someremote high buildings could affect the results

32 Data Processing Baseband data (BB) collected by theLTE sounder in the multilink regions are used for offlineprocessing The procedure of data processing is shown inFigure 2 Firstly cell search is implemented to determine the

cell identity and obtain synchronized frames for extractingreceived CRSs and generating local CRSs Then frequency-domain correlation is used to estimate channel frequencyresponses which can be subsequently transformed to theraw CIRs by inverse fast Fourier transform (IFFT) operation[28]

4 Results and Analysis

41 Stationarity Interval High mobility leads to the viola-tion of wide sense stationary (WSS) condition for wirelesschannels under HSR scenarios The stationarity interval (SI)is defined as the maximum time or distance duration overwhich the channel satisfies the WSS condition It is summa-rized by [29] that there are severalmetrics that can be used formeasuring the SI involving local region of stationarity (LRS)correlation matrix distance (CMD) and spectral divergence(SD) Besides some statistical tests for the WSS of a randomprocess can also be applied to the determination of SI suchas RUN test and reverse arrangement test The LRS methodhas been used to estimate the time interval ofHSR channels in[25]TheRUN test was applied to the RMSDS data to identifythe stationary distance in the station scenario [24] In thispaper we choose the classical LRS approach to characterizethe SI in the measured scenarios

The aim of the LRS method is to find the maximuminterval within which the correlation coefficient between twoconsecutive PDPs exceeds a predefined threshold 119888119905ℎ Thecorrelation coefficient between PDPs is defined as

119888 (119909119896 Δ119909) = 119875 (119909119896) 119875 (119909119896 + Δ119909)max 119875 (119909119896)2 119875 (119909119896 + Δ119909)2

(1)

where

119875 (119909119896) = 1119873119896+119873minus1sum119898=119896

1003816100381610038161003816ℎ (119909119898)10038161003816100381610038162 (2)

where 119873 is the window size and ℎ(119909119898) are the samples ofchannel impulse response

Then the SI can be estimated as

119868119896 = (119896max minus 119896min) Δ119909 (3)

where119896max = argmax

119896+1le119898le119871minus119873

119888 (119909119896 119898Δ119909) lt 119888119905ℎ (4)

119896min = argmin1le119898le119896minus1

119888 (119909119896 119898Δ119909) lt 119888119905ℎ (5)

4 Wireless Communications and Mobile Computing

Table 3 Analysis results of the SI in different scenarios

Scenario Method Statistics SI (m)119888119905ℎ = 07 119888119905ℎ = 08 119888119905ℎ = 09

Rural LRSMean value 143 646 28460 of CCDF 822 429 16580 of CCDF 474 264 099

Station LRSMean value 727 384 18760 of CCDF 502 255 11780 of CCDF 307 125 037

Suburban LRSMean value 762 374 13760 of CCDF 543 233 08680 of CCDF 354 125 035

Viaduct [26] LRS 60 of CCDF - 18 -80 of CCDF - 081 -

Standard models [26] LRS 60 of CCDF - 34 -Station [25] RUN test 80 of CCDF 4

5 10 15 20 25 30 350SI [m]

0

01

02

03

04

05

06

07

08

09

1

CCD

F

=NB=07 (Station)

=NB=07 (Rural)

=NB=08 (Station)

=NB=08 (Rural)=NB=09 (Rural)

=NB=07 (Suburban)=NB=08 (Suburban)=NB=09 (Suburban)

=NB=09 (Station)

Figure 3 CCDFs of SI in rural station and suburban scenarios

where 119871 is the length of used data Here we consider threetypical correlation threshold values 119888119905ℎ = 07 119888119905ℎ = 08 and119888119905ℎ = 09

Figure 3 compares the derived SI results for different 119888119905ℎand scenarios in terms of complementary CDF (CCDF)With the increase of 119888119905ℎ the SI is gradually decreasing For119888119905ℎ = 09 only slight difference can be observed in differentenvironments while there is an obvious deviation for 119888119905ℎ =07 and 119888119905ℎ = 08 Focusing on the cases of 119888119905ℎ = 07 and119888119905ℎ = 08 we find that the SI values in the rural scenario arelarger than those in the station or suburban scenarios Thisis understandable from the completely different scatteringenvironment Since LoS is dominant in the rural scenario

the MPCs have smaller dynamic changes over time andthus the stationary distance is longer When it comes to thestation and suburban scenarios due to the rich reflection andscattering components from the awnings or buildings thenonstationarity is more serious and thus the SI decreasesThis nonstationarity could be originated from a special physi-cal phenomena for example ldquoappearance anddisappearancerdquoor ldquobirth and deathrdquo of MPCs [26] which will be furtherinvestigated in the following subsection

The detailed statistical SI results including mean valueand 60 and 80 of CCDF for different scenarios are listedin Table 3 For 119888119905ℎ = 08 the mean value of SI is 646 m inthe rural scenario while those are 384 m and 374 m in thestation and suburban scenarios In 60 and 80 of cases thechannel could be stationary over a distance of 233-429mand125-264 m for the measured scenarios respectively Thesevalues are higher than the results of 18 m for 60 and 081 mfor 80 reported in [25] From [25] the calculated stationaryinterval for standard channel models is equal to 34 m for60 which is shorter than the one of 429 m for the ruralscenario but is longer than the ones of 255 m and 233 m forthe station and suburban scenarios It is also observed thatthe value of SI in 80 of the cases in the measured stationscenario is smaller than that of around 4 m in the stationscenario reported in [24] This variance could be due to theuse of different calculation methods

42 Birth-Death Process The nonstationarity of the channelis basically due to the dynamic evolution of MPCs when theRx is in motion for example appearance to disappearance orB-D To describe this B-D process a four-state Markov chainmodel (MCM) is used where each state is defined as follows[30]

(i) 1198780 no ldquobirthsrdquo or ldquodeathsrdquo(ii) 1198781 ldquobirthsrdquo only(iii) 1198782 ldquodeathsrdquo only(iv) 1198783 both ldquobirthsrdquo and ldquodeathsrdquo

Wireless Communications and Mobile Computing 5

Table 4 Analysis results of the B-D process in different scenarios

Scenario State transition probability matrix Steady-state probability

Rural

[[[[[[[[

03361 04025 01203 0141100497 00315 05348 0384101658 07148 00282 0091200332 00600 02505 06563

]]]]]]]]

[01017 02533 02530 03920]

Station

[[[[[[[[

02791 02093 01628 0348800224 00096 04936 0474400354 07235 00129 0228300081 00471 00905 08544

]]]]]]]]

[00189 01374 01366 07071]

Suburban

[[[[[[[[

01870 02439 01463 0422800557 00557 02256 0663000780 04150 00613 0445700151 00464 00690 08695

]]]]]]]]

[00287 00837 00837 08039]

Viaduct [17]

[[[[[[[[

02917 05208 01667 0020800200 01000 05200 0360002577 04330 00928 0216500673 02115 02788 04423

]]]]]]]]

[01371 02800 02857 02971]

000

003

002

001

011

010

020

030

031

032

022

023

021

012

013

03330 31 32 33

Figure 4 State transition diagram of the four-state MCM

Note that the state in the MCM only considers the varia-tion of MPCs from the current moment to the next momentWith the motion of the Rx the states can be transformed toeach other Figure 4 illustrates the state transition diagramof the four-state MCM [30] The probabilistic switchingprocess between states in the MCM is controlled by the statetransition probability matrix P given by

P = 119901119894119895 =[[[[[[

11990100 11990101 11990102 1199010311990110 11990111 11990112 1199011311990120 11990121 11990122 1199012311990130 11990131 11990132 11990133

]]]]]]

(6)

where 119894 and 119895 represent the state index while 119901119894119895 is thetransition probability from state 119878119894 to state 119878119895 Note that 119901119894119895must satisfy the following requirement

0 le 119901119894119895 le 1 119894 119895 = 0 1 119873 minus 1 (7)

119873minus1sum119895=0

119901119894119895 = 1 119894 = 0 1 119873 minus 1 (8)

where119873 is the number of states that is119873 = 4 in our caseThe steady-state probability can be expressed as

P119878 = [1198751198780 1198751198781 1198751198782 1198751198783] (9)

which satisfies 1198751198780 + 1198751198781 + 1198751198782 + 1198751198783 = 1 Each element in P119878indicates the overall state occupancy probability

The obtained results of the state transition probabilitymatrix in different scenarios are listed in Table 4 Here thestate transition matrix is derived from each CIR Since thesample rate of CIR is 2000 Hz and the velocity of train is79 ms the reference value for state transition matrix is 004m It is observed that in the suburban scenario the next stateis more likely to transit into the state 1198783 no matter what thecurrent state is 1198780 1198781 or 1198782 This means some new MPCs areborn and older MPCs die most of the time because of therich reflection and scattering components in the suburbanscenario For other scenarios in the case of 1198780 the next statecould be any one of the four states in the case of 1198781 1198782 or1198783 have the maximum probability to be the next state inthe case of 1198782 the next state is more likely to transit into1198781 in the case of 1198783 the next state is still likely to be 1198783The results show the nonsymmetric transition matrix whichmeans that the transition probability from state A to state Bhas no relationship with that from state B to state A

According to the state transition probability matrix thesteady-state probability can be derived as listed in Table 4It is found that 1198783 is the most likely state in the stationand suburban scenarios For the rural scenario either 1198781 1198782

6 Wireless Communications and Mobile Computing

or 1198783 could be the steady state It is worth noting that 1198781and 1198782 have approximately similar steady-state probabilityin any scenario This confirms that the appearance anddisappearance of MPCs are equivalent The above results inthe rural scenario are similar to those in the open viaductscenario reported in [26]The obtained results can be appliedfor ONOFF tapped delay line models that use the Markovchain to model the ONOFF process of MPCs [31]

5 Conclusion

This paper analyzes the nonstationary characteristics in typi-calHSR scenarios rural station and suburban depending onthe passive LTE-based channel measurements With regardto the nonstationary characteristics it is found that the SIis longest in the rural scenario Additionally a four-stateMCM is established to characterize the B-D process ofMPCsand the corresponding state transition probability matrix andsteady-state probability are provided These results show therealistic channel characteristics in the HSR communicationnetwork which will provide helpful information for nonsta-tionary channel modeling of HSR communication systems

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the Fundamental ResearchFunds for the Central Universities (Grant 2018JBZ102)the National Natural Science Foundation of China (Grant61701017) the Beijing Municipal Natural Science Foundation(Grant 4174102) and the Open Research Fund throughthe National Mobile Communications Research LaboratorySoutheast University (Grant 2018D11)

References

[1] R He B Ai G Wang et al ldquoHigh-Speed railway communi-cations from GSM-R to LTE-Rrdquo IEEE Vehicular TechnologyMagazine vol 11 no 3 pp 49ndash58 2016

[2] B Ai X Cheng T Kurner et al ldquoChallenges toward wirelesscommunications for high-speed railwayrdquo IEEE Transactions onIntelligent Transportation Systems vol 15 no 5 pp 2143ndash21582014

[3] White Paper for 5G High Mobility [Online] Available httpwwwfuture-forumorg

[4] H Wei Z Zhong K Guan and B Ai ldquoPath loss modelsin viaduct and plain scenarios of the High-speed Railwayrdquoin Proceedings of the 5th International ICST Conference onCommunications and Networking in China Beijing ChinaAugust 2010

[5] R He Z Zhong B Ai and J Ding ldquoAn empirical pathloss model and fading analysis for high-speed railway viaductscenariosrdquo IEEE Antennas andWireless Propagation Letters vol10 pp 808ndash812 2011

[6] K Guan Z Zhong B Ai and T Kurner ldquoEmpirical models forextra propagation loss of train stations on high-speed railwayrdquoIEEE Transactions on Antennas and Propagation vol 62 no 3pp 1395ndash1408 2014

[7] R He Z Zhong B Ai and C Oestges ldquoShadow fading corre-lation in high-speed railway environmentsrdquo IEEE Transactionson Vehicular Technology vol 64 no 7 pp 2762ndash2772 2015

[8] L Liu C Tao D W Matolak T Zhou and H Chen ldquoInvesti-gation of Shadowing Effects in Typical Propagation Scenariosfor High-Speed Railway at 2350MHzrdquo International Journal ofAntennas and Propagation vol 2016 pp 1ndash8 2016

[9] O Renaudin V-M Kolmonen P Vainikainen and C Oest-ges ldquoNon-stationary narrowband MIMO inter-vehicle channelcharacterization in the 5-GHz bandrdquo IEEE Transactions onVehicular Technology vol 59 no 4 pp 2007ndash2015 2010

[10] D W Matolak ldquoModeling the vehicle-to-vehicle propagationchannel A reviewrdquo Radio Science vol 49 no 9 pp 721ndash7362014

[11] L Liu C Tao J Qiu et al ldquoPosition-basedmodeling forwirelesschannel on high-speed railway under a viaduct at 235GHzrdquoIEEE Journal on Selected Areas in Communications vol 30 no4 pp 834ndash845 2012

[12] T Zhou C Tao L Liu and Z Tan ldquoA semiempirical MIMOchannel model in obstructed viaduct scenarios on high-speedrailwayrdquo International Journal of Antennas and Propagation vol2014 Article ID 287159 10 pages 2014

[13] T Zhou C Tao S Salous Z Tan L Liu and L TianldquoGraph-based stochastic model for high-speed railway cuttingscenariosrdquo IETMicrowaves Antennas amp Propagation vol 9 no15 pp 1691ndash1697 2015

[14] R Sun C Tao L Liu and Z Tan ldquoChannel Measurement andCharacterization for HSR U-Shape Groove Scenarios at 235GHzrdquo in Proceedings of the 2013 IEEE 78th Vehicular TechnologyConference (VTC Fall) pp 1ndash5 Las Vegas NV USA September2013

[15] T Zhou C Tao S Salous and L Liu ldquoSpatial Characterizationfor High-Speed Railway Channels Based on Moving VirtualArray Measurement Schemerdquo IEEE Antennas and WirelessPropagation Letters vol 16 pp 1423ndash1426 2017

[16] R He Z Zhong B Ai G Wang J Ding and A F MolischldquoMeasurements and analysis of propagation channels in high-speed railway viaductsrdquo IEEE Transactions onWireless Commu-nications vol 12 no 2 pp 794ndash805 2013

[17] R He Z Zhong B Ai J Ding Y Yang and A F MolischldquoShort-term fading behavior in high-speed railway cuttingscenario Measurements analysis and statistical modelsrdquo IEEETransactions on Antennas and Propagation vol 61 no 4 pp2209ndash2222 2013

[18] P Kyosti et al WINNER II channel models part II radiochannel measurement and analysis results vol ST-4-0277562007 WINNER II D112 v10

[19] R Parviainen P Ky Y Hsieh P Ting and J Chiou ldquoResultsof high speed train channel measurements inrdquo in Proc COST2100 TD France Lille 2008

[20] F Luan Y Zhang L Xiao C Zhou and S Zhou ldquoFadingcharacteristics of wireless channel on high-speed railway inhilly terrain scenariordquo International Journal of Antennas andPropagation vol 2013 Article ID 378407 9 pages 2013

Wireless Communications and Mobile Computing 7

[21] Y Zhang ZHeWZhang L Xiao and S Zhou ldquoMeasurement-based delay and doppler characterizations for high-speedrailway hilly scenariordquo International Journal of Antennas andPropagation vol 2014 Article ID 875345 8 pages 2014

[22] P Aikio R Gruber and P Vainikainen ldquoWideband radio chan-nel measurements for train tunnelsrdquo in Proceedings of the VTCrsquo98 48th IEEE Vehicular Technology Conference Pathway to aGlobalWireless Revolution pp 460ndash464 Ottawa Ont Canada

[23] KGuan Z Zhong B Ai andT Kurner ldquoPropagationmeasure-ments and modeling of crossing bridges on high-speed railwayat 930 MHzrdquo IEEE Transactions on Vehicular Technology vol63 no 8 pp 3349ndash3516 2014

[24] T Zhou C Tao S Salous L Liu and Z Tan ldquoChannelcharacterization in high-speed railway station environments at189 GHzrdquo Radio Science vol 50 no 11 pp 1176ndash1186 2015

[25] B Chen Z Zhong and B Ai ldquoStationarity intervals of time-variant channel in high speed railway scenariordquo China Commu-nications vol 9 no 8 pp 64ndash70 2012

[26] L Liu C Tao J Qiu T Zhou R Sun and H Chen ldquoThedynamic evolution of multipath components in High-SpeedRailway in viaduct scenarios From the birth-death processpoint of viewrdquo in Proceedings of the 2012 IEEE 23rd InternationalSymposium on Personal Indoor and Mobile Radio Commu-nications - (PIMRC 2012) pp 1774ndash1778 Sydney AustraliaSeptember 2012

[27] T Zhou C Tao S Salous L Liu andZ Tan ldquoChannel soundingfor high-speed railway communication systemsrdquo IEEE Commu-nications Magazine vol 53 no 10 pp 70ndash77 2015

[28] T Zhou C Tao S Salous L Liu and Z Tan ldquoImplementationof an LTE-based channel measurement method for high-speedrailway scenariosrdquo IEEE Transactions on Instrumentation andMeasurement vol 65 no 1 pp 25ndash36 2016

[29] R He O Renaudin V-M Kolmonen et al ldquoCharacterizationof quasi-stationarity regions for vehicle-to-vehicle radio chan-nelsrdquo IEEE Transactions on Antennas and Propagation vol 63no 5 pp 2237ndash2251 2015

[30] C-C Chong C-M Tan D I Laurenson S McLaughlin MA Beach andA RNix ldquoA novel wideband dynamic directionalindoor channel model based on a Markov processrdquo IEEETransactions on Wireless Communications vol 4 no 4 pp1539ndash1552 2005

[31] I Sen and D Matolak ldquoVehicle channel models for the 5-ghzbandrdquo IEEET Intell Transp vol 9 no 2 pp 235ndash245 June 2008

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

Page 3: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

Wireless Communications and Mobile Computing 3

(a) (b) (c)

Figure 1 Measurement scenarios (a) Rural (b) Station (c) Suburban

Local CRSGeneration

IFFT

Received CRSExtraction

CIRCellSearchBB

Figure 2 The procedure of data processing

the suburban area the speed is decreased from 285 kmh to185 kmh The specification of the directional eNB antennasuch as gain and beamwidth has slight difference in differentfrequencies At Rx side the LTE sounder is connected to atrain-mounted omnidirectional antennaThe average spacingbetween neighboring PSs is around 12 km The height anddistance difference between the PS and the rail track are about20 m and 30 m respectively

The measured scenarios are shown in Figure 1 As forthe rural scenario the transmit antenna is much higher thanthe surroundings which are light forests and a few buildingswith an average height of less than 10 m The link betweenthe transmitter (Tx) and Rx generally has a strong line-of-sight (LoS) component However after a certain distancethe impact of the sparse scatterers will be noticed at the Rxrepresented by non-LoS (NLoS) components With regardto the station scenario in the measurement the HST runsthrough the station without stopping The measured stationcan be regarded as an open-type station with two awningsthat only cover the platform supporting a clear free space overthe rail However the awnings can still produce lots of NLoScomponents to complicate the fading behavior The lengthof the station is 440 m the width of the awning is 14 mand the width of the gap between the two awnings is 9 mSuburban is a transition zone between the rural and urbanThe NLoS components in the suburban environment will bemuch richer than those in the rural environmentThe densityof the buildings in the suburban scenario is similar to that inthe urban scenario but the height of the buildings is lowerSince the measured suburban is close to the urban area someremote high buildings could affect the results

32 Data Processing Baseband data (BB) collected by theLTE sounder in the multilink regions are used for offlineprocessing The procedure of data processing is shown inFigure 2 Firstly cell search is implemented to determine the

cell identity and obtain synchronized frames for extractingreceived CRSs and generating local CRSs Then frequency-domain correlation is used to estimate channel frequencyresponses which can be subsequently transformed to theraw CIRs by inverse fast Fourier transform (IFFT) operation[28]

4 Results and Analysis

41 Stationarity Interval High mobility leads to the viola-tion of wide sense stationary (WSS) condition for wirelesschannels under HSR scenarios The stationarity interval (SI)is defined as the maximum time or distance duration overwhich the channel satisfies the WSS condition It is summa-rized by [29] that there are severalmetrics that can be used formeasuring the SI involving local region of stationarity (LRS)correlation matrix distance (CMD) and spectral divergence(SD) Besides some statistical tests for the WSS of a randomprocess can also be applied to the determination of SI suchas RUN test and reverse arrangement test The LRS methodhas been used to estimate the time interval ofHSR channels in[25]TheRUN test was applied to the RMSDS data to identifythe stationary distance in the station scenario [24] In thispaper we choose the classical LRS approach to characterizethe SI in the measured scenarios

The aim of the LRS method is to find the maximuminterval within which the correlation coefficient between twoconsecutive PDPs exceeds a predefined threshold 119888119905ℎ Thecorrelation coefficient between PDPs is defined as

119888 (119909119896 Δ119909) = 119875 (119909119896) 119875 (119909119896 + Δ119909)max 119875 (119909119896)2 119875 (119909119896 + Δ119909)2

(1)

where

119875 (119909119896) = 1119873119896+119873minus1sum119898=119896

1003816100381610038161003816ℎ (119909119898)10038161003816100381610038162 (2)

where 119873 is the window size and ℎ(119909119898) are the samples ofchannel impulse response

Then the SI can be estimated as

119868119896 = (119896max minus 119896min) Δ119909 (3)

where119896max = argmax

119896+1le119898le119871minus119873

119888 (119909119896 119898Δ119909) lt 119888119905ℎ (4)

119896min = argmin1le119898le119896minus1

119888 (119909119896 119898Δ119909) lt 119888119905ℎ (5)

4 Wireless Communications and Mobile Computing

Table 3 Analysis results of the SI in different scenarios

Scenario Method Statistics SI (m)119888119905ℎ = 07 119888119905ℎ = 08 119888119905ℎ = 09

Rural LRSMean value 143 646 28460 of CCDF 822 429 16580 of CCDF 474 264 099

Station LRSMean value 727 384 18760 of CCDF 502 255 11780 of CCDF 307 125 037

Suburban LRSMean value 762 374 13760 of CCDF 543 233 08680 of CCDF 354 125 035

Viaduct [26] LRS 60 of CCDF - 18 -80 of CCDF - 081 -

Standard models [26] LRS 60 of CCDF - 34 -Station [25] RUN test 80 of CCDF 4

5 10 15 20 25 30 350SI [m]

0

01

02

03

04

05

06

07

08

09

1

CCD

F

=NB=07 (Station)

=NB=07 (Rural)

=NB=08 (Station)

=NB=08 (Rural)=NB=09 (Rural)

=NB=07 (Suburban)=NB=08 (Suburban)=NB=09 (Suburban)

=NB=09 (Station)

Figure 3 CCDFs of SI in rural station and suburban scenarios

where 119871 is the length of used data Here we consider threetypical correlation threshold values 119888119905ℎ = 07 119888119905ℎ = 08 and119888119905ℎ = 09

Figure 3 compares the derived SI results for different 119888119905ℎand scenarios in terms of complementary CDF (CCDF)With the increase of 119888119905ℎ the SI is gradually decreasing For119888119905ℎ = 09 only slight difference can be observed in differentenvironments while there is an obvious deviation for 119888119905ℎ =07 and 119888119905ℎ = 08 Focusing on the cases of 119888119905ℎ = 07 and119888119905ℎ = 08 we find that the SI values in the rural scenario arelarger than those in the station or suburban scenarios Thisis understandable from the completely different scatteringenvironment Since LoS is dominant in the rural scenario

the MPCs have smaller dynamic changes over time andthus the stationary distance is longer When it comes to thestation and suburban scenarios due to the rich reflection andscattering components from the awnings or buildings thenonstationarity is more serious and thus the SI decreasesThis nonstationarity could be originated from a special physi-cal phenomena for example ldquoappearance anddisappearancerdquoor ldquobirth and deathrdquo of MPCs [26] which will be furtherinvestigated in the following subsection

The detailed statistical SI results including mean valueand 60 and 80 of CCDF for different scenarios are listedin Table 3 For 119888119905ℎ = 08 the mean value of SI is 646 m inthe rural scenario while those are 384 m and 374 m in thestation and suburban scenarios In 60 and 80 of cases thechannel could be stationary over a distance of 233-429mand125-264 m for the measured scenarios respectively Thesevalues are higher than the results of 18 m for 60 and 081 mfor 80 reported in [25] From [25] the calculated stationaryinterval for standard channel models is equal to 34 m for60 which is shorter than the one of 429 m for the ruralscenario but is longer than the ones of 255 m and 233 m forthe station and suburban scenarios It is also observed thatthe value of SI in 80 of the cases in the measured stationscenario is smaller than that of around 4 m in the stationscenario reported in [24] This variance could be due to theuse of different calculation methods

42 Birth-Death Process The nonstationarity of the channelis basically due to the dynamic evolution of MPCs when theRx is in motion for example appearance to disappearance orB-D To describe this B-D process a four-state Markov chainmodel (MCM) is used where each state is defined as follows[30]

(i) 1198780 no ldquobirthsrdquo or ldquodeathsrdquo(ii) 1198781 ldquobirthsrdquo only(iii) 1198782 ldquodeathsrdquo only(iv) 1198783 both ldquobirthsrdquo and ldquodeathsrdquo

Wireless Communications and Mobile Computing 5

Table 4 Analysis results of the B-D process in different scenarios

Scenario State transition probability matrix Steady-state probability

Rural

[[[[[[[[

03361 04025 01203 0141100497 00315 05348 0384101658 07148 00282 0091200332 00600 02505 06563

]]]]]]]]

[01017 02533 02530 03920]

Station

[[[[[[[[

02791 02093 01628 0348800224 00096 04936 0474400354 07235 00129 0228300081 00471 00905 08544

]]]]]]]]

[00189 01374 01366 07071]

Suburban

[[[[[[[[

01870 02439 01463 0422800557 00557 02256 0663000780 04150 00613 0445700151 00464 00690 08695

]]]]]]]]

[00287 00837 00837 08039]

Viaduct [17]

[[[[[[[[

02917 05208 01667 0020800200 01000 05200 0360002577 04330 00928 0216500673 02115 02788 04423

]]]]]]]]

[01371 02800 02857 02971]

000

003

002

001

011

010

020

030

031

032

022

023

021

012

013

03330 31 32 33

Figure 4 State transition diagram of the four-state MCM

Note that the state in the MCM only considers the varia-tion of MPCs from the current moment to the next momentWith the motion of the Rx the states can be transformed toeach other Figure 4 illustrates the state transition diagramof the four-state MCM [30] The probabilistic switchingprocess between states in the MCM is controlled by the statetransition probability matrix P given by

P = 119901119894119895 =[[[[[[

11990100 11990101 11990102 1199010311990110 11990111 11990112 1199011311990120 11990121 11990122 1199012311990130 11990131 11990132 11990133

]]]]]]

(6)

where 119894 and 119895 represent the state index while 119901119894119895 is thetransition probability from state 119878119894 to state 119878119895 Note that 119901119894119895must satisfy the following requirement

0 le 119901119894119895 le 1 119894 119895 = 0 1 119873 minus 1 (7)

119873minus1sum119895=0

119901119894119895 = 1 119894 = 0 1 119873 minus 1 (8)

where119873 is the number of states that is119873 = 4 in our caseThe steady-state probability can be expressed as

P119878 = [1198751198780 1198751198781 1198751198782 1198751198783] (9)

which satisfies 1198751198780 + 1198751198781 + 1198751198782 + 1198751198783 = 1 Each element in P119878indicates the overall state occupancy probability

The obtained results of the state transition probabilitymatrix in different scenarios are listed in Table 4 Here thestate transition matrix is derived from each CIR Since thesample rate of CIR is 2000 Hz and the velocity of train is79 ms the reference value for state transition matrix is 004m It is observed that in the suburban scenario the next stateis more likely to transit into the state 1198783 no matter what thecurrent state is 1198780 1198781 or 1198782 This means some new MPCs areborn and older MPCs die most of the time because of therich reflection and scattering components in the suburbanscenario For other scenarios in the case of 1198780 the next statecould be any one of the four states in the case of 1198781 1198782 or1198783 have the maximum probability to be the next state inthe case of 1198782 the next state is more likely to transit into1198781 in the case of 1198783 the next state is still likely to be 1198783The results show the nonsymmetric transition matrix whichmeans that the transition probability from state A to state Bhas no relationship with that from state B to state A

According to the state transition probability matrix thesteady-state probability can be derived as listed in Table 4It is found that 1198783 is the most likely state in the stationand suburban scenarios For the rural scenario either 1198781 1198782

6 Wireless Communications and Mobile Computing

or 1198783 could be the steady state It is worth noting that 1198781and 1198782 have approximately similar steady-state probabilityin any scenario This confirms that the appearance anddisappearance of MPCs are equivalent The above results inthe rural scenario are similar to those in the open viaductscenario reported in [26]The obtained results can be appliedfor ONOFF tapped delay line models that use the Markovchain to model the ONOFF process of MPCs [31]

5 Conclusion

This paper analyzes the nonstationary characteristics in typi-calHSR scenarios rural station and suburban depending onthe passive LTE-based channel measurements With regardto the nonstationary characteristics it is found that the SIis longest in the rural scenario Additionally a four-stateMCM is established to characterize the B-D process ofMPCsand the corresponding state transition probability matrix andsteady-state probability are provided These results show therealistic channel characteristics in the HSR communicationnetwork which will provide helpful information for nonsta-tionary channel modeling of HSR communication systems

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the Fundamental ResearchFunds for the Central Universities (Grant 2018JBZ102)the National Natural Science Foundation of China (Grant61701017) the Beijing Municipal Natural Science Foundation(Grant 4174102) and the Open Research Fund throughthe National Mobile Communications Research LaboratorySoutheast University (Grant 2018D11)

References

[1] R He B Ai G Wang et al ldquoHigh-Speed railway communi-cations from GSM-R to LTE-Rrdquo IEEE Vehicular TechnologyMagazine vol 11 no 3 pp 49ndash58 2016

[2] B Ai X Cheng T Kurner et al ldquoChallenges toward wirelesscommunications for high-speed railwayrdquo IEEE Transactions onIntelligent Transportation Systems vol 15 no 5 pp 2143ndash21582014

[3] White Paper for 5G High Mobility [Online] Available httpwwwfuture-forumorg

[4] H Wei Z Zhong K Guan and B Ai ldquoPath loss modelsin viaduct and plain scenarios of the High-speed Railwayrdquoin Proceedings of the 5th International ICST Conference onCommunications and Networking in China Beijing ChinaAugust 2010

[5] R He Z Zhong B Ai and J Ding ldquoAn empirical pathloss model and fading analysis for high-speed railway viaductscenariosrdquo IEEE Antennas andWireless Propagation Letters vol10 pp 808ndash812 2011

[6] K Guan Z Zhong B Ai and T Kurner ldquoEmpirical models forextra propagation loss of train stations on high-speed railwayrdquoIEEE Transactions on Antennas and Propagation vol 62 no 3pp 1395ndash1408 2014

[7] R He Z Zhong B Ai and C Oestges ldquoShadow fading corre-lation in high-speed railway environmentsrdquo IEEE Transactionson Vehicular Technology vol 64 no 7 pp 2762ndash2772 2015

[8] L Liu C Tao D W Matolak T Zhou and H Chen ldquoInvesti-gation of Shadowing Effects in Typical Propagation Scenariosfor High-Speed Railway at 2350MHzrdquo International Journal ofAntennas and Propagation vol 2016 pp 1ndash8 2016

[9] O Renaudin V-M Kolmonen P Vainikainen and C Oest-ges ldquoNon-stationary narrowband MIMO inter-vehicle channelcharacterization in the 5-GHz bandrdquo IEEE Transactions onVehicular Technology vol 59 no 4 pp 2007ndash2015 2010

[10] D W Matolak ldquoModeling the vehicle-to-vehicle propagationchannel A reviewrdquo Radio Science vol 49 no 9 pp 721ndash7362014

[11] L Liu C Tao J Qiu et al ldquoPosition-basedmodeling forwirelesschannel on high-speed railway under a viaduct at 235GHzrdquoIEEE Journal on Selected Areas in Communications vol 30 no4 pp 834ndash845 2012

[12] T Zhou C Tao L Liu and Z Tan ldquoA semiempirical MIMOchannel model in obstructed viaduct scenarios on high-speedrailwayrdquo International Journal of Antennas and Propagation vol2014 Article ID 287159 10 pages 2014

[13] T Zhou C Tao S Salous Z Tan L Liu and L TianldquoGraph-based stochastic model for high-speed railway cuttingscenariosrdquo IETMicrowaves Antennas amp Propagation vol 9 no15 pp 1691ndash1697 2015

[14] R Sun C Tao L Liu and Z Tan ldquoChannel Measurement andCharacterization for HSR U-Shape Groove Scenarios at 235GHzrdquo in Proceedings of the 2013 IEEE 78th Vehicular TechnologyConference (VTC Fall) pp 1ndash5 Las Vegas NV USA September2013

[15] T Zhou C Tao S Salous and L Liu ldquoSpatial Characterizationfor High-Speed Railway Channels Based on Moving VirtualArray Measurement Schemerdquo IEEE Antennas and WirelessPropagation Letters vol 16 pp 1423ndash1426 2017

[16] R He Z Zhong B Ai G Wang J Ding and A F MolischldquoMeasurements and analysis of propagation channels in high-speed railway viaductsrdquo IEEE Transactions onWireless Commu-nications vol 12 no 2 pp 794ndash805 2013

[17] R He Z Zhong B Ai J Ding Y Yang and A F MolischldquoShort-term fading behavior in high-speed railway cuttingscenario Measurements analysis and statistical modelsrdquo IEEETransactions on Antennas and Propagation vol 61 no 4 pp2209ndash2222 2013

[18] P Kyosti et al WINNER II channel models part II radiochannel measurement and analysis results vol ST-4-0277562007 WINNER II D112 v10

[19] R Parviainen P Ky Y Hsieh P Ting and J Chiou ldquoResultsof high speed train channel measurements inrdquo in Proc COST2100 TD France Lille 2008

[20] F Luan Y Zhang L Xiao C Zhou and S Zhou ldquoFadingcharacteristics of wireless channel on high-speed railway inhilly terrain scenariordquo International Journal of Antennas andPropagation vol 2013 Article ID 378407 9 pages 2013

Wireless Communications and Mobile Computing 7

[21] Y Zhang ZHeWZhang L Xiao and S Zhou ldquoMeasurement-based delay and doppler characterizations for high-speedrailway hilly scenariordquo International Journal of Antennas andPropagation vol 2014 Article ID 875345 8 pages 2014

[22] P Aikio R Gruber and P Vainikainen ldquoWideband radio chan-nel measurements for train tunnelsrdquo in Proceedings of the VTCrsquo98 48th IEEE Vehicular Technology Conference Pathway to aGlobalWireless Revolution pp 460ndash464 Ottawa Ont Canada

[23] KGuan Z Zhong B Ai andT Kurner ldquoPropagationmeasure-ments and modeling of crossing bridges on high-speed railwayat 930 MHzrdquo IEEE Transactions on Vehicular Technology vol63 no 8 pp 3349ndash3516 2014

[24] T Zhou C Tao S Salous L Liu and Z Tan ldquoChannelcharacterization in high-speed railway station environments at189 GHzrdquo Radio Science vol 50 no 11 pp 1176ndash1186 2015

[25] B Chen Z Zhong and B Ai ldquoStationarity intervals of time-variant channel in high speed railway scenariordquo China Commu-nications vol 9 no 8 pp 64ndash70 2012

[26] L Liu C Tao J Qiu T Zhou R Sun and H Chen ldquoThedynamic evolution of multipath components in High-SpeedRailway in viaduct scenarios From the birth-death processpoint of viewrdquo in Proceedings of the 2012 IEEE 23rd InternationalSymposium on Personal Indoor and Mobile Radio Commu-nications - (PIMRC 2012) pp 1774ndash1778 Sydney AustraliaSeptember 2012

[27] T Zhou C Tao S Salous L Liu andZ Tan ldquoChannel soundingfor high-speed railway communication systemsrdquo IEEE Commu-nications Magazine vol 53 no 10 pp 70ndash77 2015

[28] T Zhou C Tao S Salous L Liu and Z Tan ldquoImplementationof an LTE-based channel measurement method for high-speedrailway scenariosrdquo IEEE Transactions on Instrumentation andMeasurement vol 65 no 1 pp 25ndash36 2016

[29] R He O Renaudin V-M Kolmonen et al ldquoCharacterizationof quasi-stationarity regions for vehicle-to-vehicle radio chan-nelsrdquo IEEE Transactions on Antennas and Propagation vol 63no 5 pp 2237ndash2251 2015

[30] C-C Chong C-M Tan D I Laurenson S McLaughlin MA Beach andA RNix ldquoA novel wideband dynamic directionalindoor channel model based on a Markov processrdquo IEEETransactions on Wireless Communications vol 4 no 4 pp1539ndash1552 2005

[31] I Sen and D Matolak ldquoVehicle channel models for the 5-ghzbandrdquo IEEET Intell Transp vol 9 no 2 pp 235ndash245 June 2008

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Page 4: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

4 Wireless Communications and Mobile Computing

Table 3 Analysis results of the SI in different scenarios

Scenario Method Statistics SI (m)119888119905ℎ = 07 119888119905ℎ = 08 119888119905ℎ = 09

Rural LRSMean value 143 646 28460 of CCDF 822 429 16580 of CCDF 474 264 099

Station LRSMean value 727 384 18760 of CCDF 502 255 11780 of CCDF 307 125 037

Suburban LRSMean value 762 374 13760 of CCDF 543 233 08680 of CCDF 354 125 035

Viaduct [26] LRS 60 of CCDF - 18 -80 of CCDF - 081 -

Standard models [26] LRS 60 of CCDF - 34 -Station [25] RUN test 80 of CCDF 4

5 10 15 20 25 30 350SI [m]

0

01

02

03

04

05

06

07

08

09

1

CCD

F

=NB=07 (Station)

=NB=07 (Rural)

=NB=08 (Station)

=NB=08 (Rural)=NB=09 (Rural)

=NB=07 (Suburban)=NB=08 (Suburban)=NB=09 (Suburban)

=NB=09 (Station)

Figure 3 CCDFs of SI in rural station and suburban scenarios

where 119871 is the length of used data Here we consider threetypical correlation threshold values 119888119905ℎ = 07 119888119905ℎ = 08 and119888119905ℎ = 09

Figure 3 compares the derived SI results for different 119888119905ℎand scenarios in terms of complementary CDF (CCDF)With the increase of 119888119905ℎ the SI is gradually decreasing For119888119905ℎ = 09 only slight difference can be observed in differentenvironments while there is an obvious deviation for 119888119905ℎ =07 and 119888119905ℎ = 08 Focusing on the cases of 119888119905ℎ = 07 and119888119905ℎ = 08 we find that the SI values in the rural scenario arelarger than those in the station or suburban scenarios Thisis understandable from the completely different scatteringenvironment Since LoS is dominant in the rural scenario

the MPCs have smaller dynamic changes over time andthus the stationary distance is longer When it comes to thestation and suburban scenarios due to the rich reflection andscattering components from the awnings or buildings thenonstationarity is more serious and thus the SI decreasesThis nonstationarity could be originated from a special physi-cal phenomena for example ldquoappearance anddisappearancerdquoor ldquobirth and deathrdquo of MPCs [26] which will be furtherinvestigated in the following subsection

The detailed statistical SI results including mean valueand 60 and 80 of CCDF for different scenarios are listedin Table 3 For 119888119905ℎ = 08 the mean value of SI is 646 m inthe rural scenario while those are 384 m and 374 m in thestation and suburban scenarios In 60 and 80 of cases thechannel could be stationary over a distance of 233-429mand125-264 m for the measured scenarios respectively Thesevalues are higher than the results of 18 m for 60 and 081 mfor 80 reported in [25] From [25] the calculated stationaryinterval for standard channel models is equal to 34 m for60 which is shorter than the one of 429 m for the ruralscenario but is longer than the ones of 255 m and 233 m forthe station and suburban scenarios It is also observed thatthe value of SI in 80 of the cases in the measured stationscenario is smaller than that of around 4 m in the stationscenario reported in [24] This variance could be due to theuse of different calculation methods

42 Birth-Death Process The nonstationarity of the channelis basically due to the dynamic evolution of MPCs when theRx is in motion for example appearance to disappearance orB-D To describe this B-D process a four-state Markov chainmodel (MCM) is used where each state is defined as follows[30]

(i) 1198780 no ldquobirthsrdquo or ldquodeathsrdquo(ii) 1198781 ldquobirthsrdquo only(iii) 1198782 ldquodeathsrdquo only(iv) 1198783 both ldquobirthsrdquo and ldquodeathsrdquo

Wireless Communications and Mobile Computing 5

Table 4 Analysis results of the B-D process in different scenarios

Scenario State transition probability matrix Steady-state probability

Rural

[[[[[[[[

03361 04025 01203 0141100497 00315 05348 0384101658 07148 00282 0091200332 00600 02505 06563

]]]]]]]]

[01017 02533 02530 03920]

Station

[[[[[[[[

02791 02093 01628 0348800224 00096 04936 0474400354 07235 00129 0228300081 00471 00905 08544

]]]]]]]]

[00189 01374 01366 07071]

Suburban

[[[[[[[[

01870 02439 01463 0422800557 00557 02256 0663000780 04150 00613 0445700151 00464 00690 08695

]]]]]]]]

[00287 00837 00837 08039]

Viaduct [17]

[[[[[[[[

02917 05208 01667 0020800200 01000 05200 0360002577 04330 00928 0216500673 02115 02788 04423

]]]]]]]]

[01371 02800 02857 02971]

000

003

002

001

011

010

020

030

031

032

022

023

021

012

013

03330 31 32 33

Figure 4 State transition diagram of the four-state MCM

Note that the state in the MCM only considers the varia-tion of MPCs from the current moment to the next momentWith the motion of the Rx the states can be transformed toeach other Figure 4 illustrates the state transition diagramof the four-state MCM [30] The probabilistic switchingprocess between states in the MCM is controlled by the statetransition probability matrix P given by

P = 119901119894119895 =[[[[[[

11990100 11990101 11990102 1199010311990110 11990111 11990112 1199011311990120 11990121 11990122 1199012311990130 11990131 11990132 11990133

]]]]]]

(6)

where 119894 and 119895 represent the state index while 119901119894119895 is thetransition probability from state 119878119894 to state 119878119895 Note that 119901119894119895must satisfy the following requirement

0 le 119901119894119895 le 1 119894 119895 = 0 1 119873 minus 1 (7)

119873minus1sum119895=0

119901119894119895 = 1 119894 = 0 1 119873 minus 1 (8)

where119873 is the number of states that is119873 = 4 in our caseThe steady-state probability can be expressed as

P119878 = [1198751198780 1198751198781 1198751198782 1198751198783] (9)

which satisfies 1198751198780 + 1198751198781 + 1198751198782 + 1198751198783 = 1 Each element in P119878indicates the overall state occupancy probability

The obtained results of the state transition probabilitymatrix in different scenarios are listed in Table 4 Here thestate transition matrix is derived from each CIR Since thesample rate of CIR is 2000 Hz and the velocity of train is79 ms the reference value for state transition matrix is 004m It is observed that in the suburban scenario the next stateis more likely to transit into the state 1198783 no matter what thecurrent state is 1198780 1198781 or 1198782 This means some new MPCs areborn and older MPCs die most of the time because of therich reflection and scattering components in the suburbanscenario For other scenarios in the case of 1198780 the next statecould be any one of the four states in the case of 1198781 1198782 or1198783 have the maximum probability to be the next state inthe case of 1198782 the next state is more likely to transit into1198781 in the case of 1198783 the next state is still likely to be 1198783The results show the nonsymmetric transition matrix whichmeans that the transition probability from state A to state Bhas no relationship with that from state B to state A

According to the state transition probability matrix thesteady-state probability can be derived as listed in Table 4It is found that 1198783 is the most likely state in the stationand suburban scenarios For the rural scenario either 1198781 1198782

6 Wireless Communications and Mobile Computing

or 1198783 could be the steady state It is worth noting that 1198781and 1198782 have approximately similar steady-state probabilityin any scenario This confirms that the appearance anddisappearance of MPCs are equivalent The above results inthe rural scenario are similar to those in the open viaductscenario reported in [26]The obtained results can be appliedfor ONOFF tapped delay line models that use the Markovchain to model the ONOFF process of MPCs [31]

5 Conclusion

This paper analyzes the nonstationary characteristics in typi-calHSR scenarios rural station and suburban depending onthe passive LTE-based channel measurements With regardto the nonstationary characteristics it is found that the SIis longest in the rural scenario Additionally a four-stateMCM is established to characterize the B-D process ofMPCsand the corresponding state transition probability matrix andsteady-state probability are provided These results show therealistic channel characteristics in the HSR communicationnetwork which will provide helpful information for nonsta-tionary channel modeling of HSR communication systems

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the Fundamental ResearchFunds for the Central Universities (Grant 2018JBZ102)the National Natural Science Foundation of China (Grant61701017) the Beijing Municipal Natural Science Foundation(Grant 4174102) and the Open Research Fund throughthe National Mobile Communications Research LaboratorySoutheast University (Grant 2018D11)

References

[1] R He B Ai G Wang et al ldquoHigh-Speed railway communi-cations from GSM-R to LTE-Rrdquo IEEE Vehicular TechnologyMagazine vol 11 no 3 pp 49ndash58 2016

[2] B Ai X Cheng T Kurner et al ldquoChallenges toward wirelesscommunications for high-speed railwayrdquo IEEE Transactions onIntelligent Transportation Systems vol 15 no 5 pp 2143ndash21582014

[3] White Paper for 5G High Mobility [Online] Available httpwwwfuture-forumorg

[4] H Wei Z Zhong K Guan and B Ai ldquoPath loss modelsin viaduct and plain scenarios of the High-speed Railwayrdquoin Proceedings of the 5th International ICST Conference onCommunications and Networking in China Beijing ChinaAugust 2010

[5] R He Z Zhong B Ai and J Ding ldquoAn empirical pathloss model and fading analysis for high-speed railway viaductscenariosrdquo IEEE Antennas andWireless Propagation Letters vol10 pp 808ndash812 2011

[6] K Guan Z Zhong B Ai and T Kurner ldquoEmpirical models forextra propagation loss of train stations on high-speed railwayrdquoIEEE Transactions on Antennas and Propagation vol 62 no 3pp 1395ndash1408 2014

[7] R He Z Zhong B Ai and C Oestges ldquoShadow fading corre-lation in high-speed railway environmentsrdquo IEEE Transactionson Vehicular Technology vol 64 no 7 pp 2762ndash2772 2015

[8] L Liu C Tao D W Matolak T Zhou and H Chen ldquoInvesti-gation of Shadowing Effects in Typical Propagation Scenariosfor High-Speed Railway at 2350MHzrdquo International Journal ofAntennas and Propagation vol 2016 pp 1ndash8 2016

[9] O Renaudin V-M Kolmonen P Vainikainen and C Oest-ges ldquoNon-stationary narrowband MIMO inter-vehicle channelcharacterization in the 5-GHz bandrdquo IEEE Transactions onVehicular Technology vol 59 no 4 pp 2007ndash2015 2010

[10] D W Matolak ldquoModeling the vehicle-to-vehicle propagationchannel A reviewrdquo Radio Science vol 49 no 9 pp 721ndash7362014

[11] L Liu C Tao J Qiu et al ldquoPosition-basedmodeling forwirelesschannel on high-speed railway under a viaduct at 235GHzrdquoIEEE Journal on Selected Areas in Communications vol 30 no4 pp 834ndash845 2012

[12] T Zhou C Tao L Liu and Z Tan ldquoA semiempirical MIMOchannel model in obstructed viaduct scenarios on high-speedrailwayrdquo International Journal of Antennas and Propagation vol2014 Article ID 287159 10 pages 2014

[13] T Zhou C Tao S Salous Z Tan L Liu and L TianldquoGraph-based stochastic model for high-speed railway cuttingscenariosrdquo IETMicrowaves Antennas amp Propagation vol 9 no15 pp 1691ndash1697 2015

[14] R Sun C Tao L Liu and Z Tan ldquoChannel Measurement andCharacterization for HSR U-Shape Groove Scenarios at 235GHzrdquo in Proceedings of the 2013 IEEE 78th Vehicular TechnologyConference (VTC Fall) pp 1ndash5 Las Vegas NV USA September2013

[15] T Zhou C Tao S Salous and L Liu ldquoSpatial Characterizationfor High-Speed Railway Channels Based on Moving VirtualArray Measurement Schemerdquo IEEE Antennas and WirelessPropagation Letters vol 16 pp 1423ndash1426 2017

[16] R He Z Zhong B Ai G Wang J Ding and A F MolischldquoMeasurements and analysis of propagation channels in high-speed railway viaductsrdquo IEEE Transactions onWireless Commu-nications vol 12 no 2 pp 794ndash805 2013

[17] R He Z Zhong B Ai J Ding Y Yang and A F MolischldquoShort-term fading behavior in high-speed railway cuttingscenario Measurements analysis and statistical modelsrdquo IEEETransactions on Antennas and Propagation vol 61 no 4 pp2209ndash2222 2013

[18] P Kyosti et al WINNER II channel models part II radiochannel measurement and analysis results vol ST-4-0277562007 WINNER II D112 v10

[19] R Parviainen P Ky Y Hsieh P Ting and J Chiou ldquoResultsof high speed train channel measurements inrdquo in Proc COST2100 TD France Lille 2008

[20] F Luan Y Zhang L Xiao C Zhou and S Zhou ldquoFadingcharacteristics of wireless channel on high-speed railway inhilly terrain scenariordquo International Journal of Antennas andPropagation vol 2013 Article ID 378407 9 pages 2013

Wireless Communications and Mobile Computing 7

[21] Y Zhang ZHeWZhang L Xiao and S Zhou ldquoMeasurement-based delay and doppler characterizations for high-speedrailway hilly scenariordquo International Journal of Antennas andPropagation vol 2014 Article ID 875345 8 pages 2014

[22] P Aikio R Gruber and P Vainikainen ldquoWideband radio chan-nel measurements for train tunnelsrdquo in Proceedings of the VTCrsquo98 48th IEEE Vehicular Technology Conference Pathway to aGlobalWireless Revolution pp 460ndash464 Ottawa Ont Canada

[23] KGuan Z Zhong B Ai andT Kurner ldquoPropagationmeasure-ments and modeling of crossing bridges on high-speed railwayat 930 MHzrdquo IEEE Transactions on Vehicular Technology vol63 no 8 pp 3349ndash3516 2014

[24] T Zhou C Tao S Salous L Liu and Z Tan ldquoChannelcharacterization in high-speed railway station environments at189 GHzrdquo Radio Science vol 50 no 11 pp 1176ndash1186 2015

[25] B Chen Z Zhong and B Ai ldquoStationarity intervals of time-variant channel in high speed railway scenariordquo China Commu-nications vol 9 no 8 pp 64ndash70 2012

[26] L Liu C Tao J Qiu T Zhou R Sun and H Chen ldquoThedynamic evolution of multipath components in High-SpeedRailway in viaduct scenarios From the birth-death processpoint of viewrdquo in Proceedings of the 2012 IEEE 23rd InternationalSymposium on Personal Indoor and Mobile Radio Commu-nications - (PIMRC 2012) pp 1774ndash1778 Sydney AustraliaSeptember 2012

[27] T Zhou C Tao S Salous L Liu andZ Tan ldquoChannel soundingfor high-speed railway communication systemsrdquo IEEE Commu-nications Magazine vol 53 no 10 pp 70ndash77 2015

[28] T Zhou C Tao S Salous L Liu and Z Tan ldquoImplementationof an LTE-based channel measurement method for high-speedrailway scenariosrdquo IEEE Transactions on Instrumentation andMeasurement vol 65 no 1 pp 25ndash36 2016

[29] R He O Renaudin V-M Kolmonen et al ldquoCharacterizationof quasi-stationarity regions for vehicle-to-vehicle radio chan-nelsrdquo IEEE Transactions on Antennas and Propagation vol 63no 5 pp 2237ndash2251 2015

[30] C-C Chong C-M Tan D I Laurenson S McLaughlin MA Beach andA RNix ldquoA novel wideband dynamic directionalindoor channel model based on a Markov processrdquo IEEETransactions on Wireless Communications vol 4 no 4 pp1539ndash1552 2005

[31] I Sen and D Matolak ldquoVehicle channel models for the 5-ghzbandrdquo IEEET Intell Transp vol 9 no 2 pp 235ndash245 June 2008

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

Page 5: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

Wireless Communications and Mobile Computing 5

Table 4 Analysis results of the B-D process in different scenarios

Scenario State transition probability matrix Steady-state probability

Rural

[[[[[[[[

03361 04025 01203 0141100497 00315 05348 0384101658 07148 00282 0091200332 00600 02505 06563

]]]]]]]]

[01017 02533 02530 03920]

Station

[[[[[[[[

02791 02093 01628 0348800224 00096 04936 0474400354 07235 00129 0228300081 00471 00905 08544

]]]]]]]]

[00189 01374 01366 07071]

Suburban

[[[[[[[[

01870 02439 01463 0422800557 00557 02256 0663000780 04150 00613 0445700151 00464 00690 08695

]]]]]]]]

[00287 00837 00837 08039]

Viaduct [17]

[[[[[[[[

02917 05208 01667 0020800200 01000 05200 0360002577 04330 00928 0216500673 02115 02788 04423

]]]]]]]]

[01371 02800 02857 02971]

000

003

002

001

011

010

020

030

031

032

022

023

021

012

013

03330 31 32 33

Figure 4 State transition diagram of the four-state MCM

Note that the state in the MCM only considers the varia-tion of MPCs from the current moment to the next momentWith the motion of the Rx the states can be transformed toeach other Figure 4 illustrates the state transition diagramof the four-state MCM [30] The probabilistic switchingprocess between states in the MCM is controlled by the statetransition probability matrix P given by

P = 119901119894119895 =[[[[[[

11990100 11990101 11990102 1199010311990110 11990111 11990112 1199011311990120 11990121 11990122 1199012311990130 11990131 11990132 11990133

]]]]]]

(6)

where 119894 and 119895 represent the state index while 119901119894119895 is thetransition probability from state 119878119894 to state 119878119895 Note that 119901119894119895must satisfy the following requirement

0 le 119901119894119895 le 1 119894 119895 = 0 1 119873 minus 1 (7)

119873minus1sum119895=0

119901119894119895 = 1 119894 = 0 1 119873 minus 1 (8)

where119873 is the number of states that is119873 = 4 in our caseThe steady-state probability can be expressed as

P119878 = [1198751198780 1198751198781 1198751198782 1198751198783] (9)

which satisfies 1198751198780 + 1198751198781 + 1198751198782 + 1198751198783 = 1 Each element in P119878indicates the overall state occupancy probability

The obtained results of the state transition probabilitymatrix in different scenarios are listed in Table 4 Here thestate transition matrix is derived from each CIR Since thesample rate of CIR is 2000 Hz and the velocity of train is79 ms the reference value for state transition matrix is 004m It is observed that in the suburban scenario the next stateis more likely to transit into the state 1198783 no matter what thecurrent state is 1198780 1198781 or 1198782 This means some new MPCs areborn and older MPCs die most of the time because of therich reflection and scattering components in the suburbanscenario For other scenarios in the case of 1198780 the next statecould be any one of the four states in the case of 1198781 1198782 or1198783 have the maximum probability to be the next state inthe case of 1198782 the next state is more likely to transit into1198781 in the case of 1198783 the next state is still likely to be 1198783The results show the nonsymmetric transition matrix whichmeans that the transition probability from state A to state Bhas no relationship with that from state B to state A

According to the state transition probability matrix thesteady-state probability can be derived as listed in Table 4It is found that 1198783 is the most likely state in the stationand suburban scenarios For the rural scenario either 1198781 1198782

6 Wireless Communications and Mobile Computing

or 1198783 could be the steady state It is worth noting that 1198781and 1198782 have approximately similar steady-state probabilityin any scenario This confirms that the appearance anddisappearance of MPCs are equivalent The above results inthe rural scenario are similar to those in the open viaductscenario reported in [26]The obtained results can be appliedfor ONOFF tapped delay line models that use the Markovchain to model the ONOFF process of MPCs [31]

5 Conclusion

This paper analyzes the nonstationary characteristics in typi-calHSR scenarios rural station and suburban depending onthe passive LTE-based channel measurements With regardto the nonstationary characteristics it is found that the SIis longest in the rural scenario Additionally a four-stateMCM is established to characterize the B-D process ofMPCsand the corresponding state transition probability matrix andsteady-state probability are provided These results show therealistic channel characteristics in the HSR communicationnetwork which will provide helpful information for nonsta-tionary channel modeling of HSR communication systems

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the Fundamental ResearchFunds for the Central Universities (Grant 2018JBZ102)the National Natural Science Foundation of China (Grant61701017) the Beijing Municipal Natural Science Foundation(Grant 4174102) and the Open Research Fund throughthe National Mobile Communications Research LaboratorySoutheast University (Grant 2018D11)

References

[1] R He B Ai G Wang et al ldquoHigh-Speed railway communi-cations from GSM-R to LTE-Rrdquo IEEE Vehicular TechnologyMagazine vol 11 no 3 pp 49ndash58 2016

[2] B Ai X Cheng T Kurner et al ldquoChallenges toward wirelesscommunications for high-speed railwayrdquo IEEE Transactions onIntelligent Transportation Systems vol 15 no 5 pp 2143ndash21582014

[3] White Paper for 5G High Mobility [Online] Available httpwwwfuture-forumorg

[4] H Wei Z Zhong K Guan and B Ai ldquoPath loss modelsin viaduct and plain scenarios of the High-speed Railwayrdquoin Proceedings of the 5th International ICST Conference onCommunications and Networking in China Beijing ChinaAugust 2010

[5] R He Z Zhong B Ai and J Ding ldquoAn empirical pathloss model and fading analysis for high-speed railway viaductscenariosrdquo IEEE Antennas andWireless Propagation Letters vol10 pp 808ndash812 2011

[6] K Guan Z Zhong B Ai and T Kurner ldquoEmpirical models forextra propagation loss of train stations on high-speed railwayrdquoIEEE Transactions on Antennas and Propagation vol 62 no 3pp 1395ndash1408 2014

[7] R He Z Zhong B Ai and C Oestges ldquoShadow fading corre-lation in high-speed railway environmentsrdquo IEEE Transactionson Vehicular Technology vol 64 no 7 pp 2762ndash2772 2015

[8] L Liu C Tao D W Matolak T Zhou and H Chen ldquoInvesti-gation of Shadowing Effects in Typical Propagation Scenariosfor High-Speed Railway at 2350MHzrdquo International Journal ofAntennas and Propagation vol 2016 pp 1ndash8 2016

[9] O Renaudin V-M Kolmonen P Vainikainen and C Oest-ges ldquoNon-stationary narrowband MIMO inter-vehicle channelcharacterization in the 5-GHz bandrdquo IEEE Transactions onVehicular Technology vol 59 no 4 pp 2007ndash2015 2010

[10] D W Matolak ldquoModeling the vehicle-to-vehicle propagationchannel A reviewrdquo Radio Science vol 49 no 9 pp 721ndash7362014

[11] L Liu C Tao J Qiu et al ldquoPosition-basedmodeling forwirelesschannel on high-speed railway under a viaduct at 235GHzrdquoIEEE Journal on Selected Areas in Communications vol 30 no4 pp 834ndash845 2012

[12] T Zhou C Tao L Liu and Z Tan ldquoA semiempirical MIMOchannel model in obstructed viaduct scenarios on high-speedrailwayrdquo International Journal of Antennas and Propagation vol2014 Article ID 287159 10 pages 2014

[13] T Zhou C Tao S Salous Z Tan L Liu and L TianldquoGraph-based stochastic model for high-speed railway cuttingscenariosrdquo IETMicrowaves Antennas amp Propagation vol 9 no15 pp 1691ndash1697 2015

[14] R Sun C Tao L Liu and Z Tan ldquoChannel Measurement andCharacterization for HSR U-Shape Groove Scenarios at 235GHzrdquo in Proceedings of the 2013 IEEE 78th Vehicular TechnologyConference (VTC Fall) pp 1ndash5 Las Vegas NV USA September2013

[15] T Zhou C Tao S Salous and L Liu ldquoSpatial Characterizationfor High-Speed Railway Channels Based on Moving VirtualArray Measurement Schemerdquo IEEE Antennas and WirelessPropagation Letters vol 16 pp 1423ndash1426 2017

[16] R He Z Zhong B Ai G Wang J Ding and A F MolischldquoMeasurements and analysis of propagation channels in high-speed railway viaductsrdquo IEEE Transactions onWireless Commu-nications vol 12 no 2 pp 794ndash805 2013

[17] R He Z Zhong B Ai J Ding Y Yang and A F MolischldquoShort-term fading behavior in high-speed railway cuttingscenario Measurements analysis and statistical modelsrdquo IEEETransactions on Antennas and Propagation vol 61 no 4 pp2209ndash2222 2013

[18] P Kyosti et al WINNER II channel models part II radiochannel measurement and analysis results vol ST-4-0277562007 WINNER II D112 v10

[19] R Parviainen P Ky Y Hsieh P Ting and J Chiou ldquoResultsof high speed train channel measurements inrdquo in Proc COST2100 TD France Lille 2008

[20] F Luan Y Zhang L Xiao C Zhou and S Zhou ldquoFadingcharacteristics of wireless channel on high-speed railway inhilly terrain scenariordquo International Journal of Antennas andPropagation vol 2013 Article ID 378407 9 pages 2013

Wireless Communications and Mobile Computing 7

[21] Y Zhang ZHeWZhang L Xiao and S Zhou ldquoMeasurement-based delay and doppler characterizations for high-speedrailway hilly scenariordquo International Journal of Antennas andPropagation vol 2014 Article ID 875345 8 pages 2014

[22] P Aikio R Gruber and P Vainikainen ldquoWideband radio chan-nel measurements for train tunnelsrdquo in Proceedings of the VTCrsquo98 48th IEEE Vehicular Technology Conference Pathway to aGlobalWireless Revolution pp 460ndash464 Ottawa Ont Canada

[23] KGuan Z Zhong B Ai andT Kurner ldquoPropagationmeasure-ments and modeling of crossing bridges on high-speed railwayat 930 MHzrdquo IEEE Transactions on Vehicular Technology vol63 no 8 pp 3349ndash3516 2014

[24] T Zhou C Tao S Salous L Liu and Z Tan ldquoChannelcharacterization in high-speed railway station environments at189 GHzrdquo Radio Science vol 50 no 11 pp 1176ndash1186 2015

[25] B Chen Z Zhong and B Ai ldquoStationarity intervals of time-variant channel in high speed railway scenariordquo China Commu-nications vol 9 no 8 pp 64ndash70 2012

[26] L Liu C Tao J Qiu T Zhou R Sun and H Chen ldquoThedynamic evolution of multipath components in High-SpeedRailway in viaduct scenarios From the birth-death processpoint of viewrdquo in Proceedings of the 2012 IEEE 23rd InternationalSymposium on Personal Indoor and Mobile Radio Commu-nications - (PIMRC 2012) pp 1774ndash1778 Sydney AustraliaSeptember 2012

[27] T Zhou C Tao S Salous L Liu andZ Tan ldquoChannel soundingfor high-speed railway communication systemsrdquo IEEE Commu-nications Magazine vol 53 no 10 pp 70ndash77 2015

[28] T Zhou C Tao S Salous L Liu and Z Tan ldquoImplementationof an LTE-based channel measurement method for high-speedrailway scenariosrdquo IEEE Transactions on Instrumentation andMeasurement vol 65 no 1 pp 25ndash36 2016

[29] R He O Renaudin V-M Kolmonen et al ldquoCharacterizationof quasi-stationarity regions for vehicle-to-vehicle radio chan-nelsrdquo IEEE Transactions on Antennas and Propagation vol 63no 5 pp 2237ndash2251 2015

[30] C-C Chong C-M Tan D I Laurenson S McLaughlin MA Beach andA RNix ldquoA novel wideband dynamic directionalindoor channel model based on a Markov processrdquo IEEETransactions on Wireless Communications vol 4 no 4 pp1539ndash1552 2005

[31] I Sen and D Matolak ldquoVehicle channel models for the 5-ghzbandrdquo IEEET Intell Transp vol 9 no 2 pp 235ndash245 June 2008

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

Page 6: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

6 Wireless Communications and Mobile Computing

or 1198783 could be the steady state It is worth noting that 1198781and 1198782 have approximately similar steady-state probabilityin any scenario This confirms that the appearance anddisappearance of MPCs are equivalent The above results inthe rural scenario are similar to those in the open viaductscenario reported in [26]The obtained results can be appliedfor ONOFF tapped delay line models that use the Markovchain to model the ONOFF process of MPCs [31]

5 Conclusion

This paper analyzes the nonstationary characteristics in typi-calHSR scenarios rural station and suburban depending onthe passive LTE-based channel measurements With regardto the nonstationary characteristics it is found that the SIis longest in the rural scenario Additionally a four-stateMCM is established to characterize the B-D process ofMPCsand the corresponding state transition probability matrix andsteady-state probability are provided These results show therealistic channel characteristics in the HSR communicationnetwork which will provide helpful information for nonsta-tionary channel modeling of HSR communication systems

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This work was supported by the Fundamental ResearchFunds for the Central Universities (Grant 2018JBZ102)the National Natural Science Foundation of China (Grant61701017) the Beijing Municipal Natural Science Foundation(Grant 4174102) and the Open Research Fund throughthe National Mobile Communications Research LaboratorySoutheast University (Grant 2018D11)

References

[1] R He B Ai G Wang et al ldquoHigh-Speed railway communi-cations from GSM-R to LTE-Rrdquo IEEE Vehicular TechnologyMagazine vol 11 no 3 pp 49ndash58 2016

[2] B Ai X Cheng T Kurner et al ldquoChallenges toward wirelesscommunications for high-speed railwayrdquo IEEE Transactions onIntelligent Transportation Systems vol 15 no 5 pp 2143ndash21582014

[3] White Paper for 5G High Mobility [Online] Available httpwwwfuture-forumorg

[4] H Wei Z Zhong K Guan and B Ai ldquoPath loss modelsin viaduct and plain scenarios of the High-speed Railwayrdquoin Proceedings of the 5th International ICST Conference onCommunications and Networking in China Beijing ChinaAugust 2010

[5] R He Z Zhong B Ai and J Ding ldquoAn empirical pathloss model and fading analysis for high-speed railway viaductscenariosrdquo IEEE Antennas andWireless Propagation Letters vol10 pp 808ndash812 2011

[6] K Guan Z Zhong B Ai and T Kurner ldquoEmpirical models forextra propagation loss of train stations on high-speed railwayrdquoIEEE Transactions on Antennas and Propagation vol 62 no 3pp 1395ndash1408 2014

[7] R He Z Zhong B Ai and C Oestges ldquoShadow fading corre-lation in high-speed railway environmentsrdquo IEEE Transactionson Vehicular Technology vol 64 no 7 pp 2762ndash2772 2015

[8] L Liu C Tao D W Matolak T Zhou and H Chen ldquoInvesti-gation of Shadowing Effects in Typical Propagation Scenariosfor High-Speed Railway at 2350MHzrdquo International Journal ofAntennas and Propagation vol 2016 pp 1ndash8 2016

[9] O Renaudin V-M Kolmonen P Vainikainen and C Oest-ges ldquoNon-stationary narrowband MIMO inter-vehicle channelcharacterization in the 5-GHz bandrdquo IEEE Transactions onVehicular Technology vol 59 no 4 pp 2007ndash2015 2010

[10] D W Matolak ldquoModeling the vehicle-to-vehicle propagationchannel A reviewrdquo Radio Science vol 49 no 9 pp 721ndash7362014

[11] L Liu C Tao J Qiu et al ldquoPosition-basedmodeling forwirelesschannel on high-speed railway under a viaduct at 235GHzrdquoIEEE Journal on Selected Areas in Communications vol 30 no4 pp 834ndash845 2012

[12] T Zhou C Tao L Liu and Z Tan ldquoA semiempirical MIMOchannel model in obstructed viaduct scenarios on high-speedrailwayrdquo International Journal of Antennas and Propagation vol2014 Article ID 287159 10 pages 2014

[13] T Zhou C Tao S Salous Z Tan L Liu and L TianldquoGraph-based stochastic model for high-speed railway cuttingscenariosrdquo IETMicrowaves Antennas amp Propagation vol 9 no15 pp 1691ndash1697 2015

[14] R Sun C Tao L Liu and Z Tan ldquoChannel Measurement andCharacterization for HSR U-Shape Groove Scenarios at 235GHzrdquo in Proceedings of the 2013 IEEE 78th Vehicular TechnologyConference (VTC Fall) pp 1ndash5 Las Vegas NV USA September2013

[15] T Zhou C Tao S Salous and L Liu ldquoSpatial Characterizationfor High-Speed Railway Channels Based on Moving VirtualArray Measurement Schemerdquo IEEE Antennas and WirelessPropagation Letters vol 16 pp 1423ndash1426 2017

[16] R He Z Zhong B Ai G Wang J Ding and A F MolischldquoMeasurements and analysis of propagation channels in high-speed railway viaductsrdquo IEEE Transactions onWireless Commu-nications vol 12 no 2 pp 794ndash805 2013

[17] R He Z Zhong B Ai J Ding Y Yang and A F MolischldquoShort-term fading behavior in high-speed railway cuttingscenario Measurements analysis and statistical modelsrdquo IEEETransactions on Antennas and Propagation vol 61 no 4 pp2209ndash2222 2013

[18] P Kyosti et al WINNER II channel models part II radiochannel measurement and analysis results vol ST-4-0277562007 WINNER II D112 v10

[19] R Parviainen P Ky Y Hsieh P Ting and J Chiou ldquoResultsof high speed train channel measurements inrdquo in Proc COST2100 TD France Lille 2008

[20] F Luan Y Zhang L Xiao C Zhou and S Zhou ldquoFadingcharacteristics of wireless channel on high-speed railway inhilly terrain scenariordquo International Journal of Antennas andPropagation vol 2013 Article ID 378407 9 pages 2013

Wireless Communications and Mobile Computing 7

[21] Y Zhang ZHeWZhang L Xiao and S Zhou ldquoMeasurement-based delay and doppler characterizations for high-speedrailway hilly scenariordquo International Journal of Antennas andPropagation vol 2014 Article ID 875345 8 pages 2014

[22] P Aikio R Gruber and P Vainikainen ldquoWideband radio chan-nel measurements for train tunnelsrdquo in Proceedings of the VTCrsquo98 48th IEEE Vehicular Technology Conference Pathway to aGlobalWireless Revolution pp 460ndash464 Ottawa Ont Canada

[23] KGuan Z Zhong B Ai andT Kurner ldquoPropagationmeasure-ments and modeling of crossing bridges on high-speed railwayat 930 MHzrdquo IEEE Transactions on Vehicular Technology vol63 no 8 pp 3349ndash3516 2014

[24] T Zhou C Tao S Salous L Liu and Z Tan ldquoChannelcharacterization in high-speed railway station environments at189 GHzrdquo Radio Science vol 50 no 11 pp 1176ndash1186 2015

[25] B Chen Z Zhong and B Ai ldquoStationarity intervals of time-variant channel in high speed railway scenariordquo China Commu-nications vol 9 no 8 pp 64ndash70 2012

[26] L Liu C Tao J Qiu T Zhou R Sun and H Chen ldquoThedynamic evolution of multipath components in High-SpeedRailway in viaduct scenarios From the birth-death processpoint of viewrdquo in Proceedings of the 2012 IEEE 23rd InternationalSymposium on Personal Indoor and Mobile Radio Commu-nications - (PIMRC 2012) pp 1774ndash1778 Sydney AustraliaSeptember 2012

[27] T Zhou C Tao S Salous L Liu andZ Tan ldquoChannel soundingfor high-speed railway communication systemsrdquo IEEE Commu-nications Magazine vol 53 no 10 pp 70ndash77 2015

[28] T Zhou C Tao S Salous L Liu and Z Tan ldquoImplementationof an LTE-based channel measurement method for high-speedrailway scenariosrdquo IEEE Transactions on Instrumentation andMeasurement vol 65 no 1 pp 25ndash36 2016

[29] R He O Renaudin V-M Kolmonen et al ldquoCharacterizationof quasi-stationarity regions for vehicle-to-vehicle radio chan-nelsrdquo IEEE Transactions on Antennas and Propagation vol 63no 5 pp 2237ndash2251 2015

[30] C-C Chong C-M Tan D I Laurenson S McLaughlin MA Beach andA RNix ldquoA novel wideband dynamic directionalindoor channel model based on a Markov processrdquo IEEETransactions on Wireless Communications vol 4 no 4 pp1539ndash1552 2005

[31] I Sen and D Matolak ldquoVehicle channel models for the 5-ghzbandrdquo IEEET Intell Transp vol 9 no 2 pp 235ndash245 June 2008

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

Page 7: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

Wireless Communications and Mobile Computing 7

[21] Y Zhang ZHeWZhang L Xiao and S Zhou ldquoMeasurement-based delay and doppler characterizations for high-speedrailway hilly scenariordquo International Journal of Antennas andPropagation vol 2014 Article ID 875345 8 pages 2014

[22] P Aikio R Gruber and P Vainikainen ldquoWideband radio chan-nel measurements for train tunnelsrdquo in Proceedings of the VTCrsquo98 48th IEEE Vehicular Technology Conference Pathway to aGlobalWireless Revolution pp 460ndash464 Ottawa Ont Canada

[23] KGuan Z Zhong B Ai andT Kurner ldquoPropagationmeasure-ments and modeling of crossing bridges on high-speed railwayat 930 MHzrdquo IEEE Transactions on Vehicular Technology vol63 no 8 pp 3349ndash3516 2014

[24] T Zhou C Tao S Salous L Liu and Z Tan ldquoChannelcharacterization in high-speed railway station environments at189 GHzrdquo Radio Science vol 50 no 11 pp 1176ndash1186 2015

[25] B Chen Z Zhong and B Ai ldquoStationarity intervals of time-variant channel in high speed railway scenariordquo China Commu-nications vol 9 no 8 pp 64ndash70 2012

[26] L Liu C Tao J Qiu T Zhou R Sun and H Chen ldquoThedynamic evolution of multipath components in High-SpeedRailway in viaduct scenarios From the birth-death processpoint of viewrdquo in Proceedings of the 2012 IEEE 23rd InternationalSymposium on Personal Indoor and Mobile Radio Commu-nications - (PIMRC 2012) pp 1774ndash1778 Sydney AustraliaSeptember 2012

[27] T Zhou C Tao S Salous L Liu andZ Tan ldquoChannel soundingfor high-speed railway communication systemsrdquo IEEE Commu-nications Magazine vol 53 no 10 pp 70ndash77 2015

[28] T Zhou C Tao S Salous L Liu and Z Tan ldquoImplementationof an LTE-based channel measurement method for high-speedrailway scenariosrdquo IEEE Transactions on Instrumentation andMeasurement vol 65 no 1 pp 25ndash36 2016

[29] R He O Renaudin V-M Kolmonen et al ldquoCharacterizationof quasi-stationarity regions for vehicle-to-vehicle radio chan-nelsrdquo IEEE Transactions on Antennas and Propagation vol 63no 5 pp 2237ndash2251 2015

[30] C-C Chong C-M Tan D I Laurenson S McLaughlin MA Beach andA RNix ldquoA novel wideband dynamic directionalindoor channel model based on a Markov processrdquo IEEETransactions on Wireless Communications vol 4 no 4 pp1539ndash1552 2005

[31] I Sen and D Matolak ldquoVehicle channel models for the 5-ghzbandrdquo IEEET Intell Transp vol 9 no 2 pp 235ndash245 June 2008

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RotatingMachinery

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Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

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Navigation and Observation

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wwwhindawicom Volume 2018

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Submit your manuscripts atwwwhindawicom

Page 8: Analysis of Nonstationary Characteristics for High-Speed Railway …downloads.hindawi.com/journals/wcmc/2018/1729121.pdf · 2019-07-30 · WirelessCommunicationsandMobileComputing

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Shock and Vibration

Hindawiwwwhindawicom Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwwwhindawicom Volume 2018

International Journal of

RotatingMachinery

Hindawiwwwhindawicom Volume 2018

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Navigation and Observation

International Journal of

Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom