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Interference-Based Cell Selectionin Heterogenous Networks

Kemal Davaslioglu and Ender AyanogluCenter for Pervasive Communications and Computing

Department of Electrical Engineering and Computer Science, University of California, Irvine

Abstract—Heterogeneous cellular networks provide significantimprovements in terms of increased data rates and cell coverage,and offer reduced user rate starvation. However, there areimportant problems to be solved. In this paper, we identify thatthe cell selection criterion is an important factor determining theuser rates especially in the uplink transmissions and applycellbreathing to determine the user and base station assignments.We observe that the proposed interference-based cell selectionalgorithm provides better load balancing among the base stationsin the system to improve the uplink user rates. We present theimplementation steps in a typical LTE network and demonstratethe performance improvements through simulations.

I. I NTRODUCTION

Next generation cellular systems such as Long-Term Evo-lution (LTE) target significantly increased throughput andcapacity requirements to answer the rapidly increasing userdata demand. For instance, LTE systems require a peak datarate of100 Mbps for downlink and50 Mbps for uplink in a20MHz bandwidth with 64 quadrature amplitude modulation [1].Antenna-based improvements such as employing multiple an-tennas at both base stations and user equipments (UEs), trans-mit beamforming, and spectrum-based improvements such ascarrier aggregation (CA) are a few of the enabling technologiesin LTE systems to achieve these challenging rates [1]. How-ever, these will not suffice. It is obvious that, in the near future,the deployment of small cells will create a network topologyshift in order to achieve significant gains that the operatorsneed to consider along with the enabling technologies.

The deployment of different low-cost low-power base sta-tion nodes (LPN) such as picocells, femtocells and relays willprovide the opportunities to increase the capacity within themacrocell area and avoid coverage holes by adapting to thevarying nature of user traffic demand. According to a recentstudy by Ericsson, each macrocell will be overlaid with anaverage of three LPNs by 2017 to meet the demand for thecoverage, mobility and thereby, the improved user experience[2]. However, to fully exploit the possible gains throughheterogeneous network (HetNet) deployments, we need toconsider the differences in base station types and change theconventional single-layer homogenous networks approach toinclude these differences. Network planning in HetNets suchas LTE systems differs from conventional network planning inseveral aspects.

In conventional single-layer networks, base station selectionwas based on the highest reference signal received power

(RSRP) measured at UE. While this gives the optimum selec-tion methodology for these networks, it does not always applyto the HetNets where base stations have different transmitpowers. Macrocell and picocell base stations, namely MeNBsand pico-eNBs, differ by almost16 dB in their downlinktransmit power levels [3]. If the cell selection is based onRSRP only, UEs are more likely to connect to the MeNBseven when the path loss conditions between the pico-eNB andthe UE are better. If the optimal cell selection were assignedto this case, UE could reduce its transmission power since ithas higher uplink (UL) signal to interference plus noise ratio(SINR) at the closer pico-eNB, which will consequently leadto a longer battery life for the UE and reduce the interferencein the system. Also, this would lead to a more balanced loadingwithin the macrocell footprint where the resources in MeNBsand LPNs are better utilized.

In this paper, we seek to find the user-base station as-signments that maximize the uplink throughput in a densebase station deployment. For this purpose, we apply thecell breathing results derived in [4]–[6] to the current LTEstructure. We introduce an adaptive cell selection frameworkthat enables the network to balance the traffic load betweenthe macrocell and the picocell base stations. Our formulationenables us to exploit base station diversity to improve systemperformance. We compare the performance results of theproposed cell selection method to the other conventional cellselection criteria such as reference signal received power-(RSRP), cell range extension- (CRE), and path loss-based(PL) cell selection through simulations. We show that theconventional cell selection critera are not sufficient to adaptto the interference conditions when base stations are denselydeployed. We observe that the interference-based cell selectioncriterion is better suited to reduce user rate starvation andimprove median user rates in HetNet deployments with highnetwork traffic by providing better user-base station assign-ments. It can provide a moderate rate increase compared tothe CRE- and PL-based cell selection scheme but providesmore than twice the SINR for the cell-edge users and50%improvement for the median users when compared to theRSRP-based cell selection scheme. Related works in literatureinclude [7] that also incorporates cell breathing to a macrocell-femtocell network with a frequency reuse of three. Anotherrelated work is [8] where similar results comparing RSRP-and CRE-based cell selection are shown. This work differsfrom [8] in that we employ interference-based cell selection,

and the distinction between the work in [7] and here will bepresented in the sequel.

II. SYSTEM BASICS

In this section, we introduce the system basics and thenomenclature used in this paper. We will briefly introduce thetransmission schemes and the uplink power control proposedin LTE standards. Uplink transmissions in LTE are designedto be highly power-efficient to improve the coverage and toreduce the power consumption at UE [9]. For this purpose,single carrier frequency-division multiple access (SC-FDMA)is adopted [1]. SC-FDMA enables a smaller peak-to-averagepower ratio compared to the regular orthogonal frequencydivision multiplexing access (OFDMA) [10]. In this paper,we use localized FDMA where the user outputs are mappedto consecutive subcarriers as defined in [1].

We consider a system withK users andB base stations,and let ck denote the base station that userk is associatedwith. We form anK×1 vectorc to represent all the user-basestation assignments in the system. The LTE specification in[11] defines the closed-loop uplink power control as

Pk = min{Pmax, P0 + 10 log10(NRBk) + αPLk,ck (1)

+∆TFk+ fk}

wherePk denotes the uplink transmit power of userk andrepresented in dBm units above,Pmax denotes the maximumUE transmit power, andP0 is open loop transmit power.NRBk

represents the number of resource blocks (RBs) thatare assigned to userk, andPLk,ck is the path loss betweenuser k and its serving base stationck. Note that the pathloss also includes the shadow fading of the link. The pathloss compensation factorα takes its value from the followingset,α ∈ {0, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. ∆TFi

is a parameterbased on the modulation and coding scheme andfi is a closedloop power control adjustment parameter. It is important toemphasize that path loss compensation factorα determinesthe fairness within the cell such that for a full path losscompensation,α = 1, the network enables the cell edge users,that have high path loss values, to transmit at high powerlevels. The fairness in the system decreases as the path losscompensation approaches zero, i.e.,α → 0 since the highpath loss for the cell-edge users are not compensated for.Consequently, this improves the rates for the cell center andmedian users due to less interference compared to the fullpath loss compensation case, i.e.,α = 1. Fig. 1 shows theeffects of path loss compensation factorα, on user SINRcumulative distribution function (c.d.f.) in a heterogenouscellular network. We note that Fig. 1 assumes the samesimulation setup and parameters presented in Section IV.

In this paper, we will investigate the performance of theusers under open loop power control. The user power, in unitsof dBm, is given by

Pk = min{Pmax, P0 + 10 log10(NRBk) + αPLk,ck} (2)

where∆TFkand fk in (1) are ignored in the system level

simulations as in [10, pp. 195]. The UE transmission power

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1

SINR (dB)

α = 1α = 0.8α = 0.6α = 0.4

Figure 1. The effects of various path loss compensation factors, varying fromfractional path loss compensation valuesα = {0.4, 0.6, 0.8} to the full pathloss compensation ofα = 1, on the c.d.f. of user SINR in a heterogenousnetwork deployment with2 picocells per sector.P0 is taken as−90 dBmand the proposed interference-based cell selection is usedfor user-base stationassignments.

is equally distributed on the allocated bandwidth. The corre-sponding UE transmit power spectral density indBm/Hz isgiven by

Pk,n = P0 + αPLk,ck (3)

wherePk,n denotes the uplink transmit power of userk onsubcarriern.

III. C ELL SELECTION TECHNIQUES

A. Conventional Cell Selection Schemes

The RSRP-based cell selection is carried out by choosingthe base station that maximizes the received power of thereference signals such that

ck = argmaxj∈B

gjk p̃j = argmaxj∈B

RSRPj (4)

whereB is the base station search space,gjk is the channelgain between base stationj and userk, and p̃j denotes theaverage power of the cell-specific downlink reference signalsfor the jth base station within the considered measurementbandwidth as they are defined in the standards [12].

The key point in our analysis is that we assume theaverage power allocated to the reference signals by MeNBs,p̃MeNBm , are larger compared to those of pico-eNBs,p̃peNB

p ,i.e., p̃MeNB

m > p̃peNBp , ∀m ∈ M, ∀p ∈ P where M and

P denote the sets of macrocell and picocell base stationssuch thatM ∪ P = B. The reason for this assumption isthat the coverage area of an MeNB,AMeNB , is significantlylarger than the coverage area of a pico-eNB,ApeNB , and inorder to provide full coverage within the cell, more powerneeds to be allocated for the MeNB reference symbols, i.e.,AMeNB > ApeNB ⇒ p̃MeNB

m > p̃peNBp , ∀m ∈ M, ∀p ∈ P .

For PL-based cell selection, userk selects the base stationwith the maximum channel gain based on its reference signal

Figure 2. Difference in coverage regions of picocells underRSRP-based,PL-based and interference-based cell selection strategies in a HetNet layout.

measurements such that

ck = argmaxj∈B

gjk. (5)

An important observation is that, based on these definitions,it is straightforward to see that the coverage area of a picocellwith PL-based cell selection is always greater than its coveragearea with RSRP-based cell selection in a HetNet layout suchthat

APLpeNBp

≥ ARSRPpeNBp

, ∀p ∈ P . (6)

We depict this result in Fig. 2. Also, note that in single layerhomogenous networks, RSRP- and PL-based cell selectionsare equivalent since base stations allocate the same powerlevels for reference signals. Although a variation of PL-basedcell selection was employed in Global System for Mobile(GSM) [13], this criterion is not included in the LTE standards.However, we include it as a reference in our paper since itpotentially provides increased uplink rates by better user-basestation assignments compared to RSRP-based cell selection.

The third cell selection method we investigate is to use thecell range expansion to extend the picocell coverage with aconstant offset to offload macrocell users to picocells. Similarto the RSRP-based cell selection, this criterion also uses theRSRP measurements at the UE for cell selection. CRE-basedcell selection is expressed as

ck = argmaxj∈B

RSRPdBj + BiasdBj (7)

where the above terms are expressed in dB,Biasj = 0 forall macrocell base stations,m ∈ M, and typical values forpicocell base station offset values are3, 6, 9, and 12 dB.When we consider a userk, a macrocell base stationm ∈ Mand a picocell base stationp ∈ P , userk chooses the picocellbase stationp with a Biasp under CRE-based cell selection ifthe following holds true

RSRPdBm < RSRPdB

p + BiasdBp (8)

where a large offsetBiasp increases the picocell coverage.Although this may offer increased rates in the uplink, theopposite is true for the downlink user rates. Especially, theusers in the range expansion area are exposed to severe cross-tier interference from macrocell base stations in the downlink.Therefore, the offset values and consequent picocell coverageregions need to be carefully adjusted such that the imbalancein the downlink and the uplink user rates can be mitigated.

The main drawback of these three cell selection schemes is

that they solely depend on path loss and downlink transmitpowers and do not consider the instantaneous interferencein the system. In the case of high interference at a basestation either caused by increased number of interfering usersin neighboring cells or interfering users with high data ratedemand, a better approach is to hand over excess users toneighboring cells to better balance the load among the basestations. The following section introduces an adaptive cellselection criterion that mitigates this drawback.

B. Interference-Based Cell Selection

Letn be a subchannel assigned to userk ∈ U andΘk denotethe set of subcarriers within the consecutive RBs assigned touserk. The uplink interference plus noise experienced by theserving base stationS on the set of subcarriersn ∈ Θk canbe represented as

IS =∑

n∈Θk

∑

u∈U ,u6=k

pu,n gu,S + σ2S

(9)

wherepu,n denotes the transmit power of the interfering usersu ∈ U on subcarriern, and gu,S denotes the channel gainbetween the useru and the base stationS. Note that the usersthat are associated with the same base station are orthogonalin the frequency domain, and the interfering users are theusers that are allocated to the same resource blocks in theneighboring cells. On the other hand, the interference plusnoise at a candidate base stationC on the set of subcarriersin the resource blocksn ∈ Θk is given by

IC =∑

n∈Θk

(∑

u∈U

pu,n gu,C + σ2C

)(10)

where the uplink transmission of userk is also considered asan interfering link to the candidate cellC since userk is notserved by the candidate base station C. In (4), we have shownthat the downlink RSRP measurements can be used in cellselection procedure. When the uplink and downlink channelgains are symmetric, typically a valid assumption for timedomain duplexing systems, downlink reference signal (RS)broadcasts can also be used to estimate the uplink channelgains such thatgu,c ≈ gc,u. The proposed cell selectionmethod can be used in a frequency domain duplexing systembased on the sounding reference signals (SRS) transmitted byUEs to estimate the uplink channel quality at the base station.In either case, we can rewrite the cell selection rule betweenthe serving and the candidate base stations. Userk selects thecandidate base stationC if the following is true

p̃S ISRSRPS

>p̃C(IC −

∑n∈Θk

pu,ngu,C)

RSRPC

(11)

wherep̃S and p̃C denote the cell-specific downlink referencesignal broadcast from the serving and candidate cells, respec-tively, and we used the fact thatgu,C = RSRPC/p̃C to derive(11). Note that the interference caused by uplink transmissionsof user k is subtracted from the total interference on the

candidate cell in (11). When we rearrange terms, we obtain

RSRPC > RSRPS

p̃Cp̃S

(IC −

∑n∈Θk

pu,ngu,C)

IS. (12)

Notice that the above form is very similar to the CRE-basedcell selection in (8). Rather than applying a predeterminedcon-stant bias value, we can now obtain an adaptive offset for eachbase station for different resource blocks when interference-based cell selection is used. Thus, the interference-basedbiasvalue is given by

Biasj =p̃Cp̃S

(IC −

∑n∈Θk

pu,ngu,C)

IS. (13)

Note that this approach assumes that userk is scheduled thesame resource blocks in both its serving cell and its candidatecell.

Hence, to summarize our results in this section, the generalrule of interference-based cell selection for userk can beexpressed as

c∗k = argminc∈B

∑

n∈Θk

∑u∈U,u6=k pu,n gu,c + σ2

c

gk,c. (14)

We observe that the coverage area of a picocell withinterference-based cell selection depends on the interference ineach cell. It can be seen that, under heavy traffic, the coveragearea of our algorithm is smaller than both RSRP-based and PL-based cell selection criteria. A smaller cell size is desirableunder heavy traffic, such that extra traffic can be offloadedto the neighboring cells. On the other hand, the proposedcell selection method results in a larger cell size than bothRSRP-based and PL-based cell selection schemes under lightload, and it is indeed desirable to have larger cells to extendcoverage under light load.

Our proposed method, although based on [7], differs from[7] in several aspects. First, the approach in [7] employs amacrocell-femtocell layout in which macrocells are allowedto operate at a single band and femtocells can select oneof the three subbands that only one overlaps with the bandthat macrocell operates at. Hence, the model in [7] inherentlyapplies a frequency reuse of three among base stations. In ourpaper, we do not impose this constraint. Instead, we applya full bandwidth share that the macrocells and picocells canoperate at any subband within the channel bandwidth. Weconsider the interference on each resource block and adjustthe cell coverage accordingly. Thereby, our work considersamore efficient and realistic model considering LTE standards.Second remark is that we focus on the SINR and rate im-provements whereas [7] considers the power consumption andhandover probability.

IV. SIMULATION RESULTS

In this section, we present the performance results of theproposed cell selection method and compare its performanceto RSRP-, PL- and CRE-based cell selection strategies in aHetNet deployment scenario. As a measure of performance,we focus on the90th, 50th and 5th percentile users. These

x (km)

y (k

m)

Figure 3. Figure depicts the simulation layout of a HetNet deployment. Thelayout includes an idealized19 macrocells with each employing a 3 sectorantenna that is overlaid with2 pico-eNBs per sector. The simulation is carriedout for 12 active users per sector. Macro- and picocell base stations and usersare represented by squares, triangles and circles, respectively.

Table ISIMULATION PARAMETERS

Parameter Setting

UE to MeNB channel model 128.1 + 37.6 log10(d)

UE to Pico-eNB channel model 140.7 + 36.7 log10(d)

Inter-site distance 500mMinimum Pico- to Macro- distance 75mMinimum Pico- to Pico- distance 35m

Total number of Data RBs 48 RBsNumber of RBs for each user 4 RBs

Maximum UE Power 23 dBmEffective Thermal Noise Power 174 dBm/HzNoise Figure, Total Bandwidth 5dB, 10 MHz

MeNB Rx Antenna Gain 15 dBPico-eNB Rx Antenna Gain 5 dB

Antenna Horizontal Pattern,A(θ) −min(12(θ/θ3dB)2, Am)

Am, θ3dB 20 dB, 70◦

Penetration Loss 20 dBUE to MeNB Shadowing σ = 8 dB

UE to Pico-eNB Shadowing σ = 10 dB

typically correspond to cell center, median and the cell-edgeuser rates, and the latter two are also defined in standards suchas [3].

The simulation scenario we consider in this section is shownin Fig. 3. It assumes a HetNet deployment of idealized19-cellmacrocells, each employed with3-sector antennas, and theyare shown with squares. In our simulations, we investigatedtheperformance improvements achieved by moderate and densepicocell deployments. For this purpose, we first simulated tworandomly placed picocell base stations in each sector. Then,we repeated our simulations for a dense deployment of sixpicocells per sector. We initially placed one user per picocellwithin the picocell coverage of50 m and randomly placedthe remaining users within the macrocell sector. A total of12 users per each sector are investigated. The purpose of thistype of user distribution is to observe the under utilizationof picocell base station overlay and to see the improvementsoffered by the proposed cell selection method. The minimum

pico-eNB to pico-eNB and pico-eNB to MeNB distances areshown in Table I along with the other system simulationparameters. These parameters and channel models are takenfrom the proposed models in [3] for the heterogeneous systemsimulation baseline parameters. The downlink reference signalpowers are taken as̃pMeNB = 46 dBm and p̃peNB = 30dBm. We assumed a full buffer model for the users whereeach user has an infinite queue length and always has datato transmit. The wrap around technique is used to avoid edgeeffects. Also, we assume that minimum mean square error(MMSE) equalizers are employed at the base station receivers.Then, the wideband SINR for each userγk can be obtainedusing the SINR of eachNRBk

subcarriers assigned to userk,γk,n as [14]

γk =

1

1

NRBk

∑NRBk

n=1

γk,n

γk,n+1

− 1

−1

. (15)

Fig. 4 (a)-(d) displays the c.d.f. of user wideband SINRfor different (P0, α) pairs. We assumed a moderate picocelldeployment of2 picocells per sector. We see that for allthree types of users, cell center, median and cell-edge users,the proposed interference-based cell selection outperforms theconventional cell selection methods of RSRP-, CRE- and PL-based cell selection. The marks in the figure are to comparethe RSRP- and interference-based cell selection methods. Wesee that cell-edge users experience the most significant SINRimprovements of achieving more than double gains in SINRwith the proposed scheme. These gains are achieved throughadjusting to the interference conditions at each base station.For instance, if a base station has high interference from itsneighboring cells, it is often advantageous to offload some ofits users to the other cells. This type of allocation enablesthecell-edge users or the users with bad channel conditions tosignificantly improve their rates. We note here that this typeof cell selection scheme performs best when base stations haveoverlapping cells. In the case where base stations are sparselydeployed, these gains may not be achieved.

Next, we investigate a heterogenous network deploymentwith a dense small cell overlay. We simulate the previoussystem setup with6 picocells per sector. The resulting c.d.f.versus SINR results are depicted in Fig 5 (a)-(d). As a firstnote, we can see a significant SINR improvement achievedby the dense base station deployment when we compare Figs.4 and 5. We observed more than5 dB gains in SINR canbe achieved when we increase the number of picocells persector from two to six. In fact, these gains are mainly obtainedthrough finding either closer base stations or base stationswith less interference. Hence, these improvements are thedirect results of increased base station diversity. Obviously, interms of network operators, this comes with additional capitaland operational expenses for the dense picocell deployments.However, we can see that this translates into direct gains interms of SINR. Another observation we can make based onFig. 5 (a)-(d) is that the previous results for the2 picocells

per sector deployments still apply to the6 picocells per sector.With the interference-based cell selection method, the SINRoffered to the cell-edge users or users high path loss valuesare significantly improved compared to RSRP-, CRE- andPL-based cell selection methods. Likewise, the proposed cellselection method also provides increases in user SINR around30− 40% for the median users.

V. CONCLUSION

The deployment of heterogeneous base stations providessubstantial gains on the cellular network performance in termsof increased data rates, improvements in cell coverage andsignificantly reduced user outages. In order to fully utilizethe benefits from heterogenous base station deployments, adifferent approach in network planning needs to be pursuedand for this purpose, we identified the critical role of thecell selection criterion for the user-base station assignments.Conventional cell selection schemes such as RSRP-, CRE-and PL-based strategies ignore the network traffic load andoften times do not provide the optimal solution. Instead,interference-based cell selection offers more flexibilityto adaptto the varying traffic load and user mobility. Based on oursimulation results, we conclude that the proposed interference-based cell selection provides significant improvements in theuplink to double the cell-edge user SINR and increase themedian user SINR by50% when compared to the RSRP-basedcell selection criterion.

REFERENCES

[1] 3GPP, TR 25.912,Feasibility study for evolved Universal TerrestrialRadio Access (UTRA) and Universal Terrestrial Radio AccessNetwork(UTRAN),Mar. 2011.

[2] D. Gilstrap. (2012, Nov.). Ericsson mobility report; onthepulse of the networked society. Ericsson AB. [Online]. Available:http://www.ericsson.com/ericsson-mobility-report .

[3] 3GPP, TR 36.814,Further advancements for E-UTRA physical layeraspects, Mar. 2010.

[4] S. V. Hanly, “Information capacity of radio networks,” Ph.D. thesis,University of Cambridge, 1993.

[5] S. V. Hanly, “An algorithm for combined cell-site selection and powercontrol to maximize cellular spread spectrum capacity,”IEEE J. Sel.Areas Commun., vol. 13, no. 7, pp. 1332-1340, Sep. 1995.

[6] R. Yates and C. Y. Huang, “Integrated power control and base stationassignment,”IEEE Trans. Veh. Technol., vol. 44, no. 3, pp. 638-644,Aug. 1995.

[7] D. Xenakis, N. Passas, A. Radwan, J. Rodriguez and C. Verikoukis,“Energy efficient mobility management for the Macrocell FemtocellLTE Network” in Energy Efficiency - The Innovative Ways for SmartEnergy, The Future Towards Modern Utilities. InTech Publishers, 2012,ch. 3, pp. 57-78.

[8] 3GPP, R1-094882, “Importance of serving cell selectionin HetNets,Qualcomm Europe, Nov. 2009.

[9] D. Astly, E. Dahlman, A. Furuskr, Y. Jading, M. Lindstrm,and S.Parkvall, “LTE: The Evolution of Mobile Broadband,”IEEE Commun.Mag., pp. 52-59, Apr. 2009.

[10] H. Holma and A. Toskala,LTE for UMTS, OFDMA and SC-FDMABased Radio Access, Wiley & Sons, 2009.

[11] 3GPP, TS 36.213,Physical layer procedures, Jul. 2012.[12] 3GPP, TS 36.214,Physical layer measurements, Sep. 2012.[13] 3GPP, TS 03.22,Functions related to Mobile Station (MS) in idle mode

and group receive mode, Aug. 2002.[14] H. G. Myung and D. J. Goodman,Single Carrier FDMA, A New Air

Interface for Long Term Evolution. Wiley & Sons, 2008, pp. 88-91.

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Prob

( SI

NR <

Abs

icca

)5.7%

59.8%

115.9%

HetNeT, PL−Based CSHetNeT, Proposed CSHetNeT, RSRP−based CSHetNeT, RE Bias: 3 dBHetNeT, RE Bias: 6 dBHetNeT, RE Bias: 9 dBHetNeT, RE Bias: 12 dB

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HetNeT, PL−Based CSHetNeT, Proposed CSHetNeT, RSRP−based CSHetNeT, RE Bias: 3 dBHetNeT, RE Bias: 6 dBHetNeT, RE Bias: 9 dBHetNeT, RE Bias: 12 dB

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icca

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118.2%

HetNeT, PL−Based CSHetNeT, Proposed CSHetNeT, RSRP−based CSHetNeT, RE Bias: 3 dBHetNeT, RE Bias: 6 dBHetNeT, RE Bias: 9 dBHetNeT, RE Bias: 12 dB

(c)

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Prob

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NR <

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icca

)

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57.6%

119.5%

HetNeT, PL−Based CSHetNeT, Proposed CSHetNeT, RSRP−based CSHetNeT, RE Bias: 3 dBHetNeT, RE Bias: 6 dBHetNeT, RE Bias: 9 dBHetNeT, RE Bias: 12 dB

(d)

Figure 4. Each plot displays the c.d.f. of of user wideband SINR with different cell selection strategies forP0 = −90 dBm andα varying between0.4 to1 (fractional PL compensation to full PL compensation) in a HetNet deployment of2 picocells and12 users per sector. The marked percentiles of5, 50 and90th percentile represent cell-edge, median and cell center users, respectively.

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NR <

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icca

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5.4%

35.5%

167.2%

HetNeT, PL−Based CSHetNeT, Proposed CSHetNeT, RSRP−based CSHetNeT, RE Bias: 3 dBHetNeT, RE Bias: 6 dBHetNeT, RE Bias: 9 dBHetNeT, RE Bias: 12 dB

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Prob

( SI

NR <

Abs

icca

)

4.6%

38.9%

142.7%

HetNeT, PL−Based CSHetNeT, Proposed CSHetNeT, RSRP−based CSHetNeT, RE Bias: 3 dBHetNeT, RE Bias: 6 dBHetNeT, RE Bias: 9 dBHetNeT, RE Bias: 12 dB

(c)

−10 −5 0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

P0 = −90 dBm, α = 1

SINR (dB)

Prob

( SI

NR <

Abs

icca

)

6.0%

39.2%

142.1%

HetNeT, PL−Based CSHetNeT, Proposed CSHetNeT, RSRP−based CSHetNeT, RE Bias: 3 dBHetNeT, RE Bias: 6 dBHetNeT, RE Bias: 9 dBHetNeT, RE Bias: 12 dB

(d)

Figure 5. Each plot displays the c.d.f. of of user wideband SINR with different cell selection strategies forP0 = −90 dBm andα varying between0.4 to1 (fractional PL compensation to full PL compensation) in a HetNet deployment of6 picocells and12 users per sector.