1300 ieee transactions on parallel and · pdf filein orthogonal frequency division...

CSI-Based Indoor LocalizationKaishun Wu, Member, IEEE, Jiang Xiao, Student Member, IEEE,

Youwen Yi, Student Member, IEEE, Dihu Chen, Member, IEEE,

Xiaonan Luo, Member, IEEE, and Lionel M. Ni, Fellow, IEEE

Abstract—Indoor positioning systems have received increasing attention for supporting location-based services in indoor

environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the

distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to

the multipath effect, which contributes to most of the estimation errors in current systems. In this work, we analyze this effect across

the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers

in orthogonal frequency division multiplexing systems and propose a novel approach called FILA, which leverages the channel state

information (CSI) to build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on

commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that

the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve

the localization accuracy compared with the corresponding RSSI approach.

Index Terms—Indoor localization, channel state information, RSSI, physical layer

Ç

1 INTRODUCTION

LOCALIZATION is one of the essential modules of manymobile wireless applications. Although global position-

ing system (GPS) works extremely well for an open-airlocalization, it does not perform effectively in indoorenvironments due to the disability of GPS signals topenetrate in-building materials. Therefore, precise indoorlocalization is still a critical missing component and hasbeen gaining growing interest from a wide range ofapplications, e.g., location detection of assets in a ware-house, patient tracking inside the building of the hospital,and emergency personnel positioning in a disaster area.

A great number of researches have been done to addressthe indoor localization problem. Many range-based locali-zation protocols compute positions based on receivedsignal strength indicator (RSSI), which represents thereceived power level at the receiver. According to propaga-tion loss model [1], received signal power monotonicallydecreases with increasing distance from the source, whichis the foundation of the model-based localization. Most ofthe existing radio frequency (RF)-based indoor localization

are based on the RSSI values [1], [2], [3], [4], [5]. Morerelated work is in the supplemental file, which can be foundon the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.214. How-ever, we claim that the fundamental reasons why RSSI isnot suitable for indoor localization are from two aspects:First, RSSI is measured from the RF signal at a per packetlevel, which is difficult to obtain an accurate value.According to our measurement in a typical indoor environ-ment as shown in Fig. 1, the variance of RSSIs collectedfrom an immobile receiver in 1 minute is up to 5 dB.Second, RSSI is easily varied by the multipath effect. Intheory, it is possible to establish a model to estimate theseparation distance using the received power. In reality,however, the propagation of a RF wave is attenuated byreflection when it hits the surface of an obstacle. In additionto the line-of-sight (LOS) signal, there are possibly multiplesignals arriving at the receiver through different paths. Thismultipath effect is even more severe in indoor environ-ments where a ceiling, floor, and walls are present. As aresult, it is possible for a closer receiver to have a lowerRSSI than a more distant one. Consequently, a simplerelationship between received power and separating dis-tance cannot be established. Therefore, this time varyingand vulnerable RSSI value creates undesirable localizationerrors.

We argue that a reliable metric provided by commercialNICs to improve the accuracy of indoor localization is inneed. Such metric should be more temporal stable andprovide the capability to benefit from the multipath effect.In current widely used orthogonal frequency divisionmultiplexing (OFDM) systems, where data are modulatedon multiple subcarriers in different frequencies andtransmitted simultaneously, we have a value that estimatesthe channel in each subcarrier called channel state informa-tion (CSI). Different from RSSI, CSI is a fine-grained valuefrom the PHY layer which describes the amplitude andphase on each subcarrier in the frequency domain. Incontrast to having only one RSSI per packet, we can obtain

1300 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 7, JULY 2013

. K. Wu is with the National Engineering Research Center of Digital Life,State-Province Joint Laboratory of Digital Home Interactive Applications,School of Information Science and Technology, Sun Yat-sen University,Guangzhou 510006, China, and Guangzhou HKUST Fok Ying TungResearch Institute. E-mail: [email protected].

. J. Xiao, Y. Yi, and L.M. Ni are with the Department of Computer Scienceand Engineering, Hong Kong University of Science and Technology, HongKong. E-mail: {jxiao, ywyi, ni}@cse.ust.hk.

. D. Chen is with the School of Physics and Engineering, Sun Yat-senUniversity, Guangzhou, China. E-mail: [email protected].

. X. Luo is with the National Engineering Research Center of Digital Life,State-Province Joint Laboratory of Digital Home Interactive Applications,Sun Yat-sen University, Guangzhou, China.E-mail: [email protected].

Manuscript received 27 Dec. 2011; revised 19 June 2012; accepted 2 July 2012;published online 11 July 2012.Recommended for acceptance by J. Cao.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TPDS-2011-12-0937.Digital Object Identifier no. 10.1109/TPDS.2012.214.

1045-9219/13/$31.00 � 2013 IEEE Published by the IEEE Computer Society

multiple CSIs at one time. More importantly, the CSIs overmultisubcarriers will travel along different fading orscattering paths on account of the multipath effects. It thennaturally brings in the frequency diversity attribute of CSI,which each subcarrier has different amplitudes and phases.By exploiting the frequency diversity, we can construct aunique “fingerprinting” indicating each location on theradio map. According to these advantages, it is favorable toleverage the CSI to improve the performance of indoorlocation fingerprinting. And thus, designing a precisetracking/localization system becomes possible.

Based on CSI, in this paper, we present the design andimplementation of FILA, a novel cross-layer approachbased on OFDM for indoor localization using WLANs.

In summary, the main contributions of this paper are asfollows:

1. We design FILA, a cross-layer approach thatenables fine-grained indoor localization in WLANs.FILA includes two parts, the first one is CSI-basedpropagation model and the second one is CSI-based fingerprinting. To the best of our knowledge,FILA is the first to use fine-gained PHY layerinformation (CSI) in OFDM to build a propagationmodel so as to improve indoor localization perfor-mance. And it is also the first time to take advanceof the combination of the fine-grained PHY layerinformation CSI with frequency diversity andmultiple antennas with spatial diversity for indoorlocation fingerprinting.

2. We implement FILA in commercial 802.11 NICsand conduct extensive experiments in severaltypical indoor environments to show the feasibilityof our design.

3. Experimental results demonstrate that FILA sig-nificantly improves the localization accuracy ascompared to the corresponding traditional RSSI-based approach.

The rest of this paper is organized as follows. In Section. 2,we introduce some preliminaries. This is followed by thesystem architecture design in Section 3. In Section 4, wedemonstrate the CSI-based propagation model. In Section 5,we illustrate the methodology of CSI-based fingerprinting.The implementation of FILA and experimental evaluationsare presented in Section 6. Finally, conclusions are presentedand suggestions are made for future research in Section 7.

2 PRELIMINARIES

In this section, we introduce the CSI value which is thefoundation of FILA design. And the background informa-tion of the OFDM system can be found in supplemental file,available online.

2.1 CSI

Based on OFDM, channel measurement at the subcarrierlevel becomes available. Nowadays, adaptive transmissionsystems in wireless communication always improve thethroughput by utilizing some knowledge of the channelstate to adapt or allocate transmitter resources [6].

CSI or channel status information is information thatestimates the channel by representing the channel proper-ties of a communication link. More specifically, CSIdescribes how a signal propagates from the transmitter(s)to the receiver(s) and reveals the combined effect of, forinstance, scattering, fading, and power decay with distance.In summary, the accuracy of CSI greatly influences theoverall OFDM system performance. It is worth pointing outthat according to the definition of CSI, only OFDM-basedWLAN systems can demonstrate the frequency diversity inCSI since they use multiple subcarriers for data transmis-sion. In another word, other modulation schemes like DSSScannot provide this value.

In a narrow band flat-fading channel, the OFDM systemin the frequency domain is modeled as

y ¼ Hxþ n; ð1Þ

where y and x are the received and transmitted vectors,respectively, and H and n are the channel matrix and theadditive white Gaussian noise vector, respectively.

Thus, CSI of all subcarriers can be estimated according to(1) as

H ¼ y

x; ð2Þ

which is a fine-grained value from the PHY layer thatdescribes the channel gain from TX baseband to RX baseband.

3 SYSTEM DESIGN

In this section, we first give an overview of the systemarchitecture. Challenges in the system design are presentedin the supplemental file, available online.

3.1 System Architecture

FILA system is built based on the current communicationsystem and thus compatible to the under layer design. Moreprecisely, no modification is required at the transmitter end(TX-the AP), while only one new component for CSIprocessing is introduced for localization purposes at thereceiver end (RX-the target mobile device). Fig. 2 demon-strates the detailed design of the system architecture. Fortraditional packet transmission in wireless communication,only the demodulated signal is exported to the decoder formessage content retrieval. However, a prerequisite in FILAlocalization system is that it should be able to export the CSIvalue after the normal demodulation process. Such that wedevise a localization block to exploit the CSI information.

In our designated localization block, the CSI collectedfrom 30 groups different subcarriers will first be processed.

WU ET AL.: CSI-BASED INDOOR LOCALIZATION 1301

Fig. 1. Temporal variance of RSSI.

After running the proposed algorithm, we can obtainthe effective CSI in an efficient time constraint. Then, theeffective CSI will be used to estimate the location of thetarget object. As mentioned in the previous section, CSIvalue is the channel matrix from RX baseband to TXbaseband which is needed for channel equalization. There-fore, there is no extra processing overhead when obtainingthe CSI information. Nevertheless, RSSI is obtained at thereceiver antenna in the 2.4 GHz RF before down convert tothe IF and baseband. Therefore, the free space model thatbuilt for RSSI-based localization approaches cannot bedirectly applied to process the CSI value. We need to refinesuch radio propagation model according to the CSIinformation and compute the distance based on theproposed one. Finally, as the AP location information isobtained from the network layer while CSI is collected fromthe physical layer, we then use the simplest trilaterationmethod to obtain the location. For the fingerprinting, weleverage the CSI values including different amplitudes andphases at multiple propagation paths, known as thefrequency diversity, to uniquely manifest a location. Wethen present a probability algorithm with a correlation filterto map object to the fingerprints.

In our FILA system architecture design, CSI is onlyprocessed by a newly designed localization block if needed.Owing to the fact, FILA can be applied concurrently with theoriginal packet transmission. In other words, it will notintroduce additional overhead during the data transmission.

4 CSI-BASED PROPAGATION MODEL

In this section, we leverage the fine grained CSI valueinstead of RSSI to build a propagation model and addressthe indoor localization issue. The CSI-based propagationmodel can be built based on three following steps.

1. CSI processing: First, we need to mitigate estimationerror by effectively processing the CSI value denotedas CSIeff . This is known as the prerequisite of theongoing two steps.

2. Calibration: Afterward, we develop a refined indoorpropagation model and a fast training algorithm toderive the relationship between CSIeff and distance.

3. Location determination: By receiving the APs coordi-nates in network layer and CSIeff values fromphysical layer, we apply the revised propagationmodel and trilateration method to accomplish thelocalization. This part is in the supplemental file,available online.

4.1 CSI Processing

For wireless communication, attenuation of signal strengththrough a mobile radio channel is caused by three nearlyindependent factors: path loss, multipath fading, andshadowing. The path loss characterizes the property thatthe signal strength decays as the distance between thetransmitter and receiver increases, which is the foundationof our CSI-based localization. Multipath fading is a rapidfluctuation of the complex envelope of received signalcaused by reception of multiple copies of a transmittedsignal through multipath propagation. Shadowing repre-sents a slow variation in a received signal strength due tothe obstacles in propagation path. Therefore, before estab-lishing the relationship between CSI and distance, we needto mitigate the estimation error introduced by multipathfading and shadowing.

4.1.1 Time-Domain Multipath Mitigation

The first concentration of our design is that the system mustbe capable of dealing with the challenge of operating over amultipath propagation channel. Since multipath effect willintroduce intersymbol interference, cyclic prefix (CP) isadded to each symbol to combat the time delay in OFDMsystems. However, the CP technique is helpless for themultiple reflections within a symbol time.

For narrow-band systems, these reflections will not beresolvable by the receiver when the bandwidth is less thanthe coherence bandwidth of the channel. Fortunately, thebandwidth of 802.11n waveforms is 20 MHz (with channelbonding, the bandwidth could be 40 MHz), which providesthe capability of the receiver to resolve the differentreflections in the channel. We propose a multipath mitiga-tion mechanism that can distinguish the LOS signal or themost closed NLOS from other reflections in the expectationof reducing the distance estimation error.

The commonly used profile of multipath channel in thetime domain is described as follow:

hð�Þ ¼XLp�1

k¼0

�k�ð� � �kÞ; ð3Þ

where Lp is the number of multipath channel component.�k and �k are the amplitude and propagation delay of thekth path. In practice, OFDM technologies are efficientlyimplemented using a combination of fast Fourier Trans-form (FFT) and inverse fast Fourier Transform (IFFT)blocks. The 30 groups of CSI represent the channelresponse in frequency domain, which is about one groupper two subcarriers. With IFFT processing of the CSI, wecan obtain the channel response in the time domain, i.e.,hðtÞ. From Fig. 3, we can observe that the LOS signal andmultipath reflections come with different time delay, andgenerally the LOS signal has higher channel gain, so wecan use a trunk window with the first largest channel gain


Fig. 2. System architecture.

in the center to filter out those reflections. If LOS does notexist, we can identify the shortest path NLOS reflection.According to Nyquist sampling theorem, wider spectrumleads to higher resolution in the time domain. Due to thebandwidth limitation of WLAN, we cannot distinguish allthe reflections but we can use this method to reduce thevariance induced by multipath effects. The time duration ofthe first cluster is determined by setting the truncationthreshold as 50 percent of the first peak value. In doing so,we expect to mitigate the estimation error introduced bymultipath reflection.

After the time-domain signal processing, we reobtain theCSI using FFT. Fig. 3 shows the CSI results after time-domain filtering. Note that, commercial NICs embedshardware circuits for the FFT and IFFT processing, ouralgorithm introduces ignorable latency to the wholelocalization procedure.

4.1.2 Frequency-Domain Fading Compensation

Moreover, since CSI represents the channel responses ofmultiple subcarriers, a combination scheme is also intro-duced to process the CSI value in our system forcompensation of the fading of received signals in frequencydomain to enhance location accuracy.

In general, when the space between two subcarriers islarger than the coherence bandwidth, they are fadingindependently. Since the channel bandwidth of 802.11nsystem is larger than the coherence bandwidth in typicalindoor environment, the fading across all subcarriers arefrequency selective. To combat such frequency selectivelyfading of wireless signals, multiple uncorrelated fadingsubchannels (multiple frequency subcarriers), that is 30groups of CSI values are combined at the receiver.Motivation for leveraging the frequency diversity stemsfrom the fact that the probability of simultaneous deepfading occurring on multiple uncorrelated fading envelopes(in our case, resulting from frequency diversity) is muchlower than the probability of a deep fade occurring on asingle-frequency system. Thus, exploiting the wide band-width of WLAN that assures sufficiently uncorrelatedsubcarriers will reduce the variance in CSIs owing to smallscale factors, which appears to be one of the major sources oflocation determination error. In our FILA system, weweighted average the 30 groups CSIs in frequency domain

so as to obtain the effective CSI, which exploits the frequencydiversity to compensate the small-scale fading effect.

Given a packet with 30 groups of subcarriers, theeffective CSI of this packet is calculated as

CSIeff ¼1

K

XKk¼1

fkf0� jHkj; k 2 ð�15; 15Þ; ð4Þ

where f0 is the central frequency, fk is the frequency of thek-th subcarrier, and jHkj is the amplitude of the kthsubcarrier CSI.

Note that selection of weighting factors are based on thefact that the radio propagation is frequency related.According to the free space model, the received signalstrength is related to the frequency the signal is transmitted.So, by this weighting method, we transfer the channel gainfrom multiple subcarriers to a single subcarrier, i.e., thecentral one. Next, we will establish the relationship betweenthe CSIeff and distance.

4.2 Calibration

Since CSI value is obtained from the baseband on thereceiver side, the radio propagation model [7] for RSSI is nolonger suitable for our design. So, we develop a refinedindoor propagation model to represent the relationshipbetween the CSIeff and distance by revising the free spacepath loss propagation model, given by

d ¼ 1

4�

c

f0 � jCSIeff j

� �2

� �" #1

n

; ð5Þ

where c is the wave velocity, � is the environment factor,and n is the path loss fading exponent. Both of the twoparameters are dependent on distinctive indoor environ-ments. The environment factor � represents the gain of thebaseband to the RF band at the transmitter side, inversely,the gain of RF band to baseband at the receiver side, and theantenna gains as well. Moreover, for NLOS AP, the � alsoincludes the power loss due to wall penetration or theshadowing. The path loss fading exponent n is varyingdepending on the environment. For instance, when the RFsignal is propagating along a free space like corridor, thepath loss fading exponent n will be around 2. In other cases,such as an office that represents a complex indoor scenario,the exponent could be larger than 4. In an indoor radiochannel with clutter in medium, where often the LOS pathis augmented with the multipath NLOS at the receiver,signal power decreases with a pass loss fading exponenthigher than 2 and is typically in the order of 2 to 4 [8].Hence, it is not trivial to determine the received signalpower and we need to refine the free space propagationmodel that obeys the analytical and empirical methods. Awidely used simplification is to assume that all the path lossexponents that model propagations between the specificreceiver and all the APs are equal. This simplification in atypical indoor environment is an oversimplification, sincethe channel propagation is usually very different dependingon the relative position of the mobile client with regard toeach AP. Therefore, we calibrate both environment factor �and the path loss fading exponent n in a per-AP manner.


Fig. 3. Time-domain channel response.

We propose a simple fast training algorithm based onsupervised learning to retrieve the parameters with threeanchors in offline phase. In the first step, CSIs of multiplepackets are collected at two of the anchors to train theenvironment factor � and the path loss fading exponent nfor the refined indoor propagation model. In the secondstep, CSIs collected at the third anchor are used to test theefficiency of the parameter estimation. The two steps runiteratively until convergence. The experimental results inthe next section show that this simple algorithm can achievesatisfactory accuracy, more sophisticated training methodwill be able to obtain better performance.

5 CSI-BASED FINGERPRINTING

In this section, we introduce the methodology of a CSI-based location fingerprinting approach. We start by using amobile device equipped with 802.11 NICs to receive thebeacon message from nearby APs at each sample position.The message contains CSI that represents the channelresponse of multiple subcarriers. We modify the driverand divide the CSIs into 30 groups. Hence, N ¼ 30 groupsCSI values are collected simultaneously at the receiver thatrepresented as

H ¼ ½H1; H2; . . . ; Hi; . . . ; HN �T ; i 2 ½1; 30�; ð6Þ

where each subcarrier Hi is defined as

Hi ¼ jHijej sinfffHig; ð7Þ

where jHij is the amplitude response and ffH is the phraseresponse of the ith subcarrier.

Then, it comes to CSI processing which is the prerequi-site of calculating position likelihood in the positioningphase. We quantify the power of a package, denoted assummational CSI, by adding up the power with respect to30 groups of subcarriers. Specifically,

He ¼XIi¼1

jHij2; i 2 ½1; 30�; ð8Þ

5.1 CSI-Based Fingerprinting Generation

As the foundation of fingerprinting approach, the measuredCSI values are processed to construct a radio map. Sincemost of the RF-based fingerprint methods consider twospatial dimensions for localization [9], we also follow theprinciple. Therefore, the two-dimension physical spacecoordinate of a sample position lj is lj ¼ ðlj;x; lj;yÞ. Togenerate a radio map, we first extract the statistic determinethe number of detectable APs for a sample position. At eachreference point, we will estimate the signal strengthdistribution for each access point at each location. In ourlocation system, the signal strength over all the subcarriersis represented by He.

Moreover, another component of the radio map will benormalized CSI values at each subcarrier for each AP. Themotivation of leveraging the frequency diversity stems fromthe fact that CSI benefits from the multipath effects, becausethe received signal at different positions will be the combina-tion of different reflections. Therefore, these normalized CSI

values will reflect the combination result ignoring the large-scale fading.

5.2 Positioning Phase

For object location estimation, the target is required to beaccurately mapped to the radio map.

Previous works show that the probabilistic approachessuch as maximum likelihood provide more accurate resultsthan deterministic ones do in indoor environments [9].Therefore, we adapt the probability model in [10] exceptthat we use He instead of RSS value. Similarly, we treat He

observed from the AP to the receiver at a fixed location as aGaussian variable. In the proposed system, we will select Kbest APs to calculate the probability of the MS at eachreference point. The criteria for the best AP selection is thatthose APs with highest He values, because they are morereliable. In our experiment, we fix the K to be 3.

The selected K He values obtained by the terminal to belocated form a vector IHe ¼ ½He;1; . . . ;He;K �. Then, theposition estimation problem is equivalent to finding the l

that maximizes the posteriori probability P ðljjIHeÞ. Accord-ing to Bayes’ law,

P ðljjIHeÞ ¼P ðljÞP ðIHejljÞPi P ðliÞP ðIHejliÞ

¼ P ðljÞP ðIHejljÞP ðIHeÞ

: ð9Þ

Note that P ðljÞ is the prior probability that the terminallocated at the reference point li. In [9], [10], uniformdistribution is assumed. In contrast, we will leverage thespatial correlation of the CSI to determine the P ðljÞ.

Recall that CSI is a fine-grained information, we canobserve channel response over multiple subcarriers repre-sented by Hk ¼ ½H1; H2; . . . ; Hi; . . . ; HN �T for the kth AP. Wedenote the observed CSI with normalization for each AP asHðOÞ 2 jCN�K , and the CSI recorded in the radio map forthe same set of APs at position lj as HðljÞ. To quantify thesimilarity of the observed CSI and the stored “fingerprints”for all the APs, we use the Pearson correlation betweenthem which is defined as

�HðOÞ;HðljÞ ¼YKk¼1

covðHkðOÞ;HkðljÞÞ�HkðOÞ�HkðljÞ

; ð10Þ

where each AP is considered to be independent. Accordingto the measurement, the spatial channel correlation willdecrease as the distance between the two receiver increases.Therefore, with higher �, the position of the terminal will becloser to the reference point. Then, the probability of theterminal on each candidate point is defined as

P ðljÞ ¼�HðOÞ;HðljÞPJi¼1 �HðOÞ;HðliÞ

; ð11Þ

where J is the size of the candidate reference points set.Considering uncorrelated property between each AP, the

likelihood P ðIHejljÞ can be calculated as

P ðIHejljÞ ¼YKk¼1

P ðHe;kjljÞ: ð12Þ

Since the signal strength at each reference point ismodeled as a Gaussian variable which requires less


samplings than the histogram approach [11]. At the offlinephase, we can obtain the expectation �He;k and variance �e;kcorresponding to the He;k, and the P ðHe;kjljÞ is obtained as

P ðHe;kjljÞ ¼1ffiffiffiffiffiffiffiffiffiffiffiffiffi

2��e;kp exp

�ðHe;k � �He;kÞ2

2�e;k: ð13Þ

The location estimation of the terminal is the weightedaverage over the whole candidate set

l ¼XJj

�P ðljjIHeÞlj: ð14Þ

For the fingerprinting method, the terminal can processCSI by itself and then check the globe or local database forlocalization. It does not need to rely on one pubic server.

The performance of the proposed fingerprinting metho-dology is evaluated in the following section.

6 EXPERIMENTAL RESULTS

In this section, we present the implementation andexperimental evaluation of FILA. First, we describe theexperimental setup which can be found in the supplementalfile, available online. Then, we illustrate the validationresults for our refined propagation model. Finally, weevaluate the performance of CSI-based propagation modeland its fingerprinting. In our evaluation, we use theperformance of corresponding RSSI-based approach basedon radio propagation model and trilateration as baseline.

Experimental Scenarios. We conduct experiments to showthe performance and robustness of our FILA system in fourdifferent scenarios in the campus of Hong Kong Universityof Science and Technology as follows:

1. Chamber: First, we set up a testbed in a 3 m� 4 mChamber to collect the RSSI and CSI as shown inFig. 4. In general, chamber is an enclosure that usedas environmental conditions for conducting testingof specimen. In our experiment, chamber representsthe ideal free space indoor environment whichmeans only LOS signal exists without other multi-path reflection or external interference.

2. Research laboratory: Then, we deployed FILA in anidentical indoor scenario—a 5 m� 8 m researchlaboratory as shown in Fig. 5. In the laboratoryregion, we place three APs on the top of threeshelters in three dimensions. The experiment wasconducted on a weekday afternoon when there werea couple of students sitting or walking around,

which will show the robustness of our system totemporal dynamics of the environment. The laptopwas placed at a fixed position at the beginning of theexperiment and then moved to the next point along apremeasured path.

3. Lecture theatre: In addition, we chose a larger lecturehall to conduct the localization experiments, whichis a 20 m� 20 m lecture theatre. Since the space isrelatively large, the influence of the room size canbe explored.

4. Corridor: Finally, we performed experiments in acorridor environment with multiple offices aside inour academic building, which is 32:5 m� 10 mcovering corridors, rooms, and cubicles. In thisscenario, we expect to illustrate the impact of theabsence of LOS APs on the location accuracy.

6.1 Validate the Refined Model

As the target for precise indoor localization, two mostimportant metrics are used to testify FILA: the accuracy andthe temporal stability of location estimation. Afterward, wecompared the performance of our CSI-based localizationsystem with the corresponding RSSI-based approach.

6.1.1 Robustness of the Refined Model

One essential aspect that needs to be determined before thelocalization experiments is whether CSI value can build arelationship with distance. In general, indoor RF signalstrength is a nonmonotonic function with distance due tomultipath and shadowing effects. Fig. 6 illustrates the CSIvalue approximated by a power function of distanceaccording to our refined propagation model. In diversescenarios with corresponding environment factor �, thepath loss fading exponent n varies in a range of ½2; 4�. It isshown that our refined model properly fits the relationshipbetween CSIeff and distance.

6.1.2 Temporal Stability of CSI

Temporal stability is a fundamental criteria in validatingthe robustness of the localization systems. We thus set outto examine the stability of the proposed new metric CSIeffand RSSI value in time series. It is well known that RSSI is afickle measurement of the channel gain because of its coarsepacket-level estimation and easily varied by multipatheffect. As CSI is fine-grained PHY layer information thatprovides detailed CSI in subcarrier level, it is of greatimportance to figure out whether it will remain in a stablemanner in practical environment.

Figs. 7 and 8 illustrate both the interactions of CSIeff andRSSI values on temporal variance, respectively. It is shown


Fig. 4. Chamber. Fig. 5. Research laboratory.

that the received signal power calculated by RSSI has avariance up to 5 dBm, while the variance of CSIeff is within1 dBm. Therefore, the proposed new matric is much morestable in time domain compared with RSSI.

In Fig. 9, we further investigate the CSIeff value and RSSIvalue in the chamber and research laboratory so as todiscover the effect of any temporal instability on distanceestimation. Chamber provides a free space-like environmentas it uses specific material that can absorb the non-LOSsignals. Thus, multipath effect can be eliminated in chamberenvironment. However, the result from our experimentshows that even in chamber the RSSI is also variedsignificantly from time to time due to the inaccuratemeasurement. In contrast, research lab is a typical multipathenvironment. Both the static obstacles and dynamic walkingaround individuals exert the influence on multipath andbring in more intense path loss. In this way, the variance ofRSSI becomes even larger and the performance of distanceestimation is even worse.

Figs. 7, 8, and 9 lead to an essential conclusion: incomparison with RSSI, CSI is more temporally stable indifferent environments and helps maintain the perfor-mance over time. Therefore, FILA can achieve accuratelocation more quickly than the RSSI-based scheme, whichis very crucial for some location-based application likesearch and rescue.

6.2 Performance Evaluation of CSI-BasedPropagation Model

As a target for precise and fast indoor localization, two mostimportant metrics are used to testify this model: theaccuracy and the latency of localization, afterward, wecompared the performance of our CSI-based localizationsystem with the corresponding RSSI-based approach byusing the propagation model.

6.2.1 Accuracy

Accuracy over a single link. As the premise of indoorlocalization, we first investigated the distance determina-tion accuracy of FILA compared with the correspondingRSSI-based approach. The primary source of error in indoorlocalization is multipath propagation caused by multiplereflections that overlap with the direct LOS subcarrier at thereceiver. FILA takes advantage of the fine-grained trait tomitigate such multipath effect, and exploits the frequencydiversity to compensate the frequency-selective shading.We repeated the distance measurement experiments across10 different locations in chamber, research laboratory, andlecture theatre, respectively. For some positions withserious multipath effect, FILA achieves up to 10 timesaccuracy gain over the corresponding RSSI-based scheme.Fig. 10 illustrates the mean distance errors in three differentenvironments. Our evaluation shows that FILA can outper-form the corresponding RSSI-based scheme by around threetimes for the distance determination of a single link.

To assess the effectiveness of the CSI-based localizationapproach, in the following we evaluate the accuracy of FILAin different typical indoor environments.

Localization accuracy in single room. In the experimentsconducted in the research laboratory, we fix three APs onthe top of the shelters. The mobile laptop with iwl5300 NICsis first fixed at one location and then moved to another. Werepeated this process and placed the device at 10 differentpositions, respectively. Fig. 11c illustrates the cumulativedistribution (CDF) of localization errors across the 10positions. In our experiments, for over 90 percent of datapoints, the localization error falls within the range of1 meter, and the 50 percent accuracy is less than 0.5 m. Insuch a dynamic environment with lots of factors interfering


Fig. 6. Relation between CSIeff and distance.

Fig. 7. CSIeff on temporal variance.

Fig. 8. RSSI on temporal variance.

Fig. 9. CSI versus RSSI on distance estimation in different environments.

the propagation of signals, FILA exhibits a preferableproperty indicating that the fine-grained nature of CSI isbeneficial to improve the accuracy of corresponding RF-based approach.

Fig. 11b depicts the CDF function errors per locationdetected at the university lecture theatre. Even for such amuch larger space, FILA can locate objects in the range within1.8 meters of their actual position with 90 percent probability,which is acceptable for most location-based applications.

Across the above two typical single room indoorscenarios, FILA achieves median accuracy of 0.45 and1.2 m, respectively. It is therefore safe to conclude that theproposed CSI-based scheme performs much better thanRSSI-based one when locating objects in a typical indoorbuilding with multipath effect.

Localization accuracy in multiple rooms. In our previousexperiments, the APs and client are placed in the sameroom. We also examined the corridor scenario whereseveral APs are deployed in the multiple rooms. Specially,we take into consideration both the complicated multipatheffect and the shadowing fading brought by wall shield. Wefirst fix the position of the object at some reference nodes,and collects the AP coordinate and CSI value for offlinetraining. Then, we move the object to arbitrary positions foronline tracking. The moving speed is around 1 m=s and wecollect 20 CSIs and RSSIs at each position.

In Fig. 11c, we plot the CDF of location errors across 10positions. It is shown that multipath propagation doesdegrade the accuracy of object localization as well as theshadowing in the multiple rooms scenarios. However,FILA is robust enough to maintain the degradation. Moreimportantly, FILA can achieve median accuracy of 1.2 m in

this corridor environment. This result indicates that FILAis able to effectively estimate and compensate for gaindifferences across multiple rooms.

6.2.2 Latency of CSI-Based Propagation Model

Localization

Two main phases contribute to the latency of FILA, thecalibration phase and location determination phase. Sincethe environment factor and the fading exponent vary indifferent environments, we need to conduct calibration totrain these two parameters for the refined propagationmodel. We should collect CSIs at some preknown positionsto calculate these two parameters using our fast trainingalgorithm. Actually, this process can be finished beforelocalization as an offline task since the APs can use eachother’s information for the calibration. In our FILA system,the AP takes about 0.8 ms to transmit a packet with100 bytes beacon message in IEEE 802.11n. Each time wecollect 20 CSIs and the time will be 0:8� 20 ¼ 16 ms. Thecalibration process can be done within 2 ms according toour measurement on a HP laptop with 2.4 GHz dual-coreCPU. In the location determination phase, the IFFT and FFTprocesses can leverage the according hardware blocks in thewireless NICs whose running time is ignorable. While weconduct these signal processing on laptop consumingaround 2 ms, including the time needed for the trilaterationlocation calculation. Therefore, the time consumption forboth training and location determination is within severalms. In our experiment, the walking speed of user is around1 m/s. The average tracking error is around 1.2 m as shownin Fig. 11c. For multiple rooms environment, the simplealpha-tracking algorithm [12] can be applied for triggeringthe training. We only need to train these parameters onceunless the environment changes greatly. In summary, oursystem can reach the average time tracking latency to as fastas about 0.01 s, which significantly outperforms previousRSSI-based tracking systems [4] (usually 2-3 s).

6.3 Performance Evaluation of CSI-BasedFingerprinting

6.3.1 Accuracy

First, we evaluate the accuracy of the proposed CSI-basedprobability algorithm and compared with Horus, thewidely used RSSI-based fingerprinting system. The meandistance error in the two different environments is shown inFig. 12. Our evaluation shows that CSI-based method can


Fig. 10. Mean distance error.

Fig. 11. CDF of localization error in different indoor environments.

achieve the median accuracy of 0.65 m, which outperformsHorus by about 0.2 m, and the gain is about 24 percent.Moreover, in the corridor scenario, where covered by sixAPs and three APs were taken into computation, the meanaccuracy of our approach is 1.07 m which is 0.35 m lowerthan Horus system, about 25 percent gain over Horus.

In addition, we compare the two approaches concerningdifferent numbers of APs in corridor. Fig. 14 depicts theaverage accuracy according to the amount of APs varyingfrom 1 to 6. Since richer information to estimate the locationcan be obtained from the more APs, both lines demonstratethe accuracy improvement. In particular, our approachreduced the mean distance error by 29 percent on theaverage. Obviously, these results show the effectiveness ofthe proposed CSI-based location system and indicate thebenefits from indoor environment with dense-deployedAPs. When the AP is sparse, our scheme performs muchbetter than the RSS-based one.

6.3.2 Precision

Fig. 15 illustrates the CDF of localization errors in thelaboratory. The data were collected across the 28 positionsin the laboratory. From Fig. 15, we can observe that theconfidence interval with confidence level 90 percent for theerror is (�1:3 m, þ1:3 m), which means the CSI-basedlocalization error falls within the range of 1.3 meters, andthe 50 percent accuracy is less than 0.6 m. However, theHorus can locate objects in the range within 1.6 meters oftheir actual position with 90 percent probability, and themedian accuracy is 0.8.

Unlike the first scenario that three APs and client areplaced in the same room, we also examined the corridortestbed where the six APs are deployed in the multiple

rooms. Fig. 13 depicts the CDF of positioning errors across20 positions. We can easily observe that both ourapproach and Horus can achieve the median accuracyless than 1.25 m. However, the accuracy improvement ofour approach over Horus for 90 percent of data points is0.55 m. We can conclude that our approach exhibits apreferable property since the fine-grained and frequencydiversity nature of CSI is beneficial to improve theprecision of location fingerprinting.

7 CONCLUSIONS AND FUTURE WORK

Localization is one of the most appealing applications andbecomes increasingly common in our daily life. RSSI-basedschemes have been widely used to provide location-awareservices in WLAN. However, in this paper, we observe thatRSSI is roughly measured and easily affected by themultipath effect which is unreliable. We then use the fine-grained information, that is, CSI, which explores thefrequency diversity characteristic in OFDM systems tobuild the indoor localization system FILA. In FILA, weprocess the CSI of multiple subcarriers in a single packet aseffective CSI value CSIeff , and develop a refined indoorradio propagation model to represent the relationshipbetween CSIeff and distance. Based on the CSIeff , wethen design a new fingerprinting method that leverages thefrequency diversity. To demonstrate the effectiveness ofFILA, we implemented it on the commercial 802.11n NICs.We then conducted extensive experiments in typical indoorenvironments and the experimental results show that theaccuracy and speed of distance calculation can be signifi-cantly enhanced by using CSI.

In this work, we just use the simplest trilateration methodto illustrate the effectiveness of CSI in indoor localization.


Fig. 12. Mean distance error.

Fig. 13. CDF of localization error in corridor. Fig. 15. CDF of localization error in laboratory.

Fig. 14. Mean distance error with different numbers of APs.

The future research in the new and largely open areas ofwireless technologies can be carried out along the followingdirections. First, we can leverage the available multiple APsto improve the location accuracy in some extent. Second, inthis paper, we only leverage the frequency diversity,however, the spatial diversity can also be exploited in MIMOto enhance the indoor localization performance. Third, sincesome of the smartphones have 802.11n chipset, the next stepof our work is to implement FILA in smartphone.

ACKNOWLEDGMENTS

This research is supported in part by Guangdong NaturalScience Funds for Distinguished Young Scholar (No.S20120011468), Guangzhou Pearl River New Star Technol-ogy Training Project (No. 2012J2200081), Guangdong NSFGrant (No. S2012010010427), China NSFC Grant 61202454,Hong Kong RGC Grants HKUST617811, 617212, S&TProject of Guangdong Province, China Grant No.2011A011302001, NSFC Grant Nos. 60933011 and61027009, NSFC-Guangdong Joint Fund (U0835004), theNational Key Technology R&D Program (2011BAH27B01,2011BHA16B08), the Project Science and Technology ofGuangdong Province (2011168014 and 2011912004) and theMajor Science and Technology Projects of Guangdong(2011A080401007). Kaishun Wu is the correspondingauthor.

REFERENCES

[1] P. Bahl and V.N. Padmanabhan, “Radar: An In-building RF-BasedUser Location and Tracking System,” Proc. IEEE INFOCOM, 2000.

[2] J. Liu, Y. Zhang, and F. Zhao, “Robust Distributed NodeLocalization with Error Management,” Proc. ACM MobiHoc, 2006.

[3] D. Moore, J. Leonard, D. Rus, and S. Teller, “Robust DistributedNetwork Localization with Noisy Range Measurements,” Proc.ACM Int’l Conf. Embedded Networked Sensor Systems (Sensys), 2004.

[4] D. Zhang, J. Ma, Q.B. Chen, and L.M. Ni, “An Rf-Based System forTracking Transceiver-Free Objects,” Proc. Fifth IEEE Int’l Conf.Pervasive Computing and Comm. (PerCom), 2007.

[5] D. Zhang and L.M. Ni, “Dynamic Clustering for TrackingMultiple Transceiver-Free Objects,” Proc. IEEE Int’l Conf. PervasiveComputing and Comm. (PerCom), 2009.

[6] D. Halperin, W.J. Hu, A. Sheth, and D. Wetherall, “Predictable802.11 Packet Delivery from Wireless Channel Measurements,”Proc. ACM SIGCOMM, 2010.

[7] A. Goldsmith, Wireless Communications and Networks: 3G andBeyond. McGraw Hill, 2010.

[8] K. Fazal and S. Kaiser, Multi-Carrier and Spread Spectrum Systems:From OFDM and MC-CDMA to LTE and WiMAX. Wiley, 2008.

[9] M. Youssef and A. Agrawala, “The Horus Wlan LocationDetermination System,” Proc. Third Int’l Conf. Mobile Systems,Applications, and Services (MobiSys), pp. 205-218, 2005.

[10] S. Fang, T. Lin, and K. Lee, “A Novel Algorithm for MultipathFingerprinting in Indoor WLAN Environments,” IEEE Trans.Wireless Comm., vol. 7, no. 9, pp. 3579-3588, Sept. 2008.

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[12] T. He, S. Krishnamurthy, T. Luo, T. Yan, L. Gu, R. Stoleru, G.Zhou, Q. Cao, P. Vicaire, J.A. Stankovic, T.F. Abdelzaher, B.Krogh, and J. Hui, “VigilNet: An Integrated Sensor NetworkSystem for Energy-Efficient Surveillance,” ACM Trans. SensorNetworks, vol. 2, pp. 1-38, 2006.

Kaishun Wu received the PhD degree incomputer science and engineering from theHong Kong University of Science and Technol-ogy in 2011. He is currently a research assistantprofessor in Fok Ying Tung Graduate School,Hong Kong University of Science and Technol-ogy. His research interests include wirelesscommunication, mobile computing, wirelesssensor networks and data center networks. Heis a member of the IEEE.

Jiang Xiao is currently working toward the PhDdegree at the Hong Kong University of Scienceand Technology. Her research interests mainlyfocused on wireless indoor localization systems,wireless sensor networks, and data center net-works. She is a student member of the IEEE.

Youwen Yi received the BSc (Hons) degreefrom the Harbin Institute of Technology in 2007,and the MSc degree from the HuazhongUniversity of Science and Technology in2009. From 2009 and 2011, he was a researchassistant at the Hong Kong University ofScience and Technology. He is currently asenior research engineer at Huawei Technolo-gies. His research interests include real-timeindoor localization system, and next-generation

self-organized network. He is a student member of the IEEE.

Dihu Chen received the BSc and MPhil degreesin semiconductor physics from Sichuan Univer-sity in 1986 and 1989, respectively, and the PhDdegree in solid-state electron from the Depart-ment of Electronic Engineering, The ChineseUniversity of Hong Kong in December 2000. He iscurrently a professor and the head of thedepartment and ASIC Design Center of SunYat-sen University. He current research interestsinclude electronic devices, IC design and design

methodology. He is a member of the IEEE.

Xiaonan Luo is a professor in the School ofInformation Science and Technology and theDirector of the Computer Application Institute,Sun Yat-sen University, China. His researchinterests include mobile computing, computergraphics and CAD. He is a member of the IEEE.

Lionel M. Ni is a chair professor in the Depart-ment of Computer Science and Engineering,Hong Kong University of Science and Technology(HKUST). He also serves as the special assistantto the president of HKUST, dean of HKUST FokYing Tung Graduate School and visiting chairprofessor of Shanghai Key Lab of ScalableComputing and Systems at Shanghai Jiao TongUniversity. He has chaired more than 30 profes-sional conferences. He is a fellow of the IEEE.


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