modeling of mimo channels for the populated indoor environment

7/29/2019 Modeling of MIMO Channels for the Populated Indoor Environment

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MODELLING OF W O HANNELS FOR THE OPULATEDINDOORENVIRONMENTW. G. S d o n &K. Ziri-C-0

ABSTRACTThe use of M ultiple-Input Multiple-Output (Mh40)channels has recently attracted considerable interest asan approach that can yield signijjcant capacity gains m e r conventional smart antenna arrays. This pape rpresents a new channel model fo r MZUO systems operating in populated indoor emironrnen ts. The model isbased on geomeMcal optics and a detailed radar cross-section(RCP epresentation of the human bo&, andit is capable of estimaring the temporal response and the capaciry baud for MIMO channels in the presenceofpedestrian trafic. For a single room environment, the new channel model predicts an increase in capacityfrom 19.1 bitsMHz to 31.4 bitsMHz solely caused by the movement of pedesmans. The new channelmodelling technique offers an ef lcie nt solution to the performance evaluation of MIMO wireless systems inpopulated indo or environments.1. IntroductionFuture broadband wireless networks will feature multi-element arrays employed at both originating anddestination terminals. 'Ibis approach can yield significant gains fo r both link and network capacities wah noadditional bandwidth or energy consumption when compared to conventional array based diversity methods[l]. ?h e application of such Multiple-Input, Multiple-Output (MIMO) hannels has attracted considerableinterest both within academia and industry as a practical approach that could offer significant benefits forbroadband wireless applications in futuregeneration networks.When the MIMO channel is deployed in suitably rich scattering c o n b o a s such as the indoor environment, aspace-time coding architecture can be used to greatly increase the spectral efliciency of th e system. Theindoor environment is ideal for MIMO systems as multi-path propagation is almost assured. However,temporal channel variations may also occur due to the movement of personnel, or in industrial applications,vehicles and equipment. Despite various measurement campaigns and attempts at channel characterisation 12-51, the effect of pedestrian movement on MIMO performance has not been fully investigated. There istherefore a requirement for the deterministic modelling of the effect of pedestrian movement on MIMOb e l s .We present a new technique for m odelling the dynamic response of MIMO channels within populated indoorenvironments. Moving human bodies introduce temporal variations due to wave blocking (absoIption),scattering and diffraction. Our approach is based on a combination of computational electromagnetictechniques: FDTDmodelling of TX and RX arrays, image-based ray tracing and detailed radar cross-section(RCS)modelling of a realistic human body phantom.In this paper, the principles of the new channel modelling technique are described. The new technique is thenused to analyse the theoretical channel capacxty of a 2.45 GHz MIMO system within a populated indooroffice.2. Capacity of MIMO ChannelsA MIMO channel canexist when there are multiple antennas at both the transmitting and receiving termmls(see Figure 1). When these dual arrays are deployed in a suitably rich scat&ering environm ent the re isconsiderable potential for obtaininghigh spectral efficiencies, provided that a suitable spa ced me code is used[l]. If n antenna elements exist at each end of the link then it is possible to create n parallel channels betweenthe transmitterand receiver elementswith a co rresponding increase in spectral efficiency.Centre for Communicatiom Engineering,School ofElectrical&MechanicalEngineering,University of Ulster,ShoreRoad, Newtownabbey, Co. Antrim, Northern Ireland, UK,BT37 OQB.Tel: 028 90366922,Fax: 028 90368571E-mait [email protected], kziricastro@dstaeuk

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ReceiverlMOChanneltansnitterFipre 1: Multiple-Inpu t Multiple-Output channel and antenna arrays with nT=3 and nR ransmit and receiveelements, respectively.The fill channel response of a narrowband system, comprising of nT t r a n s mi t elements and nR receiveelements, can therefore be described by an n,by-nT matrix, G. he elements of the matrix G,, represent thenarrowband am plitude and phase response between each receive element i , and each t r ansm i t elementj 131.The maximum possible capacity ofth is channel is then given by:

C = og,[det(Z, + 2 H H ' ) ] bits/&,"Twhere Hi s the nR by nTcomplex channel matrix (H,, is the normalised transfer function from transmit elementj o receive element i), w a n d nR are the number of transmt and receive elem ents respectively, p is the averagesignal to noise ratio at each receiver branch, I is the identity matrix, de t is the determiuant and * is thecomplex conjugate transpose. The channel response matrix, H, is normalised to remove the path losscomponen t and only show the relative variation in the pa th respo nses between all nR-by- n7 elements 111.3. Simulation SystemIn indoor wireless environments, moving human bodies introduce temporal channel variations due to waveblocking (absorptlon), scattering and di5h ctio n. The channe l modelling technique presented here im proves oncurrent site-specific ndo or pr o pa m o n prediction by including the effect of m ultiple, moving human bodies.The new technique is a site-specific radio wave prop agation sim ulator capable of determiniisticallymodellingtemporal variations caused by the movement of personnel. The technique is based on a combination of~ ~ ~ p ~ t a t i ~ ~ llectromagnetic echniques: FDTD modelling ofTX and RX anays, image-based ray tracingand d etailed radar cross-section(RCS) odelling of a realistic human body phantom .The ray-tracing component of the model uses three-dimensional image-based geometrical optlcs to calculateindoor multipath propagation. Once all suitable paths from the tr a n s m h r to the receiver are determined bythe ray-tracing model, the electric field strength an d the phase for each single ray reaching the receiver iscalculated.The eceived pow er is obtained by the following eqr ess ion :

where P, s the transmitted power, G, nd G, are the transmitting and receiving antenna field radiationpatterns, respectively. The direction of rays are located by Band 4 polar angles, d, represents the total pathlength, ni and r h re, respectively, the num ber of r eflections and the P reflection coefficient for the i* path.Only paths with a number of reflections lower then U are considered.N s the total number of paths added atthe receiver point. For =0, equation (2) represents the con tribution of the d irect ray only, where it exists (inthe line-of-sight case). Sincer, =0, equation (2) becomes:

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Path Loss is defined as the differenc e (expressed in dB) between the transmitted power and the receivedpower and is expressed in dB:

The effectof pedestrians is determined by calculating the scattering from an RCS model of an upright humanbody in the direction of the receiver. The RCS model w a s constructed using a large number of w hole-body&&-difference time-domain (FDTD) alculations. The RCS model used in this work was or an upright adultmale phantom and the frequency considered was 2.45 GHz. A range of incid ent angles w a s considered andmore than200,000 RCS plots were generated. The use of an anatomically r ealistic body phantom introducedthe po ssibility of complex scattering and difhc tion to the model. This is an improvement on previous work[6], where the human body was represented a s a finite dielectric cylinder.In order to validate this technique, measurements of narrowband propagation were recorded across an indoorpoint-to-point link at 2.45 GHz. Figure 2 shows the both the measured and simulated temporal path lossprofiles capbxd for a 6.4 m link in a 7 by 6 m room. A pedestrian was moving perpendicular o th e link andblocked the direct ray at around 1 -s. The correlation coefficient between these two data sets is 0.805.

Fipre 2: Comparison of measured an d simulated path loss at 2.45 GHZ for a singlem m oint-to-point link witha single pedestrianmoving perpendicular to the direet ray.

4. SimulationsA MIMO link within a conventional 7 m by 6 m rectangular mom w a s considered. Both transmit and receiveantennas were uniform linear arrays composed of eight ?.I2 dipoles spaced at 0.4 h. The room height was2.75 m. The ransmit a m y was fixed at a height of 1.95m, hile the receive array w a s at 1.0 m. The locationof the arrays and a plan view oft he room are shown n Figure 3. The average path loss of all 64 ombinationsof the eig ht transmit and receive elements w a s obtained by recording a vector snapshot of the channel at eachreceiver for each transmit element in turn. The results were simulated using with a narrowband signal at2.45GH z and the RCS model of the human body described in the previous section. During the simulations,one MIMO napshot w a s ecorded forevery 10 ms witha total simulation period of 12 s.

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(a) @)Figure 4: Channel responses for a single pedestrian walking at 0.5 d s erpendicular to the Link; a) temporalpath loss profile for TX 2 to RX 4 and TX 2 to RX 3; b) dpamic channel capacity for the 8x8 MIMO

channel (SNR=16 dB).

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Strong fiding w a s observed in the region where the body obstructs the direct ray (between 6-5 and 7-s). Th eduration ofthis fading w a s around 1 s, but would be gene rally dependent on the walkers speed. This can alsobe seen n the results shown in Figures 5 and 6. Th e first of these scenarios correspondsto a case in which twopedestrians walked parallel to the link in opposite directions, both at 0.5 d s . The average path loss for thiscase is similar to the casewith one pedestrian (-5 1.2dB). or this scenario, the received signal had a dynamicrange of 13.7 dB, lmost doublethat obtamed for the single pedestrian case (Figure 4a).Figure 6 shows the channel responses for a two pedestrian scenario,both waking parallel to the link inopposite directions and at different speeds (one at 1.O s nd another at 0.5 d s ) . It can be seen that the firstsignificant fade occurs at around 3 s when the first pedestrian (walking at 1.0 d s ) obstructs he direct ray. Theother fade occurs at 6 s when the second pedestrian (walking at 0.5 d s ) obstructsthe direct ray The durationof the fust fid e is approximately half of the duration of the second fid e suggesting that this is related to thespeed of he pedestrians.In this scenario the average path loss remained at -51.2 dB and the dynamic channelcapacxty reaches two maximum values @& at approximately 29 blskJz) wrresponding to when eachpedestrian obstructsthe direct ray.

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Egure 6 : Channel responses fo r t wo pedestrians walking perpendicular to the link, one at 0.5 d s nd one at1.0 d s ; a)temporal path loss profde for TX 2 to RX 4 and TX t o RX ; b) dynamic channel capacityfo r the 8x8MIMO hannel (SNR=I6dB).The channel responses shown in Figure 7 are for a case in which three pedestrians walked parallel to the linkat three different speeds (0.5 d s , 0.39 d s , and 1.0 d s ) . Three faded sectors can be observed, correspondingto the moment when each pedestrian obstructs th e Imk, at 3 s, 6 s, and 7.7 s respectively. Inthis scenario, theaverage path loss remained similar to the case with two pedestrians (-51.6 dB) and the dynamic channelcapacxty reaches three maximum values, two at approximately31 b/s/Hz and one at 27 blsRIz.

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@)Figore 17: Channel responses for three pedestrians walking perpendicular to the Link, one at 0.5 ds, ne at

0.39 m/ s and one at 1.0 m l s ; a) temporal path loss proffie for TX 2 to RX 4 and TX 2 t o RX 3; b)dynamic channel capacity for the 8x8MIMO hannel (SNR=16 B).S. ConclusionsA new modelling technique for estimating the temporal path loss response and th e capacity behaviour ofindoor MIMO channels in populated environments was presented. The model describes th e effect ofpedestrians on indoor wireless channel capacity when using M M O arrays. Initial results indicate that anincrease in th e value of th e dynamic channel capacity occurs when pedestrians block th e direct ray in a singleroom environment. Themodelling techniquepresented offers a reliable solution to the perfo-ce evaluationof MIMO wireless systems for indoor radio communications by taking into account specific location andpedestrian traffic pa m et er s.References[l] G. J. Foschini &M. J. Gans, On imts of wireless communications in a & dmg environment when usingmuftrple antennas, WirelessPersonal Communications,vol. 6 , 3, pp. 31 1-335, Mar. 1998.[a ] P.F. Driessen C G. J. Foschini, On the capacity formula for multiple inputmultiple output wirelesschannels: a geometric interpretation,IEEE Transactions Communication, vol. 47, No. , pp. 173-176,Feb. 1999.[3] D. . McNamara, M. A. Beach, P. N. letcher & P. Karlsson. Temporal Mliation of Mukiple-InputMuitiple-output (MIMO) chaMels in indoor environments, l I & Intl. Con$ Antenrm & Propagation,vol.n, pp. 578-582, Apr. 2001.[4] D. ore, D.Gesbert, H. Bolcskei, A. Paulraj. m 0 ireless channels: capacity and performanceprediction,IEEE Proc. Globecom 2000, vol. II,pp. 1083-1088,2000.[ 5 ] D. . McNamara, M. A. Beach, P. Karlsson& P. N. Fletcher, Initial CharaderiZation of Multiple-InputMulhple-Output (MIMO) hannels for space-time comunication, ZEEE Vehicular Technolop Conf,

vol. ID, p. 1193-1 197, Fall 2000.[6] F. Villanese, W. G. Scanlon, N. E. Evans & E. Gambi, A hybrid ImageDby-Shootiag UHF radiopropagation predictor for popdated indoor environments, Elecfronics Lefters, vol. 35, 21, pp. 1804-1805, Oct. 1999.[7] G. J. Foschini, Layered space-time architecture for wireless communication in a fading environmentwhen using multtple antennas,BellLubs.Tech. Journal, vol. 1, No. 2, pp. 41-59, Autunm 1996.

13163 zoo1 Th e Institution of Electrical Engineers .arinled and published by the IEE, Savoy Place, London WCPR OBL, UK.

modeling of mimo channels for the populated indoor environment

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