We study the potential benefits of cooperative multiple-input multiple-output signaling from multiple coherent base stations with one or more mobile stations in an urban macrocellular environment. The analysis uses novel and fully coherent measurements of the channel from three base stations to a single mobile station equipped with four antennas. The observed channels are used to explore the gains in capacity enabled by cooperative base station signaling for point-to-point and multi-user communications. The analysis shows that cooperative signaling using practical algorithms yields significant increases in average capacity

Furuskog, Johan

Jensen, Michael Allen

Lau, Buon Kiong

Medbo, Jonas

English

Lund University Publications

LUND UNIVERSITYPO Box 117221 00 Lund+46 46-222 00 00Performance of cooperative MIMO based on measured urban channel dataJensen, Michael Allen; Lau, Buon Kiong; Medbo, Jonas; Furuskog, JohanPublished in:Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP)2011Document Version:Peer reviewed version (aka post-print)Link to publicationCitation for published version (APA):Jensen, M. A., Lau, B. K., Medbo, J., & Furuskog, J. (2011). Performance of cooperative MIMO based onmeasured urban channel data. In Proceedings of the 5th European Conference on Antennas and Propagation(EUCAP) IEEE - Institute of Electrical and Electronics Engineers Inc..https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5782041Total number of authors:4General rightsUnless other specific re-use rights are stated the following general rights apply:Copyright and moral rights for the publications made accessible in the public portal are retained by the authorsand/or other copyright owners and it is a condition of accessing publications that users recognise and abide by thelegal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private studyor research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portalRead more about Creative commons licenses: https://creativecommons.org/licenses/Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will removeaccess to the work immediately and investigate your claim.M. A. Jensen, B. K. Lau, J. Medbo, and J. Furuskog, “Performance of cooperative MIMO based on measured urban channel data,” in Proc. 5th Europ. Conf, Antennas Propagat. (EuCAP’2011), Rome, Italy, Apr. 11-15, 2011.   This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.  ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Performance of Cooperative MIMO Based onMeasured Urban Channel DataMichael A. Jensen∗, Buon Kiong Lau†, Jonas Medbo‡, Johan Furuskog‡∗Department of Electrical and Computer Engineering, Brigham Young University459 CB, Provo, UT 84602, USA. jensen@byu.edu†Department of Electrical and Information Technology, Lund UniversityBox 118, SE-221 00 Lund, Sweden. bkl@eit.lth.se‡Ericsson Research, Ericsson ABSE-164 80 Stockholm, Sweden. {jonas.medbo,johan.furuskog}@ericsson.comAbstract—We study the potential benefits of cooperativemultiple-input multiple-output signaling from multiple coherentbase stations with one or more mobile stations in an urbanmacrocellular environment. The analysis uses novel and fully-coherent measurements of the channel from three base stations toa single mobile station equipped with four antennas. The observedchannels are used to explore the gains in capacity enabledby cooperative base station signaling for point-to-point andmulti-user communications. The analysis shows that cooperativesignaling using practical algorithms yields significant increasesin average capacity.I. INTRODUCTIONWhile multiple-input multiple-output (MIMO) technologyhas demonstrated the potential for realizing significant im-provements in wireless communication performance, realiza-tion of these gains depends on the communication environment[1]. For example, at the base station (BS), the elevated positionand sectorized nature of the antennas leads to limited observedangular spread that makes it difficult to improve performancethrough spatial processing using multiple antennas. One po-tential solution to this problem involves using multiple BSsites working cooperatively, a solution that also potentiallyenables significant benefit in terms of interference control inmulti-user signaling [2], [3]. The benefit of cooperative BScommunication has been studied in the context of determiningthe channel and shadowing correlation properties for multipleBS sites and a single mobile station (MS) [4]–[7].In this paper, we extend the prior work by reporting on theanalysis of fully-coherent measurements from three BS sitesto a single MS in a macrocellular environment, measurementsthat we believe to be the first of their kind. The observedchannels are first used to explore the gains achieved withcooperative MIMO signaling to a single user [8]. This analysisshows that BS cooperation leads to an average increase incapacity of 73% over that achieved using a single BS. Inplaces where two or more base stations contribute nearlyequal signal power to the MS, this increase in capacity canexceed 90%. We then turn our attention to the performanceof cooperative MIMO for multi-user communications for thedownlink or broadcast channel (BC) [2] based on differentpractical signaling strategies over the observed channels. Thisanalysis, which surpasses other studies on experimentally-based multi-user cooperative MIMO, shows that cooperativeMIMO signaling can provide multi-user throughput gains thatare significantly higher than what can be achieved using moretraditional multiple-access strategies under favorable channelconditions.II. MEASUREMENTSMeasurements were performed using three BS sites in anurban macrocell environment within Kista, Stockholm, Swe-den. At each BS, a single antenna mounted a few meters abovethe average rooftop level of approximately 25 m transmits alinearly-polarized (45◦ from vertical) signal. The MS consistsof two dipole and two loop antennas mounted on the top ofa measurement van as a square array with an inter-elementspacing of approximately 30 cm, which is 2.6 wavelengthsat the excitation center frequency of 2.66 GHz. Measurementof the channel between all three BS and four MS antennasis accomplished using the Ericsson mobile channel sounderbased on a prototype for LTE [9] that uses time-multiplexedorthogonal frequency division multiplexing (OFDM) symbolswith 432 tones to achieve a measurement bandwidth of19.4 MHzThe MS uses four parallel receiver chains to simultaneouslydownconvert the signals from the four receive antennas. Disci-plined rubidium clocks at the transmitter and receiver providea highly accurate synchronization between the BS and theMS. The system records the 4 × 3 MIMO channel matrixat a rate of 190 samples per second, providing high spatialresolution given the maximum van speed of 30 km/hr. Allof the parameters used in the measurements are provided inTable I. Figure 1 shows the two routes along with markersindicating the distance traveled along each route and thepositions of the base stations.III. SINGLE-USER CHANNELSTo study the benefit of BS cooperation for communicationwith a single MS, we normalize the measured 4× 3 multi-BS(MIMO) channels by the total received power of the strongest4 × 1 BS-to-MS single-input multiple-output (SIMO) link ateach measurement point. We then compute the capacity forTABLE ISPECIFICATIONS FOR THE ERICSSON CHANNEL SOUNDERParameter ValueCenter Frequency 2.66 GHzBandwidth 19.4 MHzFrequency bins 432Transmit power 36 dBmChannel acquisition rate 190 channels/sNumber of BS 3BS antenna 1 Kathrein (18 dBi 45 deg polarized)MS antenna 2 dipoles + 2 magnetic loops0 20040060080010001200140016000200400600800100012001400160018002000x−coordinate (m)y−coordinate (m)BS1BS2BS3100 200 300 400 500 600 700 8006005004003002001000Fig. 1. Location of BSs and route 1 (- -) and route 2 (---) of MS. Distances(in meters) from starting points are indicated by • and  markers.the cooperative base stations assuming a reference signal-to-noise ratio (SNR) of 20 dB and present results averaged overthe frequency samples assuming that the capacity is computedwith base stations transmitting equal power or assuming thatpower control is used to achieve the capacity of the water-filling solution.Figure 2 plots the capacity of the best single-BS link and themulti-BS communication for Route 1 after smoothing using amoving average filter over a window of 10 wavelengths anddownsampling. The capacity values in this plot are obtainedassuming the base stations have equal transmit power. Theaverage capacity achieved for channel coefficients modeledas i.i.d. zero-mean complex Gaussian random variables with20 dB SNR is indicated by the black triangle. These resultsdemonstrate that BS cooperation provides significant potentialcapacity gain, although the capacity falls short of that achiev-able with i.i.d. coefficients.Next we compute the capacity obtained assuming coopera-tive MIMO signaling (for both equal and water-filling powerallocation) from all BS sites as well as for the best BS-to-MS SIMO link averaged over all measurement points andfrequencies. Figure 3 shows the percentage increase in theaverage capacity achieved using cooperative BS signaling0 500 1000 1500 20000510152025Average Capacity (bits/s/Hz)Distance traveled (m)  Best SIMO LinkEqual Power MIMOFig. 2. Average capacity for SIMO link from the best BS and usingcooperative MIMO with equal transmit power for the MS on Route 1.Route 1 Route 2 Combined020406080100Capacity Increase over Best SIMO (%)Equal Power MIMOWater−Filling MIMOEqual Power MIMOWater−Filling MIMOEqual Power MIMOWater−Filling MIMOFig. 3. Improvement in average capacity for cooperative BS signaling basedon equal and water-filling transmit power relative to that of the best BS linkaveraged over all measurements for each route and the combined routes.relative to that achieved using the link from the best BS. Theseresults show that BS cooperation leads to significant capacityimprovements, with the capacity almost doubling for Route 2.IV. MULTI-USER CHANNELSFor multi-user communications, we focus on two MSs onthe measurement routes, which means that we use channelsmeasured at different times to obtain the required channel datafrom the BSs to the spatially-displaced users. We assume thateach MS only receives or transmits a single data stream.For the BC, the cooperative BSs apply a beamformer tothe signal for each MS and constrain the total transmit powerP , which implies that the BSs use power control and canchange their power allocation up to a total of P . The sum rateexperienced for this BC can be computed using techniquesin [10]. We first assume that each MS receives using only oneof the vertically-polarized antennas in a multiple-input single-output (MISO) configuration. We consider three differentBC MISO scenarios. (a) As a reference, each mobile userestablishes a link with the BS for which the BS-to-MS gain ismaximum, even if multiple MSs share the same BS. We alsocompute the sum rate achieved when the two MSs equallydivide the communication time (time division multiple access,or TDMA), and use this rate for the reference if it exceedsthat for BC MISO signaling. (b) We determine the BS-MSpairs that achieve the largest sum rate. (c) We compute thesum rate for the true cooperative BC MISO with the signalingestablished based on the regularized channel inversion (RCI)method [11].We also assume a BC MIMO situation where each MSknows (through feedback) all of the transmit beamformersand can therefore construct a minimum-mean-squared error(MMSE) beamformer [11]. For this BC MIMO, we use thesame scenarios as outlined above for the MISO case.For the computations, at each measurement point we scalethe channel matrices for all links by the same constant βcomputed fromβ =√2NBNr∑k ∥Hk∥2F, (1)where Hk is the Nr×NB measured channel matrix to the kthMS, NB is the number of BSs, Nr is the number of receiveantennas at each MS, and ∥ · ∥F indicates a Frobenius norm.With this normalization and given the assumption of Gaussiannoise with unit variance, the total power P represents thesingle-input single-output (SISO) SNR averaged over the MSswhich is set to be 20 dB. Referring to Fig. 1, the first MSmoves along the entirety of routes 1 and 2 while the secondMS stays stationary at points that are either 700 m or 900 mfrom the start along route 2.As an example, Fig. 4 plots the sum rate achieved assumingBC MISO signaling for the three topologies discussed. We firstobserve that the maximum gain pairing works well comparedto the optimal BS-MS pairing when MS1 is on the mainroads (e.g. between displacements of 750 and 900 m) andenjoys nearly LOS propagation and therefore a dominant linkwith a single BS. However, when MS1 deviates into a small“inlet” (e.g. between displacements of 250 and 550 m), themaximum gain pairing increases the multi-user interference,and therefore a different pairing that reduces interference isbeneficial. We emphasize that in these interference-limitedscenarios, the maximum gain pairing would suffer significantadditional degradation were it not for the ability to switch toTDMA. Finally, since the link gain for two or more BSs toa single MS is similar in these regions, allowing the multipleBSs to collaborate to control interference and maximize linkgains through application of the RCI beamforming weightsprovides sigificant additional sum rate capability.Figure 5 shows the percentage increase in average sum rateachieved for MISO and MIMO BC signaling over all foursimulations (two routes for one MS each with two locations forthe second MS) relative to the sum rate achieved for the MS-200 400 600 800 100005101520MS1 Position: Route 1 (m)Sum Rate (bits/s/Hz)MS2: Route 2, 700 m  Max Gain PairingOptimal PairingRCIFig. 4. Sum rates computed for different BC MISO signaling approacheswhen MS1 travels along a portion of route 1 and MS2 is at the point 700 mfrom the start along route 2.BC MISO BC MIMO0510152025303540Capacity Increase over Max Gain Pairing (%)  Optimal PairingRCIFig. 5. Improvement in average sum rates for BC MISO and MIMO relativeto the case where the signaling uses the optimal pairing between the BS andMS.BS pairing based on maximum link gain. Considering the dis-cussion corresponding to Fig. 4, it is not surprising to see thedramatic improvement achieved by cooperative BS signaling.This plot further reveals, however, that for MIMO signaling,simply selecting the optimal pairing achieves most of the avail-able gain. This is because the multi-antenna reception enableseach MS to reduce the interference from the stream destinedto the other MS, and therefore cooperative BS transmissionprovides only incremental additional improvement. Finally, itis important to note that while the percentage increase forMIMO BC is smaller than that for MISO BC, the averagesum rate for the MS-BS pairing based on maximum link gainis substantially higher for MIMO BC than for MISO BC. Thesubstantial performance gains observed motivate additionalresearch aimed at making cooperative MIMO systems practicalfor data intensive wireless networks.V. CONCLUSIONSThis paper uses fully-coherent measurements from threeBS sites to a single MS in a macrocellular environmentto explore the potential gains achievable with cooperativeBS communication for single-user and multi-user scenarios.Specifically, computations with the data for point-to-pointlinks demonstrate that the capacity increases by 73% onaverage and over 90% for the best route as a result of coop-erative communications. Evaluation of the data with practicalmulti-user signaling strategies assuming two MSs shows thatcooperation between the BSs can also significantly increasethe multi-user sum rate. Such dramatic capacity improvementmotivates further study of coherent cooperative communica-tions for macrocellular settings.ACKNOWLEDGMENTThis work was supported in part by Telefonaktiebolaget LMEricssons Stiftelse för Främjande av Elektroteknisk Forskning,VINNOVA under Grant 2008-00970, and in part by the U. S.Army Research Office under the Multi-University ResearchInitiative (MURI) Grant # W911NF-07-1-0318.REFERENCES[1] M. A. Jensen and J. W. Wallace, “A review of antennas and propagationfor MIMO wireless communications,” IEEE Trans. Antennas Propag.,vol. 52, pp. 2810–2824, Nov. 2004.[2] A. Goldsmith, S. A. Jafar, N. Jindal, and S. Vishwanath, “Capacity limitsof MIMO channels,” IEEE Trans. Inf. Theory, vol. 21, pp. 684–702, Jun.2003.[3] S. Zhou, M. Zhao, X. Xu, J. Wang, and Y. Yao, “Distributed wirelesscommunication system: A new architecture for future public wirelessaccess,” IEEE Commun. Mag., vol. 41, pp. 108–113, Mar. 2003.[4] N. Jaldén, P. Zetterberg, B. Ottersten, and L. Garcia, “Inter- and intrasitecorrelations of large-scale parameters from microcellular measurementsat 1800 MHz,” EURASIP J. Wireless Commun. Netw., 2007.[5] M. Alatossava, A. Taparugssanagorn, and V. Holappa, “Measurementbased capacity of distributed MIMO antenna system in urban microcel-lular environment at 5.25 GHz,” in Proc. IEEE Vehicular TechnologyConf. Spring, Singapore, May 2008, pp. 430–434.[6] V. Jungnickel, S. Jaeckel, L. Jiang, U. Krüger, A. Brylka, and C. vonHelmolt, “Capacity measurements in a cooperative MIMO network,”IEEE Trans. Veh. Technol., vol. 58, no. 5, pp. 2392–2405, Jun. 2009.[7] T. W. C. Brown, P. C. F. Eggers, and K. Olesen, “Simultaneous 5GHz co-channel multiple-input-multiple-output links at microcellularboundaries: interference or cooperation?” IET Proc. Microw. AntennasPropagat., vol. 1, no. 6, pp. 1152–1159, Dec. 2007.[8] B. K. Lau, J. Medbo, and J. Furuskog, “Downlink cooperative MIMOin urban macrocell environments,” in Proc. IEEE Antennas Propagat.Soc. Int. Symp., Toronto, Canada, Jul. 11-17, 2010.[9] Y. Selen and H. Asplund, “3G LTE simulations using measured MIMOchannels,” in Proc. Globecom IEEE Global Telecommunications Conf.,New Orleans, LA, USA, Nov. 2008.[10] A. L. Anderson, J. R. Zeidler, and M. A. Jensen, “Reduced-feedbacklinear precoding with stable performance for the time-varying MIMObroadcast channel,” IEEE J. Selected Areas Commun., vol. 26, pp. 1483–1493, 2008.[11] Q. H. Spencer, J. W. Wallace, C. B. Peel, T. Svantesson, A. L.Swindlehurst, and A. Gummalla, “Performance of multi-user spatialmultiplexing with measured channel data,” in MIMO System Technologyand Wireless Communications. CRC Press, 2006.

Performance of cooperative MIMO based on measured urban channel data

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Performance of cooperative MIMO based on measured urban channel data

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