a fourth-generation mimo-ofdm broadband wireless system design performance and field trial results

IEEE Communications Magazine • September 2002 143

A Fourth-Generation MIMO-OFDMBroadband Wireless System: Design,Performance, and Field Trial Results

0163-6804/02/$17.00 © 2002 IEEE

ABSTRACT

Increasing demand for high-performance 4Gbroadband wireless is enabled by the use of multi-ple antennas at both base station and subscriberends. Multiple antenna technologies enable highcapacities suited for Internet and multimedia ser-vices, and also dramatically increase range andreliability. In this article we describe a multiple-input multiple-output OFDM wireless communi-cation system, lab test results, and recent field testresults obtained in San Jose, California. These arethe first MIMO system field tests to establish theperformance of MIMO communication systems.Increased capacity, coverage, and reliability areclearly evident from the test results presented inthis article.

INTRODUCTIONThis design is motivated by the growing demandfor broadband Internet access. The challenge forwireless broadband access lies in providing a com-parable quality of service (QoS) for similar cost ascompeting wireline technologies. The target fre-quency band for this system is 2–5 GHz due tofavorable propagation characteristics and lowradio frequency (RF) equipment cost. The broad-band channel is typically non-LOS channel andincludes impairments such as time-selective fad-ing and frequency-selective fading. This articledescribes the physical layer design of a fourth-generation (4G) wireless broadband system thatis, motivated from technical requirements of thebroadband cellular channel, and from practicalrequirements of hardware and RF. The key objec-tives of the system are to provide good coveragein a non-line-of-sight (LOS) environment (>90percent of the users within a cell), reliable trans-mission (>99.9 percent reliability), high peak datarates (>1 Mb/s), and high spectrum efficiency(>4 b/s/Hz/sector). These system requirementscan be met by the combination of two powerfultechnologies in the physical layer design: multi-

input and multi-output (MIMO) antennas andorthogonal frequency division multiplexing(OFDM) modulation. Henceforth, the system isreferred to as Airburst.

Multiple antennas at the transmitter andreceiver provide diversity in a fading environ-ment. By employing multiple antennas, multiplespatial channels are created, and it is unlikely allthe channels will fade simultaneously. The Air-burst system employs two transmit antennas andthree receive antennas at the base station (2 × 3downlink), and one transmit antenna and threereceive antennas at the customer premises equip-ment (CPE) (1 × 3 uplink). Only one transmitantenna is used at the transmitter due to costconsiderations. It is seen that spatial diversity inAirburst yields link budget improvements of10–20 dB compared to a single-input single-out-put (SISO) system by reducing the fade margins.In addition, the two base transceiver station(BTS) antennas are used to double the data ratefor users with certain channel characteristics bytransmitting independent data streams from thetwo antennas. This technique, known as spatialmultiplexing, can significantly increase systemcapacity [1, 2]. At the receiver, multiple anten-nas are used to separate spatial multiplexingstreams and for interference mitigation, whichmakes aggressive frequency reuse a reality.

OFDM is chosen over a single-carrier solu-tion due to lower complexity of equalizers forhigh delay spread channels or high data rates. Abroadband signal is broken down into multiplenarrowband carriers (tones), where each carrieris more robust to multipath. In order to main-tain orthogonality among tones, a cyclic prefix isadded that has length greater than the expecteddelay spread. With proper coding and interleav-ing across frequencies, multipath turns into anOFDM system advantage by yielding frequencydiversity. OFDM can be implemented efficientlyby using fast Fourier transforms (FFTs) at thetransmitter and receiver. At the receiver, FFTreduces the channel response into a multiplica-

Hemanth Sampath, Shilpa Talwar, Jose Tellado, and Vinko Erceg, Iospan Wireless Inc.

Arogyaswami Paulraj, Iospan Wireless Inc. and Stanford University

TOPICS IN WIRELESS COMMUNICATIONS

Authorized licensed use limited to: ECOLE DE TECHNOLOGIE SUPERIEURE. Downloaded on April 01,2010 at 09:55:47 EDT from IEEE Xplore. Restrictions apply.

IEEE Communications Magazine • September 2002144

tive constant on a tone-by-tone basis. WithMIMO, the channel response becomes a matrix.Since each tone can be equalized independently,the complexity of space-time equalizers is avoid-ed. Multipath remains an advantage for aMIMO-OFDM system since frequency selectivitycaused by multipath improves the rank distribu-tion of the channel matrices across frequencytones, thereby increasing capacity [3].

Another key feature of the physical layerdesign is adaptive modulation and coding thatallows different data rates to be assigned to dif-ferent users depending on their channel condi-tions. Since the channel conditions vary overtime, the receiver collects a set of channel statis-tics which are used both by the transmitter andreceiver to optimize system parameters such asmodulation and coding, signal bandwidth, signalpower, training period, channel estimation fil-ters, automatic gain control, and so on. The Air-burst system has a proprietary link adaptationalgorithm (LA) that tracks channel variationsand adapts transmission parameters to performoptimally under prevailing conditions [4].

Of course, a successful broadband wirelessaccess system must have an efficient co-designedmedium access control (MAC) layer for reliablelink performance over the lossy wireless channel.The corresponding MAC is designed so that theTCP/IP layers see a high-quality link that itexpects. This is achieved by an automatic retrans-mission and fragmentation mechanism (automaticrepeat request, ARQ), wherein the transmitterbreaks up packets received from higher layersinto smaller subpackets, which are transmittedsequentially. If a subpacket is receiver incorrectly,the transmitter is requested to retransmit it. ARQcan be seen as a mechanism for introducing timediversity into the system due to its capability torecover from noise, interference, and fades. Moredetails on ARQ design can be found in [5].

The performance of the Airburst system isdemonstrated by lab and field trial results. Theperformance can be measured by three key met-rics: coverage, spectrum efficiency, and reliability.The first two metrics are key in determining thecost of the system. Good coverage is important

initially when few base stations are installed, whilespectrum efficiency defines how many users canbe supported per unit of spectrum over the long-term. Reliability determines the quality of servicea customer receives, and correspondingly long-term customer satisfaction. The system is current-ly targeted for business, home-office, residentialand mobile users requiring high-rate data services.

MIMO-OFDMDESIGN CONSTRAINTS

NON-LOS CHANNEL MODELSIn this section we briefly describe the key chan-nel characteristics that influence the broadbandwireless system design such as channel disper-sion, Ricean K-factor, Doppler, cross-polariza-tion discrimination, antenna correlation, andcondition number. Figure 1 shows a typical non-LOS propagation scenario.

Channel Dispersion — An important channelcharacteristic that influences a system perfor-mance is channel dispersion due to reflectionsfrom close in and far away objects. The dispersionis often quantified by the rms delay spread, whichincreases with distance, and changes with environ-ment, antenna beamwidth, and antenna height [6].Typical values are in the 0.1–5 µs range.

K-Factor — The fading signal magnitude follows aRice distribution, which can be characterized bytwo parameters: the power Pc of constant channelcomponents and the power Ps from scatter channelcomponents. The ratio of these two (Pc/Ps) is calledthe Ricean K-factor. The worst case fading occurswhen Pc = 0 and the distribution is regarded asRayleigh distribution (K = 0). The K-factor is animportant parameter in system design since itrelates to the probability of a fade of certain depth.Both fixed and mobile communications systemshave to be designed for the most severe fading con-ditions for reliable operation (i.e., Rayleigh fading).

Doppler — The fixed wireless channel Dopplerspectrum differs from the mobile channelDoppler spectrum [6]. For fixed wireless chan-nels, it was found that the Doppler is in the0.1–2 Hz frequency range and has close to expo-nential or rounded spectrum shape. For mobilewireless channels, the Doppler can be on theorder of 100 Hz and has the Jake’s spectrum.

Cross-Polarization Discrimination — Thecross-polarization discrimination (XPD) isdefined as the ratio of the co-polarized averagereceived power Pll to the cross-polarized averagereceived power, P⊥ . XPD quantifies the separa-tion between two transmission channels that usedifferent polarization orientations. The largerthe XPD, the less energy is coupled between thecross-polarized channels. The XPD values werefound to decrease with increasing distance [6].

Antenna Correlation — Antenna correlationplays a very important role in single-input multi-output (SIMO), multi-input single-output(MISO), and MIMO systems. If the complex cor-relation coefficient is high (e.g., greater than 0.7),

■ Figure 1. A wireless propagation scenario.

Basestation

Receiver



diversity and multiplexing gains can be significant-ly reduced (or completely diminished in the caseof correlation of 1). Generally, it was found thatthe complex correlation coefficients are low, inthe 0.1–0.5 range for properly selected base sta-tion and receiver antenna configurations.

Condition Number — The condition number isdefined as a ratio of the maximum and minimumeigenvalues of the MIMO channel matrix. Largecapacity gains from spatial multiplexing opera-tion in MIMO wireless systems is possible whenthe statistical distributions of condition numbershave mostly low values. LOS conditions oftencreate undesirable MIMO matrix conditions(i.e., high condition numbers) that can be miti-gated using dual-polarized antennas. For lowBTS antennas most propagation conditions arenon-LOS with a considerable amount of scatter-ing, in which case the multiplexing gains ofMIMO systems are very significant.

RF AND HARDWARE CONSIDERATIONSIn addition to the wireless channel characteristics,we need to consider the practical hardware (HW)limitations of low-cost RF and mixed signaldevices when designing a broadband wireless datasystem. Moreover, since wireless systems mustcoexist with other co-channel and adjacent-chan-nel services, the system must meet emission speci-fications at the transmitter (masks, max EIRP,etc.) and must be able to tolerate specified levelsof undesired interfering signals at the receiver.

The distortion effects from the HW will add tothe degradation effects from the channel to yieldthe overall link performance. Moreover, undergood channel conditions the HW distortion willultimately determine the maximum performance ofthe link. For a MIMO system operating in spatialmultiplexing mode, the HW SNDR requirement isonly a few dB higher since the channel matrix con-dition number can increase the effective receivedistortion. Measured field trial results of our sys-tem confirm that the HW SNDR requirement isonly a few dB higher for a MIMO receiver operat-ing in spatial multiplexing mode relative to a SISOcounterpart that operates at a fraction of the datarate. On the other hand, since the effective datarate grows logarithmically with increasing SNDR, aSISO system with equal data rate would requireHW specifications to get exponentially better.Moreover, for a MIMO system operating in diver-sity mode the HW requirements are lower than itsSISO counterpart due to HW impairment diversitysince the distortion is typically uncorrelated acrossmultiple HW chains. In the 2–5 GHz frequencybands, it is possible to design low-cost wireless HWusing IC components. After aggregating all the dis-tortion effects, including both the transmitter andreceiver ends, a good design will yield up to 30 dBof SNDR. With this SNDR it is possible to suc-cessfully transmit MIMO with up to 64-quadratureamplitude modulation (QAM) with light coding.There exist a large number of sources of distortionat both the transmit and receive ends of a broad-band wireless system, but the most significant are:

DAC/ADC: Digital–analog and analog–digitalconverters, mixed signal devices, generate distor-tion through saturation, quantization noise, andspurs. For high-performance broadband wireless

applications with adequate level control, 10effective bits with minimal oversampling are typ-ically enough not to degrade the overall SDR.

DAC/ADC clocks: The sampling instants atboth transmitter and receiver will not be uniformspaced and will have slightly different rates.Even with timing tracking loops at the receiverto account for clock drifts, the residual timingphase noise or jitter will cause residual SDR.The timing jitter rms must be less than 1 percentof the data sampling rate for SDR > 30 dB.

Up/downconverter oscillators: The frequencyconverters will introduce frequency drift and addphase noise. Even with phase tracking loops, theintegral of the phase noise beyond 1 percent ofthe OFDM tone width must be less than –30dBc to get SDR > 30 dB.

Linearity and dynamic range: All HW com-ponents introduce noise and have a limitedrange over which the signal can be processedwithout significantly distorting it. Thus, the sig-nal levels must be carefully controlled with acombination of power control and automaticgain control (AGC) to maximize the signal levelrelative to the HW noise without saturating thedevice. OFDM signals have slightly higher peakto average ratios (PARs) than other high-perfor-mance modulations, and extra care is required.The dynamic range and linearity requirements ofOFDM can be made comparable to single-carri-er modulation with PAR reduction algorithms.

MIMO-OFDM ARCHITECTUREThe combined application of multi-antenna tech-nology and OFDM modulation (MIMO-OFDM)yields a unique physical layer capable of meetingthe requirements of a second-generation non-LOS system. Herein, we discuss some key designand algorithmic choices made in implementationof the Airburst modem.

Transmit Diversity — Many transmit diversityschemes have been proposed in the literatureoffering different complexity vs. performancetrade-offs. We chose delay diversity for downlinktransmission due to its simple implementation,good performance, and no feedback require-ment. In this scheme, the signal sent from thesecond antenna is a delayed copy of the signal atthe first antenna. The delay introduced at thetransmitter results in frequency selectivity in thereceived channel response. With proper codingand interleaving, space-frequency diversity gainis achieved without requiring any channel knowl-edge at the transmitter. Next-generation systemsystem design incorporates improved transmitdiversity schemes. Of particular interest arespace-time codes that require no feedback [7],and linear precoding based on channel statisticsthat requires minimal feedback [8]. In space-time coding, the same signal is encoded differ-ently into different streams to be transmittedacross multiple antennas. Block codes are attrac-tive since they allow linear decoding at thereceiver (e.g., the Alamouti scheme) [9]. In lin-ear precoding, the transmitted signals are linear-ly mapped onto multiple transmit antennas,depending on the slowly varying channel statis-tics such as transmit antenna correlation. Linear

The combined

application of

multi-antenna

technology and

OFDM

modulation

(MIMO-OFDM)

yields a unique

physical layer

capable of

meeting the

requirements of a

second-

generation

non-LOS system.



precoding can be used in conjunction with space-time codes to provide performance gains. Pre-liminary field trials have shown 2–6 dB gain overdelay diversity and up to 3 dB gain over theAlamouti scheme across the cell.

Spatial Multiplexing — It is possible to trans-mit two separately encoded data streams fromthe two base station antennas. A high-rate signalis multiplexed into a set of lower-rate streams,each of which is encoded, modulated, and trans-mitted at a different antenna, while using thesame time and frequency slot. Each of the threereceiving antennas receives a linear combinationof the two transmitted messages that have beenfiltered by different channel impulse responses.The receiver separates the two signals using aspatial equalizer, and demodulates, decodes, anddemultiplexes them to yield the original signal.Separation is possible as long as each streaminduces a different spatial signature at the receiv-er (i.e., the channel matrix has full rank). Sincemost spatial equalizers use some form of chan-nel matrix inversion, a unique solution is onlypossible if the number of receive antennas isgreater than or equal to number of independenttransmit signals. In practice, it is preferred thatthe number of receive antennas be greater sincethis leads to a better-conditioned channel matrix,and therefore more accurate channel inversionin fixed precision arithmetic. The link adaptationlayer monitors the channel conditions on a peruser basis and determines the optimal transmis-sion scheme: multiplexing or diversity.

Receive Diversity and Interference Cancella-tion — Receive diversity is available at both theBTS and CPE due to three receive antennas. Thediversity is leveraged by a maximal-ratio-combin-ing (MRC) algorithm that coherently combinessignals at multiple receivers to maximize signal-to-noise ratio (SNR). There is some natural inter-ference suppression with MRC since it matchesthe spatial signature of the desired signal, not thatof the interferer, which therefore gets attenuated.MRC cannot, however, suppress strong interfer-ence such as that arising from spatial multiplexingwhere the two streams interfere with each other,or from strong co-channel users in other cells dueto aggressive frequency reuse. In this case, it is

desirable to use the minimum mean square error(MMSE) algorithm that minimizes the meansquare error between each desired signal and itsestimate, thereby maximizing signal-to-interfer-ence-plus-noise ratio (SINR). The MMSE weightsrequire knowledge of noise-plus-interferencestatistics. Therefore, it is important to detectwhether the ambient environment is noise-limitedor interference-limited, and to collect accuratestatistics by averaging over appropriate time andfrequency intervals. If the interferer is friendly(e.g., a spatial multiplexing user), the interferer’sspatial signature is available to be used in theMMSE weights. For unfriendly interference fromneighboring cells, second-order statistics (covari-ance matrices) are used to capture the spatialstructure of interference.

Soft Decoding — Both MRC and MMSE algo-rithms yield soft signal estimates that are inputto a soft decoder. The soft decisions are weight-ed by the estimated SINR on a tone-by-tonebasis to give more weight to good tones and lessweight to bad tones. Soft-decision decoding com-bined with SINR weighting provides significantperformance gains (3–4 dB) in frequency selec-tive channels.

Channel Estimation — The purpose of channelestimation is to identify the channel between eachpair of transmit and receive antennas. The train-ing tones transmitted from each antenna areorthogonal with respect to each other so that thechannel from each transmit antenna can be iden-tified uniquely. The training tones are spaced infrequency, with spacing less than the channel’sfrequency coherence so that the channel can beinterpolated between training tones. The channelinterpolation is optimized depending on channeldelay spread, and can be further improved bytime-domain filtering. In the downlink, a dedicat-ed channel identification slot is broadcast to allusers on a frame-by-frame basis. In the uplink,each slot includes both training and data tonessince the traffic from the CPE can be bursty, andthe channel may change between bursts.

Synchronization — Both uplink and downlinktransmission are preceded by a synchronization(sync) slot for timing phase, timing frequency,

The link

adaptation layer

monitors the

channel

conditions on a

per user basis

and determines

the optimal

transmission

scheme:

multiplexing

or diversity.

■ Figure 2. From left to right: a BTS chassis, a CPE board with Airburst ASIC, and CPE.



and frequency offset estimation. The slot isstructured such that data and training are trans-mitted over even numbered tones, and odd tonesare set to zero. This introduces a repetitive pat-tern in the time-domain signal, which allows esti-mation of the above parameters. Oncesynchronization is obtained, fine timing esti-mates can be computed from the training tones.

Adaptive Modulation and Coding — TheAirburst system maximizes system capacity byoptimizing the link parameters available to eachuser. There are multiple levels of coding andmodulation that can be optimized on a per userdata flow basis depending on the user’s SINRstatistics at a particular location and time, andthe user’s QoS requirements. The QAM levelsvary from 4 to 64, and the coding consists of apunctured convolutional code combined with aReed-Solomon code. There are six combinationsof QAM and coding levels, referred to as codingmodes. The coding modes 1–6 correspond todata rates of 1.1–6.8 Mb/s obtained over a 2MHz channel. For the downlink, the above ratesare doubled when spatial multiplexing is used.Each coding mode has a different setpoint,which is defined to be the average SINRrequired to obtain a specified pre-ARQ packeterror rate, typically in the 0.1–5 percent range.The setpoint is a function of channel characteris-tics: delay spread, K-factor, antenna correlation,Doppler, and so on. The link adaptation algo-rithm chooses the best coding mode per userbased on SINR statistics provided by the modem.

LABORATORYPERFORMANCE RESULTS

For software system simulations and laboratoryperformance validation purposes, a set of non-LOS channel models were developed by theIEEE 802.16 working group [6]. These modelswere implemented in both software and hard-ware to test and define the system performance.For each transceiver six multipath channels (2 ×3) had to be implemented, each with three tapdelay lines. We used three TAS-Flex 500 simula-tors with two channels each to emulate downlinkfading channels. The Airburst modem was mod-eled in software using Matlab/C code (Physim).For all channel models, bit accurate simulationsof the modem were run to predict bit error rate(BER) performance vs. SNR and SIR. Theseresults were later compared with results obtainedin the laboratory using HW channel emulatorsand the current ASIC based product. Figure 2shows a picture of some main elements of theproduct, namely a BTS chassis, a CPE boardwith an Airburst application-specific integratedcircuit (ASIC), and CPE.

Results — Figure 3 shows a BER comparison ofPhysim and laboratory results for a subset ofchannel models and transmission modes:• SUI-3 channel: uplink coding mode 2• SUI-4 channel: downlink transmit diversity

coding mode 3• SUI-6 channel: downlink spatial multiplex-

ing coding mode 3

It is seen that there is very good agreementbetween laboratory HW measurements and thePhysim model.

FIELD TRIAL RESULTS

TEST SETUPThe performance of the Airburst system is cur-rently being verified with outdoor field trials.The BTS antennas are located on the rooftop(approximately 49 ft high) of the Iospan Wire-less building, and the CPEs are placed at differ-ent locations within a 3.5-mi cell radius and a120° sector facing west of Iospan Wireless. Theenvironment can be characterized as suburbanwith residential blocks (1–3 floors), commercialbuildings (2–5 floors), moderate trees (30–90 fthigh), and slightly hilly terrain. The total BTStransmit power and EIRP are 35.5 dBm and 51dBm, respectively, while the CPE maximumtransmit power and EIRP are 30 and 42 dBm,respectively. The downlink was operated at 2.683GHz and uplink at 2.545 GHz center frequen-cies (multimegabit data service, MMDS, band),with a channel bandwidth of 2 MHz. At theBTS, the transmit and receive antennas arespaced 16 and 8 wavelengths, respectively, with again of 16 dBi and 3 dB beamwidth (azimuth) of100°. At the NAU, the receive antennas arespaced up to 1.5 wavelengths, with a gain of 12dBi and 3 dB bandwidth of 90°.

Field trials include:• Modem performance evaluation — deter-

mining the data rates at different locationsusing a fully functional CPE under normaloperating conditions

• Channel measurements — characterizingthe wireless channel across time, space andfrequency, by using the Airburst system in atest-mode.

In test mode, the BTS transmits known trainingsignals, and the CPE samples the received sig-nals every 50 ms in time and 72 kHz in frequen-

■ Figure 3. Sample BER vs. SNR for several SUI models and link modes.

SNR (dB)

20 220

10–1

Bit

erro

r ra

te 10–2

10–3

10–4

100

18161412108642

Physim SUI-3Lab SUI-3Physim SUI-4Lab SUI-4Physim SUI-6Lab SUI-6



cy (spanning a total of 2 MHz in bandwidth).The outdoor tests and measurements are beingcarried out for both fixed (short- and long-term)and moving scenarios. For the short-term mea-surements, the CPE is placed on a mast mount-ed on a mobile at approximately 9 ft above thelocal ground level. Fixed measurements lastingapproximately 5 min each are being carried outat different distances from the BTS. In addition,long-term fixed measurements lasting from a dayto several months are being carried out by moni-toring fully functional CPEs in several apart-ments and houses at ground level. Movingmeasurements at low vehicular speeds are takento cover at least 50 percent of the cell area with

the CPE antennas pointed in the general direc-tion of the BTS.

RESULTSIn this section we report on the 2 × 3 Airburstsystem field test results. The results include fadingmargins for 99.9 percent link reliability, cell size,and data rates. The results are also compared to 1× 2 and 1 × 1 antenna configurations (systems) byequivalently disconnecting the corresponding RFchains from the 2 × 3 Airburst system.

Fading Margins — The fading margins are afunction of the Ricean K-factor, delay spread,and antenna correlation. Higher delay spreadresults in frequency selectivity that can beexploited by OFDM systems to lower therequired fading margins. For Rayleigh fadingchannels (K = 0) with no delay spread and zeroantenna correlation, the fading margins (includ-ing array gain) for the 99.9 percent link reliabili-ty are 10 dB, 23 dB and 35 dB for 2 × 3, 1 × 2and 1 × 1 antenna configurations, respectively(Physim results).

Figure 4 shows a snapshot of the receivedSNR averaged across the receive antennas for a1 × 1, 1 × 2 and 2 × 3 antenna configurationsobtained from a moving measurement. The vehi-cle was moving at low speeds at a distance of 1.1mi from the BTS. The fading margins for 2 × 3,1 × 2, and 1 × 1 systems for 99.9 percent linkreliability were computed to be 7.5 dB, 14 dB,and 21 dB, respectively. The lower fade marginsfor the 2 × 3 system are attributed to a higherarray gain and diversity gain resulting from mul-tiple antennas. These margins are lower than themargins reported in the previous paragraphsince the measured channel has nonzero delayspread and yields an additional diversity gain.

Similarly, Fig. 5 shows the received SNRacross the receive antennas for a 2 × 3, 1 × 2,and 1 × 1 system for a fixed measurement madeat an apartment 0.8 mi away from the BTS. TheSNR values were reported every 1 min across a24-h period. The weather conditions were drywith wind speeds ranging from 0 to 15 mi/h. Weobserve that the mean received SNR and fadingcharacteristics for the three systems are compa-rable to that seen in a moving measurement,albeit across a larger timescale, due to windblown foliage. Hence, for reliable operation, afixed wireless system needs to be designed withsimilar fade margins as a mobile system, eventhough the former exhibits orders of magnitudelower Doppler relative to mobile links.

Cell Size — For the same transmit power and99.9 percent link reliability, when compared tothe 2 × 3 system, the higher fade marginsrequired for the 1 × 2 and 1 × 1 systems result ina smaller cell size. For example, assuming thepath loss exponent of 4, 12 dB higher fade mar-gin reduces the cell radius by half and thereforethe cell area by one-fourth. Based on our testresults for the given cell site and 90 percent cov-erage, the cell radii turned out to be 4.0 mi, 2.7mi, and 1.6 mi for the 2 × 3, 1 × 2, and 1 × 1 sys-tems, respectively.

Measured Data Rates — Figure 6 shows the

■ Figure 4. Received SNR for a moving measurement.

Time (s)

9 100–15

20

SNR

(dB)

15

10

5

0

–5

–10

25

87654321

1 × 11 × 22 × 3

■ Figure 5. Received SNR for a fixed long-term measurement at an apartment0.8 mi from the BTS.

Time (h)

200–15

15

20

SNR

(dB)

10

5

0

–5

–10

15105 25

1 × 11 × 22 × 3



average recorded data rates as a function of dis-tance from the BTS for the 2 × 3, 1 × 2, and 1 ×1 systems. Data rates are higher for users closerto the BTS due to the lower path loss and there-fore improved SNR. The peak data rates for the1 × 1 and 1 × 2 systems is 6.8 Mb/s, while thatfor the 2 × 3 system is 6.8 * 2 = 13.6 Mb/s (twodata streams with spatial multiplexing). For goodlink reliability, spatial multiplexing requires highreceiver SNR as well as good channel matrixcondition numbers. Such conditions are satisfiedin non-LOS environments for distances less than2 mi from the BTS. Approximately 80 percent ofthe users have data rates greater than 6.8 Mb/sand operate in the spatial multiplexing mode. Atdistances greater than 2 mi from the BTS, the 2× 3 system operates mostly in a diversity mode,where the signals from all antennas are com-bined to lower the fading margins and thereforeimprove coverage (i.e., increase cell radius). Theeffect of multiple antennas on the measureddata rate is dramatic. The mean data rate at the1.1 mi distance from the BTS is approximatelyquadrupled for the 2 × 3 system when comparedto the 1 × 1 system, and doubled when comparedto the 1 × 2 system. As mentioned earlier, thegains are related to the spatial multiplexingcapability and low fading margin of the 2 × 3 sys-tem.

CONCLUSIONSIn this article we describe the Airburst 4G broad-band wireless system, laboratory test results, andrecent field trial results. The laboratory test andfield trial results show the encouraging perfor-mance of the MIMO-OFDM system. The resultsshow dramatic increase in capacity, coverage,and reliability over SISO, MISO, or SIMO com-munication systems.

ACKNOWLEDGMENTThanks are given to Prof. Willie W. Lu for thetechnical review of this article and continuedencouragement. Without his help, this articlewould never have been possible.

REFERENCES[1] A. J. Paulraj and T. Kailath, “Increasing Capacity in Wireless

Broadcast Systems Using Distributed Transmission/Direc-tional Reception,” U.S. Patent, no. 5,345,599, 1994.

[2] G. J. Foschini, “Layered Space-time Architecture forWireless Communication,” Bell Labs Tech. J., vol. 1,Autumn 1996, pp. 41–59.

[3] H. Bolcskei, D. Gesbert, A. J. Paulraj, “On the Capacityof OFDM-Based Spatial Multiplexing Systems,” IEEETrans. Commun., vol. 50, no. 2, Feb. 2002, pp. 225–34.

[4] S. Catreux et al., “Adaptive Modulation and MIMO Cod-ing for Broadband Wireless Data Networks, IEEE Com-mun. Mag., June 2002.

[5] I. Stojanovic et al., “Performance of TCP/IP Over NextGeneration Broadband Wireless Access Networks,”Proc. Int’l. Symp. WPMC, Aalborg, Denmark, 2001.

[6] V. Erceg et al., “Channel Models for Fixed WirelessApplications,“ IEEE 802.16 Broadband Wireless WorkingGroup, Jan. 2001, IEEE802.16.3c-01/29r4; http://www.ieee802.org/16 /tg3/contrib/ 802163c-01_29r4.pdf

[7] V. Tarokh, N. Seshadri, and A.R. Calderbank, “Space-timeCodes for High Data Rate Wireless Communication: Perfor-mance Criterion and Code Construction,” IEEE Trans. Info.Theory, Mar. 1998, vol. 44, no. 2, pp. 744–65.

[8] H. Sampath and A. Paulraj, “Linear Precoding for Space-Time Coded Systems with Known Fading Correlations.”IEEE Commun. Lett., vol. 6, no. 6, June 2002, pp.239–41.

[9] S. Alamouti, “A Simple Transmit Diversity Technique forWireless Communications,” IEEE JSAC, vol. 16, no. 8,Oct. 1998.

BIOGRAPHIESHEMANTH SAMPATH is a senior member of technical staff atIospan Wireless Inc (2000-present). He has been workingon physical layer algorithm design, development and test-ing of the MIMO-OFDM wireless system. He actively partici-pated in outdoor channel measurement campaigns andfield trials. He obtained the Ph.D. degree in Electrical Engi-neering from Stanford University in 2001, where he pro-posed novel transmit precoding methods to improveperformance of multiple antenna systems. He has also heldinternship positions at the Digital CommunicationsResearch Group, Lucent Technologies, and Hughes NetworkSystems. He has over 10 IEEE publications and 7 patentapplications. He received a B.S.E.E degree (honors) fromthe University of Maryland at College Park in 1996, and anM.S.E.E degree from Stanford University in 1998.

SHILPA TALWAR is a senior member of technical staff in thePhysical Layer Design and Algorithms group at IospanWireless where he has been since 1999. Prior to joiningIospan, she worked at Philips Consumer Communications(1998–1999) and Stanford Telecom (1996–1998) as asenior systems engineer. She has worked on various pro-jects in wireless communications such as chip design for3G phones, satellite communications, and global position-ing systems. She holds a Ph.D. in scientific computing anda Master's in electrical engineering from Stanford University(1989–1996).

JOSE TELLADO ([email protected]) is director oftechnology at Iospan Wireless. He joined Iospan in 1999during its early stages as a startup, and has since beendirectly involved in the development of the Company'sinnovative MIMO-OFDM-enabled AirBurst™ technology. Heis the chief designer of the physical layer, and has activelyparticipated in the design of the wireless system architec-ture, the MAC layer, and the RF specification. Prior to join-ing Iospan, he was a research assistant at StanfordUniversity, where he proposed new advanced methods formulticarrier modulation (DMT/OFDM) in the areas of peakto average power rRatio reduction, and multiuser transmitoptimization. He has also consulted for Apple ComputersAdvanced Technology Wireless Group and Globalstar in theareas of WLAN and low-orbit satellite cellular systems,respectively. He received M.S. and Ph.D. degrees in electri-cal engineering, with an emphasis on communication theo-ry from Stanford University in 1994 and 1999, respectively.

VINKO ERCEG received a B.Sc. degree in electrical engineer-ing in 1988 and a Ph.D. degree in electrical engineering in1992, both from the City University of New York. From1990 to 1992 he was a lecturer in the Department of Elec-trical Engineering at the City College of New York. Concur-rently he was a research scientist with SCS Mobilecom,Port Washington, New York, working on spread-spectrumsystems for mobile communications. In 1992 he joinedAT&T Bell Laboratories and in 1996 AT&T Labs-Research asa principal member of technical staff in the Wireless Com-munications Research Department where he worked onsignal propagation as well as other projects related to thesystems engineering and performance analysis of personaland mobile communication systems. Since February 2000he has been working on systems, propagation, deploy-ment, and performance issues of a MIMO OFDM communi-cation system at Iospan Wireless Inc. as a director incommunication systems.

AROGYASWAMI PAULRAJ received a Ph.D. degree from theIndian Institute of Technology, New Delhi, in 1973. He hasbeen a professor of electrical engineering at Stanford Uni-versity since 1992, where he leads a large group in wirelesscommunications. Prior to that he directed several researchlaboratories in India and won a number of awards for hiscontributions to technology development in India. Hisresearch has spanned several disciplines, emphasizing sig-nal processing, parallel computer architectures/algorithms,and communication systems. In 1999 he founded IospanWireless Inc. to develop broadband wireless access systemsexploiting concepts initially developed at Stanford Universi-ty. He serves on the boards of directors/advisory panels ofa few U.S. and Indian companies/venture capital partner-ships.

Combining

OFDM and CDMA

technologies is

attractive for

future wireless

broadband

communications

and software

radio realization.


a fourth-generation mimo-ofdm broadband wireless system design performance and field trial results

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