MIMO SYSTEMS FOR MILITARY COMMUNICATIONSWeijun Zhut, Babak Daneshrad*, Jatin Bhatiat, Hun-Seok Kim*, Daniel Liut, Karim Mohammed, Ragh Prabhu*,

Sandeep Sasit, Anish Shah*,,tSilvus Communication Systems, Inc. Los Angeles, CA, 90064

Wireless Integrated Systems Research (WISR) lab, UCLA, EE Dept.

Abstract - Since the seminal work of Foschini, Gansand Teletar, the research community has generated alarge body of work dealing with various aspects andbenefits of MIMO for wireless data communicationsin civilian and commercial systems. Here we focus onthe use of MIMO for military communication. Inparticular the effectiveness of MIMO for: (a)communications under very high mobility such asUAV based communication and, (b) the use of multiantenna techniques for covert (LPD)communications. Our results show the ability tooperate at speeds of up to 200 mph, and a 17 dBreduction in the required TX power for covert, LPDcommunications, in addition to an interference/jammer mitigation technique based on MIMO eigenbeam-nulling.

INTRODUCTION

Since the early work of Foschini and Gans [6][7] andTeletar [11] on the capacity of multiple antenna radio(MAR), a great body of work has shown the potential forMIMO based communications to deliver unprecedentedspectral efficiency in multi-path rich environments. To alarge extent these studies have been theoretical andsimulation based [1][2][12] [13]. A few experimentalMIMO systems have also been reported in the literature[3][4][5][14][8][9]. However, these trials have beenmostly limited to controlled environments, mostlyindoors, but a few outdoor mobile environments as well.The application of MIMO communications to the needsof the military, however, has not received the same levelof attention as for commercial applications. In particularthe need to support mobility for both land based as wellas airborne assets has not been properly addressed in theliterature. Moreover the capabilities of MIMO basedsystem to aid in covert communications has hithertobeen unexplored. In this paper we take the initial stepstowards addressing these two issues.The paper is organized as follows: in section 2 weprovide an overview of the simulation environment.Section 3 will provide our results relating to highdoppler communications. This section compares two

approaches. The first is a rather straight forwardapproach whereby we limit the size of a packet inbetween channel updates. The second explores the use ofan advanced frequency-time pilot symbol insertionstrategy to improve immunity to high dopplercommunications. Section 4 discusses the utility ofMIMO techniques to aid in LPD/AJ basedcommunications. The paper is then concluded in section5.

BASELINE SIMULATION ENVIRONMENTT

As a baseline system we chose an air interface that isvery close in structure to that of the current 802.11nwireless LAN draft standard [18]. This dictates thestructure of the packet, Figure 1, as well as themodulation format which is OFDM with M-QAMconstellations on each of the subcarriers. Table 1summarizes the main parameters of the baseline system.

4 transmit antennas with 4 2.4GHz carrier frequency.spatial data streams4 receive antennas Sub-carrier spacing 312.5KHz20MHz bandwidth 56 effective carriers, 52 data

carriers + 4 pilot carriersBit interleaved coded Jakes model is used formodulation with binary simulating frequency flatconvolution code and 4-, 16- fading.and 64-QAM.

Table 1, parameters of the baseline MIMO-OFDM systemused in our study.

Figure 2 and Figure 3 show the functional block diagramsfor the transmitter and the receiver sections of thebaseline system respectively. The blocks are rather selfexplanatory and constitute a MIMO-OFDM transceiver.As is implicitly shown in the block diagram of Figure 3,our baseline system includes all receiver algorithms.Block boundary detection is performed via a crosscorrelation based approach. Coarse carrier frequencyestimation is carried out during the SISO AGC preambleportion of the packet and takes advantage of the timedomain repetition sequence used in the preamble. This is

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followed by carrier frequency tracking in the body of thepacket which uses the dedicated pilot subcarriers.Channel estimation is performed during the channeltraining portion of the packet. Here a direct timeaveraging of the channel output during the training phaseis used to arrive at an estimate of the channel response.Finally, direct inversion of the channel is used during thedemodulation process. The system also incorporates acyclic redundancy check (CRC) to flag packet errors,and we use this CRC to provide the packet error rate(PER) statistics for our study.

E pLs E pLs 12 pLs 4 pLs 16 s

SISO SISO Mode MIMO MIMO Chan.AGO Chan Est. Identifier AGC Est.

A 1N0 SISO Mode MIMO MIMO Chan.-n AGO Chan Est. Identifier AGC Est.

MIMOData

MIMOData

Figure 1. Physical Layer Frame Structure

In addition to the receiver algorithms, our simulationsalso included typical hardware impairments that aredetrimental to a MIMO-OFDM communication system.Specifically, we have included three main impairments,these are: phase noise, PA linearity and carrier frequencyoffset. Our phase noise model is described by thefollowing equation: pSD(f) = PSD(O) [1 + (f I f ) ] wheref

[+f f')21and f, are set to 7,905 KHz and 250 KHz respectivelyand PSD(O) = -100 dBc/Hz [15]. This phase noise modelis typical of an inexpensive commercial grade system.Nonlinearity of the power amplifier was also modeled.The input output relationship for the power amplifier

was given as A (1 A) (2) where p is chosen

to be 2.5 [16]. The operating point of the signal waschosen to be 10 dB lower than the full saturation level ofthe PA. Finally the carrier frequency offset of 130 KHzwas also incorporated into our studies whichcorresponds to an offset of 54 ppm at a carrier frequencyof 2.4 GHz.

Figure 2. Functional Block Diagram of the Transmitter

Figure 3. Functional Block Diagram of the Receiver

RESULTS OF THE BASELINE SYSTEM UNDERMOBILITY

In this section we present the simulation results of ourbaseline system under different mobility constrains. Ouraim is to simply vary some of the parameters in thesimulation and to quantify the performance of the systemas a function of these variations. It should be noted thatone of the inherent features of the chosen packetstructure is that it has a very compact header. Thisallows us to quickly estimate the channel andimmediately use that estimate in the data detectionprocess. This in turn provides some level of immunity tosystem degradation in high doppler applications.In the remainder of this section we will provide theperformance results of our baseline 4x4 MIMO OFDMsystem assuming 4-, 16-, or 64-QAM modulation of thedata subcarriers. In particular we will vary the relativespeed of the transmitter and receiver, the channel delayspread and the payload size.

Performance vs. SpeedThe results of Figure 4 suggest that for a 100-Bytepayload, and a target PER of 10%, a QPSK systemssuffers only a fraction of a dB over two orders ofmagnitude variation in speed (from 5 mph to 500 mph).16QAM is viable at speeds of up to 100MPH, and64QAM requires 42dB SNR at a speed of 100MPH. TheRF impairments and practical synchronization algorithmintroduces an approximate loss of 4dB, 5dB, and 12dBat 10% PER and 100MPH, for QPSK, 16QAM, 64QAMrespectively. It is also clear that if high constellationsuch as 64QAM is desired at high speed, the receiverneeds to be modified to combat the time-varying fadingwhich will cause the effective channel to be differentbetween the start and end of the packet. The results at a

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target PER of 1% are significantly different, only QPSKcan provide reasonable performance at speeds of up to100 mph.

10°

io-i\' t-

_sss1-----

xx

10o-3

10-4L4

4x4, 20MHz, QPSK, R = 1/2, 100 Bytes (16 us)

5MPH100MPH500MPH100MPH, ideal

Performance vs. Delay SpreadFigure 5 shows the performance of a stationary systemusing 16QAM with practical receiver and RFimpairments. The results clearly indicate the diversitybenefits of a coded OFDM system operating overchannels with large delay spreads (i.e. 50 ns or 150 ns.For instance, 50 ns delay spread offers about 7dB gain at10% PER compared to flat fading. The synchronizationand RF impairments result in a 4dB loss in performancerelative to an ideal system.

10°<

6 8 10 12 14 16 18 20SNR per RX, dB

(a)

4x4, 20MHz, 16QAM, R = 1/2, 500 Bytes (40 us)

0 ns15 ns50 ns150 ns50 ns, ideal

10

C: -2Lfi 10

10 '

1015 20 25 30 35 40

SNR per RX dB

Figure 5. Packet Error Rate for 16QAM with DifferentDelay Spread

Performance vs. Packet LengthFigure 6 shows the PER performance of a QPSK systemat 100 mph as a function of the packet length. Note thatin our baseline system, the channel is estimated once atthe start of the packet, and that estimate is usedthroughout the packet. Consequently, in a high-dopplermobile environment, the longer the packet becomes, theworst the channel estimate becomes at the end of thepayload. The results here suggest that for a PER of 1%payloads as long as 200 Bytes can be supported withQPSK modulation at mobile speeds of up to 100 mph.However, at 10% PER, we can reasonably supportpackets as large as 400 Bytes. Moreover the resultssuggest an approximate 1 dB penalty for every doublingof the payload size from 100 bytes to 200 and then 400bytes.

(c)

Figure 4. Packet Error Rate for QPSK, 16-QAM and 64-QAM versus speed

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4x4, 20MHz, 16QAM, R = 1/2, 200 Bytes (16 us)

5MPH100MPH100MPH, Ideal

10

10

10

10

10-310

10

w 10 -

10224

15 20 25SNR per RX, dB

(b)

30 35

4x4, 20MHz, 64QAM, R = 2/3, 400 Bytes (16 us)

1-- -I 5MPHX - X X X eT 100MPH

--- -100MPH, ideal

26 28 30 32 34 36SNR per RX, dB

38 40 42

N.l------

of the transmit antennas. Denote x. to be observation ofpilot value at the receiver and c be position of datasymbols (DS). Due to orthogonality, channel estimatescould be obtained by:

c = E[cxp]Cov(xp) xp -Wxp

100 Bytes10 = -'I. 200 Bytes

_ 11 400 Bytes

_ X 600 Bytes800 Bytes1000 Bytes

6 8 10 12 14SNR per RX dB

Figure 6. Packet Error Rate of QPSK with DifferentPacket Size

SUPPORTING HIGH MOBILITY VIA 2-D CHANNELESTIMATION

The results of the previous section suggested that thebaseline system can provide reliable communicationsusing QPSK modulation at relative speeds of up to 100mph. Although this might be acceptable for groundbased units it is not acceptable for UAV basedcommunications. In this section we present a twodimensional pilot insertion approach coupled with a twodimensional channel estimation strategy. The resultsshow that this strategy can support up to 500 mph ofrelative speed.

Due to frequency selectivity and high mobility, thechannel suffers from both frequency and time dispersion.As a consequence of the rapidly time-varying channel,more pilot symbol (PS) are expected in the time domain.It is necessary to sample the two-dimensional space (i.e.Frequency and Time) at greater than Nyquist rate of thechannel process. To perform the channelestimation/tracking in a high mobility environment, anew data-field structure which is known as 2D checkerboard pattern pilot symbol assisted modulation (PSAM)is designed and shown in Figure 7.

In practice, known symbols (Pilot Symbols, PS) areinserted at the transmitter and the channel estimate isacquired by interpolation. The channel estimator consistsof linear combinations of the observations at the PSlocations. The simplest and highest performing way toprocess and estimate channel information from theMIMO environment is to use orthogonal signals on each

Where Cov(xp) is the covariance matrix of PS andW isthe Wiener Filter coefficients. The 12 pilot symbols perOFDM block that are used in the PSAM configurationare shown in Figure 7. This particular PS placementenables the system to track a frequency roll across theframe. In some situations, the optimum interpolationfilter from this sampling of the noisy channel response isa linear filter whose tap coefficients are a function ofparticular channel statistics. Therefore, the Wiener filtercoefficients are often pre-computed and result in an openloop estimation structure which has no acquisition time.Performance of this particular MIMO-OFDM PSAMchannel estimation scheme is simulated in an idealizedenvironment in the absence of any RF impairment, andsynchronization error. The results are presented in Figure8.

MIMO-OFDM PSAM PacketStructureFigure 7, Data-FieldPSAMPacketS ePloSymbol

50 A_-

The *reut ar qut proisig An sho tha this

Ln40

(l) 30

°20 _

2 4 6 8 10 12

Time Instants

Figure 7, Data-Field PSAM Packet Structure

The results are quite promising and show that thistechnique can provide reasonable performance at speedsas high as 500 mph without any significant reduction inthe overall system performance. Figure 8a shows that ina frequency selective channel with an rms delay spreadof 50 ns, the 1% PER of the PSAM system is only 1.5dB away from a system with perfect channel stateinformation (PCSI). This gap is narrowed to 0.2 dBwhen the channel is Rayleigh flat fading, Figure 8b.

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4x4, 20MHz, QPSK, R = 1/2, 100MPH10 -+

10

Er -,w 100-

lo-IL_4 16 18 20 22

4x4 BICM: 16QAM, 5OnS RMS Delay Spread w/ PL=12, 384 BytesPSAM Filter-Coeff Design at 20 dB

1 0

12

I-b-MMSE-PCSI, 500MPH

1-3 MMSE-PSAM, 500MPH16 18 20

100

22SNR, dB

24 26 28

4x4 BICM: 16QAM, OnS RMS Delay Spread w/ PL=12, 384 BytesPSAM Filter-Coeff Design at 20 dB

1 0

-2

--&=MMSE-PCSI, 500MPH

M-3+MMSE-PSAM, 500MPH

1020 22 24 26 28 30 32 34SNR, dB

Figure 8, (a) Packet Error Rate for 16QAM Rate-1/2BICM at 500MPH, over channel with 50 ns rms delayspread, (b) same as (a) but over a Rayleigh flat fading

channel.

MIMO FOR LPD AND AJ

The use of multi antenna techniques in covert or jamresistant communications is quite complementary totraditional spread spectrum techniques employing eitherdirect sequence or frequency hopping. If covertness isneeded (LPD), then MIMO techniques can helpminimize the radiated energy via two distinctmechanisms. The first one is a direct consequence of thebenefits of spatial multiplexing, while the second iseigen-beamforming and is a superset of traditionalbeamforming techniques. In the case of AJ, multiantenna techniques can be used as eigen-beam nullingsubsystems or in the eigen-spreading mode. The eigen-spreading mode has the unique advantage of providingspatial averaging of the jammer energy, analogous tofrequency averaging in a FHSS system.

MIMOfor LPDMIMO spatial multiplexing systems can provide LPDgain by simply trading off spatial (MIMO) gains fortransmit power. Our first approach to gaining insightinto the LPD properties of MIMO systems was toestimate the SNR required to achieve a capacity of 1 bitper second per Hertz. The study generated five hundredRayleigh flat fading matrix channels and for each one

the capacity equation given below was evaluated for therequired SNR. The SNR was computed by setting thecapacity (the left hand side of the equation) to 1 andsolving for p.

C E{1o02 det In + PHH* ] }bits I /Hz

In the above equation C is the capacity, In is the nxn

identity matrix, H is the Rayliegh channel matrix, n isthe number of transmit antennas, and p is the average

signal to noise ratio at each receive branch. The tablebelow shows that at 5% outage, a gain of 22.1 dB is tobe had if the number of receiver and transmit antennas isincreased from 1 to 8. Another interpretation of theresult is that if we want to guarantee that lbps/Hz can besupported in 95% of all Rayliegh matrix channelsencountered, then a lxl system needs an average SNRof 12.8 dB at the receiver, whereas an 8x8 systemrequires an average received SNR of-9.3 dB.

Table 2. Required SNR to achieve a 95% capacity of 1 bitper second per Hertz (bps/Hz) in Rayliegh channels.

A MIMO MMSE Solution for Interference Mitigation(AJ)A rather straight forward means for MIMO systems toprovide interference immunity is by a formulating a

spatial joint detection of the desired signal and nulling ofthe interfering signal. In this section we explore thepotential for using an MMSE based solution to achievethis. We also present simulation results to quantify theperformance benefits of such an approach. We first

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100

formulate the problem by treating the jamming signal asa general interference that is white in the time domainfor most applicability. The modeling of interference is anatural extension to the well-know MIMO (MultipleInput Multiple Output) signal model y = Hx + v. We

model the interfering source z E N(O, 2) as a singleGaussian random variable with 0 mean and(Z2 variance. Augmenting this model to include the

effects of interference we have: y = Hx + hkz + v,where y represents the receive vector, x is the transmitvectorH is the channel vector, hz is the NxN interferencechannel and z is the interference source.The MMSE MIMO decoder has been studiedextensively in the literature and derivation of theoptimum solution can be found in [17]. The MMSEsolution for an NxK array is given by:

Wopt = (HH* + R,,)-'Where Wopt is the KxN weight matrix that multiplies the]xN receive vector y. H is the NxN channel responsematrix and R,, is the NxN interference cross-correlationmatrix associated with h,.

Performance of the proposed receiver is shown for twodifferent types of channels: a frequency-flat fadingchannel, and a frequency selective channel with an rms-delay spread of 50 ns.

100°

10

102

MMSE Ref. w/o InterferenceMMSE at SIR = 30 dB

10 MMSE-IRatSIR =30dB-E-MMSE at SIR =20 dB -

MMSE-IR at SIR = 20 dB XMMSE at SIR = 10 dB

MMSE at SIR =0 dB -MMSE-IR at SIR = 0 dB t

10- MMSE Ref. 4x4 w/o Interference

0 5 10 15 20 25Eb/No, dB

30 35 40

Figure 9. 3x4 un-Coded system, 16QAM, Frequency FlatFading, with PCSI

3x4 un-Coded System, 16QAM, Frequency Flat Fading100

1-110-

102

---=MMSE Ref. w/o Interference, PCSI04 MMSE Ref w Interference, PSAM w Training Period

MMSE Ref. w/o Interference, PSAM w/ Training Period = 2LMMSE Ref. w/o Interference, PSAM w/ Training Period = 3L

10-50 5 10 15 20

Eb/No, dB25 30

In Figure 9, the channel is assumed to be frequency flatfading which mimics a single sub-carrier in a morecomplex OFDM system. As SIR increases, theperformance of MMSE-IR (Interference Rejection) isapproaching the regular 4x4 MMSE curve. This iscontributed by the fact that interference stream is asstrong as the signal stream; the original 3x4 systembecomes a 4x4 system. Even at SIR = 20dB (i.e Redcurve), the performance gain between regular MMSEw/o IR and w/ IR is very significant, since w/o IR thePER is error floored at 10% PER.

Figure 10 shows the result of our MIMO MMSEinterference rejection approach when the perfect channelestimation requirement is removed, and we use amaximum likelihood estimation of the channel based onobservations of length kL1 seconds. In this case k is aninteger and L, is 4 micro-seconds. As the length oftraining period increases, the PER performanceapproaches that of a system with perfect Channel StateInformation (PCSI).

Figure 10,. 3x4 un-Coded system, 16QAM, Frequency FlatFading

16QAM Rate-1/2 BCC, Channel D w/ PSAMNLTF = 4

10°

10 : : : : : ' :

LU 1

PSAM Ref. w/o Interference, 3x4SIR =25dBSIR =25 dB w/ Interference SuppressionSIR=25dB= 1 r

10-3 SIR= 15 dB w/ Interference Suppression-*-SIR = 5 dB -A

_-SIR = 5 dB w/ Interference Suppression XtXSIR= 0dB XSIR = 0 dB w/ Interference Suppression -

-4 - PSAM Ref. w/o Interference, 4x410

10 12 14 16 18 20SNR, dB

22 24 26 28

Figure 11. 16QAM Rate-1/2 BCC, Channel D w/ PSAMNTLF = 4.

In Figure 11, we applied our proposed ML-channelestimation and MMSE-IR filter to the end to end system

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3x4 un-Coded System, 1 6QAM, Frequency Flat Fading, PCSI

rrLU0-

operating over a frequency selective channel with rmsdelay spread of 50 ns. As SIR increases, the performanceof MMSE-IR (Interference Rejection) is approaching theregular 4x4 MMSE curve. This is contributed by the factthat interference stream is as strong as the signal stream.

5. CONCLUSIONS

This paper presents a step towards addressing the needsof military based MIMO communication systems. Inparticular we have shown two means of addressing highlevels of mobility that are typical of airborne platforms.Additionally, we have quantified the performance ofMIMO based techniques in improving the covertness(LPD) properties of the communications, as well asproviding anti-jam properties through a joint channel,plus interference MMSE solution. The application ofMIMO to the needs of military communications posesmany significant design challenges that have hithertoreceived very little attention.

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[18] IEEE 802.1 1n subcommittee. EWC draft standard

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