Tsoulos George (ред.) MIMO System Technology for Wireless Communications

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304 MIMO System Technology for Wireless Communications

On-site conﬁgurability: A testbed is often very ﬂexible in terms of system

parameters and algorithm components. However, this ﬂexibility is of limited

use if it cannot be exercised quickly and with little effort through a GUI that

centralizes the conﬁguration of system parameters. Similarly, in some cases

a transmission may require handling the data ﬂow in sequential steps, and

more often than not, it is useful and even necessary to repeat measurements

in a same setting with different parameters. A GUI can greatly facilitate all these

tasks by providing dialog boxes for manual parameter input as well as push

buttons for automated script execution of commonly repeated transmission

and/or conﬁguration sequences.

Adequate display for demonstration purposes: Exhibiting the testbed to spon-

sors and visitors is very important for the continuous development of the

testbed. Unfortunately, people who see a demonstration of the testbed often

do not have a strong background in theory or implementation of wireless

communication systems. This suggests that a GUI designed solely for mon-

itoring and controlling the testbed at a technical level may not be adequate

for demonstration purposes. Therefore, the GUI should have a mode in

which transmissions can be controlled and results can be observed at a

reduced technical level. A good GUI design and development takes signiﬁ-

cant time; however, it pays off in the long run.

Figure 10.21 illustrates the GUI developed for the narrowband MIMO

testbed. The GUI displays a variety of information at different technical

depths, and it is used for both demonstration and ﬁeld test monitoring. Some

of the components in the GUI are very helpful for troubleshooting. For

instance, the bar plots of the channel powers can help identify which part

of the hardware chain has failed when something is not working as expected.

Tabbed windows are used to extend the display size, and it is especially

useful to switch between information at different technical levels.

10.6 Field Test Results

A variety of tests and measurements can be performed with the testbed

based on its capabilities. This section has a discussion on the metrics used

to measure MIMO system performance followed by some ﬁeld test results

using two different testbeds. Some of these metrics measure the quality of the

wireless channel to sustain MIMO communications, and others measure

the performance of the communication system.

10.6.1 Measuring the Quality of the MIMO Channel

The information theoretic capacity of a MIMO communications scheme is a

function of the average received SNR and the channel matrix. For a given

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Multi-Antenna Testbeds for Wireless Communications 305

SNR, the “quality” of the channel matrix determines the achievable capacity.

This can be analyzed by decomposing the channel matrix using the singular

value decomposition (SVD) [23–27].

(10.8)

U and V are unitary matrices and UU

= VV

= I. This can be simpliﬁed to

(10.9)

represents the singular values of channel matrix H. k f min(M, N) is the

rank of the channel matrix H. From Equation 10.9 it can observed that the

total capacity of a MIMO channel is the sum of parallel AWGN SISO sub-

channels, each of which has a gain given by their corresponding singular

values (or power given by their corresponding eigenvalues). When the SNR

FIGURE 10.21

The graphical user interface of the narrowband testbed.

HUV

CE I



88log det



bits/sec/Hz



log

X bits/sec/HHz

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306 MIMO System Technology for Wireless Communications

is very low, the roughly linear increase in capacity expected with MIMO

systems may not be achieved. Similarly, when there is severe fading corre-

lation, the minimum singular value could be very small thereby signiﬁcantly

reducing the achievable capacity [28,29]. Since the capacity of a given channel

can be expressed in terms of its singular values, these can in turn be used

to measure the “quality” of the channel matrix. One popular metric is the

effective degrees of freedom (EDOF) [28–30].

10.6.2 Effective Degrees of Freedom (EDOF)

Effective degrees of freedom (EDOF), introduced in [28], is a metric that

represents the number of MIMO subchannels actively participating in con-

veying information under a given set of operating conditions. EDOF gives

an indication of the capacity gain achievable in a MIMO channel. For a SISO

channel, a G-fold increase in the transmitter power results in an increase in

the channel capacity of log

G bits/sec/Hz. If a MIMO channel is equal to

EDOF SISO channels in parallel, a G times increase in the transmit power

should increase the capacity of the system by (EDOF*log

G) bits/sec/Hz.

EDOF is deﬁned at a given transmit power and outage probability.

(10.10)

The capacity at outage q is evaluated for a I increase in SNR. EDOF is a

real number in [0, k] where k is the rank of the channel matrix H. Equation

10.10 was analytically solved to get another convenient means to compute

EDOF in [30]. This is given in Equation 10.11.

(10.11)

Although EDOF was deﬁned at an outage probability, it can also be deﬁned

as the average EDOF (EDOF averaged over all the channel realizations) to

indicate the average capacity gain achievable in the MIMO system [31]. The

EDOF decreases as fading correlation increases. Similarly EDOF decreases

in a LOS channel compared to a rich scattering NLOS channel. In fact, in a

LOS channel with a dominant propagation path with very few scatterers it

could be close to 1.

10.6.3 Analysis of Field Test Measurements

The 5.25 GHz 2 × 2 MIMO system was deployed in an indoor quasi-static

environment, and the performance gain of the communication system as a

EDOF

()2

EDOF



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Multi-Antenna Testbeds for Wireless Communications 307

function of the number of antennas was measured. The same set of meas-

urements was used to measure the capacity gain with the 2 × 2 MIMO system

compared to the SISO system and the EDOF of the environment. The wireless

measurements were carried out in a laboratory environment. The transmitter

and receiver were located in the same room of the ICSL and, thus, a line of

sight existed between them. The distance between them was kept constant

at about 6–7 m. The total transmit power was ﬁxed at –3 dBm (0.5 mW) for

all the experiments. 3dB attenuators were placed before the antennas for

MIMO 2 × 2 to keep the total transmit power constant at –3 dBm. A second

set of measurements was conducted with the total transmit power ﬁxed at

–10 dBm. The transmitter was moved in steps larger than the wavelength in

order to get independent channel realizations. 200 distinct measurements for

each of SISO, 1 × 2 and 2 × 2 setups were conducted.

Figure 10.22 plots the CDF of slicer SNR comparing 1 × 1, 1 × 2, and 2 × 2

systems. The same plot also has simulation results with identical amounts

of Carrier frequency offset, I/Q mismatch, and phase noise added to mimic

the real system. It can be observed that the simulation results closely match

with the real measurements. The Slicer SNR for a 2 × 2 system is 3dB worse

than a SISO system, since the transmit power is uniformly distributed among

both the transmit antennas. Figure 10.23 plots the theoretical capacity using

the estimated channels along with the capacity using the post-processing

SNR (slicer SNR), which indicates the capacity that can be achieved with

this communication system. The difference between the two indicates the

capacity loss due to the implementation loss of the system.

FIGURE 10.22

CDF of slicer SNR for LOS channels with excess I/Q mismatch at Tx 1 ﬁxed.

CDF of slicer SNR for LOS paths(ee54116)

for SISO, SIMO 1 × 2, and MIMO 2 × 2

SISO

0.9

0.8

0.7

0.6

0.5

Probability

(

slicer SNR ≤ abscissa)

0.4

0.3

0.2

0.1

0 5 10 15 20

Slicer SNR

25 30 35 40 45

Simulation

Measurements

MIMO

SIMO

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308 MIMO System Technology for Wireless Communications

The average capacity gain observed with the communication system is

plotted in Figure 10.24. The capacity computed using the estimated normal-

ized channels is plotted along with the average achievable capacity (capacity

computed using slicer SNR). This shows that there is a capacity loss of about

2.5 bps/Hz in this communication system using the measurements made at

a received SNR of about 22 dB (when the transmit power was ﬁxed at

–10 dBm), and the capacity loss grew to 4 bps/Hz when the received SNR

increased to approx 28 dB (when the transmit power was ﬁxed at –3 dBm).

This was mainly due to the non-linearities in the RF frontend at the higher

power. The capacity loss in a SISO system is much less than the MIMO

system because the impact of analog impairments such as I/Q mismatch is

much less in a SISO system than in a MIMO system. We also observe a linear

increase in achievable capacity with the MIMO system.

Table 10.4 lists the capacity and capacity gain observed along with the

computed EDOF in the LOS channels. The theoretical MMSE capacity gain

was also computed to be 1.73. The average EDOF using the estimated chan-

nels was measured to be 1.9, and the observed capacity gain was 1.67.

10.6.4 Channel Impact on Space–Time Coding

The pioneering work by Foschini and Gans [23] and Telatar [24] showed that

multiple antennas in a wireless communication system could greatly

FIGURE 10.23

CCDFs of capacity for MIMO, SIMO, and SISO in LOS channels.

MIMO 2 × 2

Capacity CCDFs using estimated channels

and slicer SNR in LOS channels

SIMO 1 × 2

SISO

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 5 10

Capacit

b/s/Hz

15 20 25

Capacity loss due to

implementation loss

Probability(capacity ≥ abscissa)

eoretical capacity

Slicer capacity

MIMO 2 × 2

SIMO 1 × 2

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Multi-Antenna Testbeds for Wireless Communications 309

improve performance. For M transmit antennas and N receive antennas in

Rayleigh fading, it was shown that with spatial independence, there are

essentially M*N levels of diversity available and there are min(M, N) inde-

pendent parallel channels that could be established. These information

theoretic results spawned two lines of work: one to increase throughput by

exploiting spatial multiplexing [34] and one to improve error performance

by exploiting spatial diversity [35,36]. The code design and performance

analysis paradigm in the area typically assume a spatially white Rayleigh

fading channel. However, a real channel could neither be Rayleigh nor be

spatially white.

Hence, it is of great interest to test and compare the performance of the

space–time codes in a realistic environment. This section reports on a series

FIGURE 10.24

Capacity vs. SNR curves overlaid with measured average capacity values for LOS measurements.

TABLE 10.4

Capacity Gain in LOS Channels at 20 dB SNR

Average capacity 11.30 bps/Hz

Capacity at 10% outage 8.64 bps/Hz

Average EDOF 1.9

EDOF at 10% outage 1.77

Observed capacity gain 1.67

Observed capacity gain at 10% outage 1.57

Theoretical slicer capacity gain for MMSE MIMO decoder at 20 dB SNR 1.73

Capacity

its/sec/Hz

0 5 10 15

Received S

20 25 30 35

1 × 1

1 × 2

2 × 2

1 × 1

1 × 2

MMSE post

processing

capacity

eoretical

capacity

Second set of

measurements

First set of

measurements

1 × 2 eoretical

1 × 1 eoretical

2 × 2 Slicer capacity

1 × 2 Slicer capacity

1 × 1 Slicer capacity

2 × 2 eoretical

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310 MIMO System Technology for Wireless Communications

of experiments whose goal is to evaluate the impact of antenna separation

on code performance for each of these lines of work. Two types of signaling

are considered in this section: (1) multiplexing type schemes (space–time

constellations and precoding) and (2) space–time codes designed to harvest

diversity or performance.

The experimentation was done on the UCLA campus and the surrounding

West Los Angeles area using the UCLA Narrowband real-time multi-antenna

DSP testbed. The testing reported in this section was limited to the scenario

where the transmitter was deployed on the top of a ﬁve-story building and

the receiver on a vehicle (a van). The test consisted of the receiver radio

being driven around the campus area. The speed of the driving was main-

tained at a rate of less than 5 miles per hour as the space–time trellis codes

that were tested were all designed for quasi-static fading. The UCLA campus

area is heavily urbanized, and a line of sight was not achieved in any

signiﬁcant portion of the testing. An example of the testbed deployment is

shown in Figure 10.25.

Three antenna conﬁgurations are considered: (1) 2 Q spacing at the trans-

mitter with 0.5 Q spacing at the receiver, (2) 2 Q spacing at the transmitter

with 0.25 Q spacing at the receiver, and (3) 1 Q spacing at the transmitter

with 0.25 Q spacing at the receiver. For the case of precoding and space–time

constellations the Alamouti [37] coding scheme has the most robustness to

different antenna geometries. All constellations considered here use R = 4 bits

per symbol and the comparison for frame error rate is in Figure 10.26 for

M = 2 and N = 2. The Alamouti code must use a 16 QAM constellation to

achieve this rate while spatial multiplexing and the threaded architectures

can use QPSK constellations. When the spacing of the array is larger (closer

FIGURE 10.25

Test deployment.

TX deployment

(a)

(b)

RX deployment

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Multi-Antenna Testbeds for Wireless Communications 311

to spatial independence) the Golden Code [38] has the advantage due to the

smaller constellation point differences. As the spacing become closer and the

channels become correlated, the Alamouti code has the advantage. Direct

spatial multiplexing with a BLAST-like architecture has clearly lower per-

formance than either of the two considered architectures and does not

achieve full diversity. For the case of trellis codes, the universal code has the

most robustness to different antenna geometries. All constellations consid-

ered here use a R = 2 bits per symbol, and the comparison for frame error

rate is in Figure 10.27 for M = 2 and N = 2. The universal code has good

performance in most cases with moderate degradation due to spatial corre-

lation. The super-orthogonal space–time trellis codes [39,40] and CYV [41]

codes show more signiﬁcant degradation due to spatial correlation. For more

detailed results, refer to [42].

FIGURE 10.26

FER of 2 × 2 spatial multiplexing and Alamouti schemes.

−1

−2

−3

−4

Blast, QPSK, antenna separation 1

Blast, QPSK, antenna separation 2

Blast, QPSK, antenna separation 3

Golden, QPSK, antenna separation 1

Golden, QPSK, antenna separation 2

Golden, QPSK, antenna separation 3

Alamouli, 16 QAM, antenna separation 1

Alamouli, 16 QAM, antenna separation 2

Alamouli, 16 QAM, antenna separation 3

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312 MIMO System Technology for Wireless Communications

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