Influence of channel fading correlation on performance of detector algorithms for v blast architecture

David C. Wyld (Eds) : ICCSEA, SPPR, CSIA, WimoA, NECO - 2013
pp. 359–367, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3537
INFLUENCE OF CHANNEL FADING
CORRELATION ON PERFORMANCE OF
DETECTOR ALGORITHMS FOR V-BLAST
ARCHITECTURE
Bindu. E1
and B.V. R. Reddy2
1
Research Scholar, GGSIP University ,
Dept. of ECE, Amity School of Engg and Tech., New Delhi, India
2
Professor, USICT,
GGS Indraprastha University, New Delhi, India
ABSTRACT
This paper analyzes the impact of fading correlation and cross polarization coupling on the
error performance of V-BLAST MIMO system that employs detector algorithms like ZF, MMSE
and ML with ordering and successive cancellation. Simulation results show the BER
performance of these detectors for different modulation schemes. It is observed that lesser the
channel fading correlation and cross polarization coupling values better is the performance of
these detectors. Study is extended to see the effect of transmit and receive antenna correlation
on Ergodic MIMO capacity.
KEYWORDS
MIMO, V-BLAST, ZF, MMSE, ML, fading correlation, cross polarization coupling
1. INTRODUCTION
Multiple input multiple output (MIMO) system employs multiple transmit and receive antennas to
provide high data rates without using additional bandwidth and power. In a rich scattering
environment, the capacity increases linearly with the number of antennas without increasing the
transmission power. This results in the possibility of transmitting at a higher data rate, by using
spatial multiplexing (SM). Multiplexing gain is defined as the number of sub channels of the
MIMO channel asymptotically viewed as a parallel channel. Due to this the corresponding
transmission rate is multiplied by the number of transmit antennas, thereby increasing gain. With
N receive antennas for M (where M ≤ N) simultaneously transmitted data streams through
independent flat Rayleigh channels, the M streams of data can be separated by using a zero-
forcing (ZF) scheme, and d = (N−M+1) path diversity can be achieved by each of the M streams
[6]. But in most practical MIMO systems, independent channels are difficult to achieve. Layered
Space Time (LST) codes can provide multiplexing gain and are spectrally efficient and hence
suitable to realize SM. Channel fading and additive noise are main problems faced by MIMO

360 Computer Science & Information Technology (CS & IT)
LST receiver along with Multistream Interference (MSI). MSI arises due to the interaction of data
streams with each other and this interference can be minimized through correct choice of detector
algorithms [2]. Bell Lab Layered Space-Time (BLAST) detection algorithms combine linear
(interference suppression) and nonlinear (serial cancellation) detection methods. Combination of
Optimal Ordering with Successive Cancellation (SUC-OO) algorithms with Zero Forcing (ZF),
Minimum Mean Square (MMSE) or Maximum Likelihood (ML) receivers can minimize Multi-
stream interference [1].
For theoretical analysis, the channel is modeled as flat fading Rayleigh independent identically
distributed (i.i.d) Gaussian variable. The antennas are assumed to provide independent channels
for the received signals. However, in real propagation environment, the fades are not independent
due to insufficient spacing between antenna elements. When the fades are correlated, the channel
capacity decreases [9]. Presence of Line-of-Sight (LOS) component also makes the channel
deviate from Rayleigh i.i.d. Another Theoretical assumption of identical polarization for both
transmitting and receiving antenna also fails in practical scenario, due to Cross Polarization
effect. This phenomenon can also lead to high bit error rate (BER)[2]. This aspect along with the
impact of fading correlation on different detection schemes is studied in this paper.
Organization of the paper is as follows: Section II explains the basic MIMO channel model and
its capacity along with the effect of fading correlation and cross polarization on the complex
channel matrix H. In section III, VBLAST architecture with the receiver algorithms like ML, ZF
and MMSE, with and without SUC-OO are discussed. Section IV discusses the simulation results
followed by conclusion in section V.
2. MIMO CHANNEL MODE
In MIMO system with M transmitting and N receiving antenna, the received signal vector y is
y = Hx + n (1)
where H is channel matrix of dimension MxN with complex coefficients for Rayleigh i.i.d flat
fading channel and n is a wide sense stationary (WSS) noise vector with i.i.d. components and
variance of σ2
.The advantage of using MIMO system is the increased channel capacity it can
provide for the same signal conditions. The outage capacity of MIMO system having M=N
antennas and for channel unknown to the transmitter is given as [1]
C= M log2 (1+ρ) bits/s/Hz (2)
Fig. 1. One-ring model (TA -transmitting antenna, RA- receiving antenna , S(θ)- the scatterer at angle θ, δ-
angle spread, D- the distance from object X to Y) [9]

Computer Science & Information Technology (CS & IT) 361
where ρ=Eb/No. The capacity of an orthogonal MIMO channel is therefore M times the scalar
channel capacity.
Performance of MIMO system may deteriorate due to various factors, which influence the
channel matrix H. Fading correlation due to closely spaced antenna and presence of LOS
component due to poor scattering environment causes deviation of channel model from pure
Rayleigh i.i.d. Cross polarization coupling between the antennas can also alter the channel matrix.
Correlation problems arise because of the small separation distance between the antenna elements
at the Base Station (BS). Fig.1 shows the “one-ring” model [9], which is used to determine spatial
fading correlation of H. Due to the kind of geometry, all antennas will receive the same signal,
giving rise to correlation between them. The normalized mutual correlation coefficient between
any pair of antennas is [6]
{ } ( )











== j,idJhhEr R
o
*
jninij
λ
π2
(3)
where (·)* denotes complex conjugate, E{·} denotes statistical expectation, J0(·) is the zero order
Bessel function of the first kind, λ is the wavelength, and dR
(i,j) is the displacement between
receive antennas i and j. Because of this, the elements of the channel matrix H become [2]
1/2
tω
1/2
r RHR=H (4)
where Rr and Rt are the receive and transmit covariance matrices (positive definite Hermitian
matrices) of order NxN and MxM respectively. Hω is a Rayleigh i.i.d complex Gaussian matrix
with zero mean, unit variance entries. The correlation matrix R with three receive antenna is










=
1
1
1
R
3231
2321
1312
rr
rr
rr
where rij is normalized mutual correlation coefficient between any pair of antenna, r
=E{hhH
}, where h is channel vector, h=vec[H] [6]. Assuming receiver and transmitter matrix are
of full rank, then at high SNR, the capacity can be approximated as
( ) ( )t2r2
H
ωω2 RdetlogRdetlogHHdetlog ++
M
ρ
=C 





(5)
As the equation shows, fading channel correlation reduces the outage channel capacity by
[log2det(Rr)+ log2det(Rt)] bits/s/Hz.
Fig.2 V-BLAST architecture [7]

The loss caused by transmit and receive correlation is independent, the loss due to receive
correlation is determined by the determinant of the receive correlation matrix and the numbers of
receive antenna, and the loss caused by transmit correlated fading is determined by the transmit
correlation matrix and the modulation scheme [9].
The use of antennas having orthogonal polarization at the transmitter and receiver leads to a gain
and correlation imbalance between elements of H. In reality, perfect polarization cannot be
achieved, leading to cross polarization coupling [4]. The coupling factor α (0 ≤ α ≤ 1) gives a
measure of how well the antenna discriminates the polarization from other antenna. Assuming a
Rayleigh i.i.d channel, the channel H with cross-polarized antenna can be modeled as [2]
( )








1
1
whereRHR 1/2
tω
1/2
r
α
α
=ββΘ=H (6)
Where is Hadamard product. The capacity for a 2x2 system in non-scattering environment is
given as
( ) ( )ρ+log=C/ρ+=C α=α= 21and212log 2120 (7)
3. LAYERED SPACE TIME RECEIVERS
The Vertical BLAST (V-BLAST) architecture is indicated in fig.2, for M transmitter N receiver
setup operating coherently at each symbol time T, with transmission organized in bursts of L
symbols. LST decoding suffers from Multistream Interference (MSI) in which the multiple
streams superimpose over one another at the receiver, making the detection difficult. So choice of
receiver becomes important to exploit the maximum potential of SM through LST. The three
most common receivers are ML, ZF and MMSE. These schemes can be combined with nonlinear
detection schemes such as successive cancellation (SUC)
Fig.3 illustration of SUC algorithm
and Optimally Ordered SUC algorithms to enhance the performance. Fig.3 shows SUC algorithm.
In SUC-OO , the signal with the strongest SINR is selected for processing at the first stage. This
improves the quality of the decision and reduces the chances of error propagation [8]. The various
receiver models are described as follows:

i ) ML Receiver:
This is an optimum receiver, which solves for the estimated symbol vector xˆ
2
HxyminargxˆML −= (8)
by searching through all the vector constellation for the most probable transmitted signal vector.
The computational complexity is very high as M increases (xM
) but provides full receiver
diversity and zero power losses [2].
ii ) ZF V-BLAST Receiver:
The ZF receiver is a linear receiver. This technique nullifies the interference by using weight
matrix WZF as follows [3]
( ) ZFZFZF n~xyyWˆ +===
−
HHHx
1H
(9)
{ } ∑=
=
M
i i
n
ZFn~
1
2
2
2
ispowernoiseAverage
σ
σ
Ε (10)
This noise in the separated streams are correlated and consequently the SNRs are not independent.
The ZF receiver decomposes the link into M parallel streams, each with diversity gain and array
gain proportional to N-M+1. Hence, it is sub optimum. Recursive SUC-OO Zero Forcing
algorithm, which is based on performing the nulling operation using unitary transformations
results in a lower complexity and better numerical stability [2]. The recursion determines the
elements of the optimal row, which maximizes the Bit Error Rate (BER).
iii ) MMSE V-BLAST Receiver:
To reduce the impact from the background noise, the MMSE detector employs a linear filter that
can take into account the noise. The estimated signal is
( )[ ] MMSEnMMSEMMSE n~x~ˆ +=+==
−
yHIHHyWx H12H
σ (11)
{ } ( )
∑
+
=
=
M
i
in
in
MMSEn~
1
222
22
2
ispowernoiseAverage
σσ
σσ
Ε (12)
The SUC-OO also can be combined with the MMSE detection method to improve the
performance. The MMSE receiver can minimize the overall error caused by noise and mutual
interference between the cochannel signals, but this is at the cost of separation quality of the
signals. The ZF receiver on the other hand, perfectly separates the cochannels’ signals at the cost
of noise enhancement.

4. RESULTS AND DISCUSSION
In this section, the performance of various V-BLAST receiver schemes are studied with their
BER variation against input signal to noise ratio for different passband modulation techniques.
Effects of fading correlation and cross polarization at the antenna on detection are also
considered. Study is carried out on effect of antenna spacing and Angle Spread (AS) on spatial
correlation. Dropping of Ergodic Capacity for 2x2, 3x3, and 4x4 MIMO system under presence
of transmit correlation is simulated. Assumptions made for simulation of MxN MIMO system are
as follows:
• The transmitted signal has a fixed and narrow bandwidth with a flat fading channel model with
receiver noise assumed as AWGN.
• The transmitted power is independent of the number of transmit antennas
• The modulation schemes used in the simulation are Mary Phase Shift Keying and Quadrature
Amplitude Modulation since these schemes are good indicators of system performance.
Fig.4 indicates the different VBLAST receiver algorithms discussed in this paper for 2x2 MIMO.
The ML algorithm gives best BER for given SNR, but the computation complexity increases in
the order of xM
. Lowest BER is obtained BPSK modulation. The plot also indicates better
performance of MMSE over ZF detection algorithm. This is because, the MMSE receiver can
minimize the overall error caused by noise and mutual interference between the cochannel
signals, but at the cost of separation quality of the signals [5]. For a BER of 10-3
, difference in
Eb/No between ML and MMSE-SUC with Optimal Ordering is approximately 1dB where as the
other schemes require signal strength of approximately 10dB more to achieve the same BER.
Fig.5 shows how the cross polarization coupling affects the detector performance. ZF SUC
detector is considered with BPSK modulation. For very low
Fig.4 Performance comparison of V-BLAST ML, ZF and MMSE Receivers with and without Successive
Cancellation and Optimal Ordering for fading correlation=1 and α=0.5

Fig.5 V-BLAST ZF Receiver performance for different cross polarization coefficient values (α)
Fig.6 V-BLAST ZF Receiver performance for different correlation coefficient values for BPSK and QPSK
modulation, cross polarization coefficient value (α)=0.5
Fig.7 V-BLAST MMSE Receiver performance for different fading correlation values for BPSK and QPSK
modulation

Fig.8 Variation of Spatial Correlation Coefficient with d/λ for different angular Spread (AS)
Fig.9 Effect of Correlation on Channel Ergodic Capacity for different MIMO configurations
values of Eb/No, the variation of α is not prominent but as Eb/No increases, the effect is more
visible. For a BER of 10-2
, the difference is in the order of 2-3dB. α=1 implies that the antennas
fail to discriminate between each other’s signals. This give rise to a downfall in performance as
indicated in the result. Numerical results show that for high Cross Polarization Discrimination
(XPD) values, single polarized antennas perform better.
Effect of fading correlation coefficient on V-BLAST ZF and MMSE SUC detector is shown in
fig.6 and fig.7. Both plots show the general trend for other detectors also. For both BPSK and
QPSK, best performance is achieved for zero correlation. If coefficient equals to unity, by (5), the
H matrix will become rank 1 and the channel will reduce to SISO [2]. So lower the correlation,
the better. At BER=10-2
, the average difference between Eb/No is approximately 2dB.
Fig.8 shows the variation of spatial correlation coefficient with antenna element spacing (d/λ) for
different angular spreads. As seen, power distribution becomes broad over large angle as AS

increases, which decreases the correlation coefficient at the same antenna spacing [8]. The
dependency of Ergodic MIMO capacity with correlation between transmit and receive antenna is
indicated in Fig.9. For 4x4 MIMO at SNR of 20dB, the loss is 3.3bps/Hz, for 3x3 system, loss
reduces to 2.2bps/Hz and for 2x2 system, it is 1bps/Hz. At 10dB SNR, the corresponding values
are approximately 2, 1, and .6bps/Hz respectively.
5. CONCLUSION
The impact of receiver fading correlation on the error performance of MIMO systems that employ
different V-BLAST receiver algorithms are studied in this paper. It is shown that ML detector
gives optimum performance with very high computation complexity.MMSE with Successive
Cancellation and Optimum Ordering gives lower BER values, but with a tradeoff in complexity.
ZF with Successive Cancellation has lower computational complexity but require more signal
power to deliver same error rate. For all these detectors, lower correlation results in better
performance. The study is also extended to the effect of cross polarization coupling on the
detectors. It is observed that lower coupling value results in lower bit error rate given by the
detectors. It is also observed that the Ergodic MIMO capacity reduction is more prominent for
larger dimension of MIMO channel matrix.
REFERENCES
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[3] Zhongpeng Wang,” Grouped Linear ZF Receiver for Multiple Antenna Systems”, Proceedings of
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[4] R.Nabar, H Bolcskei, V Erceg, D Gesbertand A Paulraj, ”Performance of Multiantenna Signaling
Techniques in the Presence of Polarization Diversity”, IEEE Trans. Sig. Proc., Vol 50, Issue 10, 2002,
pp 2553-2562
[5] Chien-Jen Huang, Chung-Wen Yu, and Hsi-Pin Ma,” A Power-Efficient Conﬁgurable Low
Complexity MIMO Detector” IEEE Transactions on Circuits and Systems I, Vol. 56, No. 2, Feb.
2009, pp 485-496
[6] The Huaping Liu, Yongzhong Song, and Robert C. Qiu,” The Impact of Fading Correlation on the
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Communication, Vol.4, No.5, Sept. 2005. pp 2014-2019
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[8] Y S Cho, J Kim, W Y Yang, CG Kang, ”MIMO-OFDM Wireless Communications with MATLAB”,
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