Journal of Communications and Information Networks, Vol.3, No.2, Jun. 2018
DOI: 10.1007/s41650-018-0014-5 Research paper
Non-Linear and Non-Iterative Based Transceiver
Design for SU-MIMO Systems
Raja Muthalagu
Abstract—This paper considers the design of a
low-complexity and high-performance precoder for
multiple-input multiple-output (MIMO) systems. The
precoder is designed by combining both nonlinear and
non-iterative processing strategies. The proposed nonlin-
ear precoding techniques employ a nonlinear constellation
precoding technique based on maximum distance sepa-
rable codes at the transmitter. We propose to reduce the
computational complexity in iterative-based precoding
algorithms by using less complex non-iterative singular
value decomposition-based joint precoder and decoder
pair design. The maximum likelihood detection for the lin-
ear MIMO channel is considered. The simulation results
showed that the proposed nonlinear and non-iterative
precoding schemes outperform the conventional linear
MIMO precoder design, even when a reduced-complexity
suboptimal strategy is adopted, considering the bit error
rate performance.
Keywords—multiple-input multiple-output, singular
value decomposition, maximum distance separable codes,
subcarrier grouping, diversity channel selection
I. INTRODUCTION
I
n the last few decades, multiple-input multiple-output
(MIMO) systems have emerged as an important technol-
ogy amongst the methodologies known to guarantee a high
data rate in wireless communication systems. The perfor-
mance improvement of the MIMO systems in terms of either
the link reliability or data throughput depends on the assump-
tion of the availability of channel state information (CSI) at
the transmitter (CSIT) and/or that of the state information at
the receiver (CSIR). Obtaining the correct CSIT or CSIR in
Manuscript received Aug. 14, 2017; accepted Nov. 06, 2017. The asso-
ciate editor coordinating the review of this paper and approving it for publi-
cation was R. S. Kshetrimayum.
R. Muthalagu. Department of EEE, Birla Institute of Technology and
Science (BITS), Pilani, Dubai Campus, Dubai International Academic City,
Dubai 345055, UAE (e-mail: raja.m@dubai.bits-pilani.ac.in).
real time is impossible because of the dynamic nature of the
channel and the channel estimation errors. However, it is im-
portant to outline a system that is sufficient to achieve imper-
fect CSIT and/or CSIR. MIMO systems can be sub-divided
into three fundamental classifications: spatial diversity, spa-
tial multiplexing
[1-3]
, and beamforming
[4-6]
.
In single-user MIMO (SU-MIMO) systems, spatial di-
versity can be obtained through the utilization of space-
time codes
[7,8]
. The transmit beamforming with receive
combining
[9,10]
was one of the simplest methodologies to en-
able spatial multiplexing in SU-MIMO systems to accom-
plish full diversity. Appropriate transmit precoding designs
or joint precoder-decoder designs were proposed under a va-
riety of system objectives and different CSI assumptions
[11]
.
We previously proposed another beamforming method utiliz-
ing singular value decomposition (SVD) for closed-loop SU-
MIMO systems with a convolution encoder and modulation
techniques, for example, M-quadrature amplitude modulation
(M-QAM) and M-phase shift keying (M-PSK) over Rayleigh
fading
[4-6]
.
As design criteria, different performance measures were
considered, for example, weighted minimum mean square er-
ror (MMSE)
[12]
, total mean square error (TMSE)
[13]
, least bit
error rate (BER)
[14]
. From the point of view of signal pro-
cessing, TMSE is a critical metric for transceiver design and
has been embraced in SU-MIMO systems to minimize the in-
formation estimation error from the received signal. A joint
transceiver design utilizing an MSE paradigm was also dis-
cussed for the SU-MIMO framework
[12]
.
The above paragraph provides a general introduction and
addresses a few optimization criteria such as an extreme data
rate, least BER, and MMSE. The design of an optimum lin-
ear transceiver for an SU-MIMO channel, possibly with de-
lay spread, utilizing a weighted MMSE paradigm subject to
a transmit power constraint was reported
[12]
. These studies
assumed that the perfect CSI was available on the transmit-
ter side. However, in practical communication systems, the
propagation environment may be more challenging, and the
receiver and transmitter cannot have perfect knowledge of the
CSI. An imperfect CSI may emerge from an assortment of
sources, for example, outdated channel estimates, erroneous