1 research outputs found
Efficient low-complexity data detection for multiple-input multiple-output wireless communication systems
The tradeoff between the computational complexity and system performance in multipleinput
multiple-output (MIMO) wireless communication systems is critical to practical applications.
In this dissertation, we investigate efficient low-complexity data detection schemes
from conventional small-scale to recent large-scale MIMO systems, with the targeted applications
in terrestrial wireless communication systems, vehicular networks, and underwater
acoustic communication systems.
In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput
multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput
multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA)
systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition
(SQRD) based turbo equalization scheme under imperfect channel estimation.
We demonstrate the performance enhancement of the proposed scheme over the conventional
minimum mean-square error (MMSE) based turbo equalization scheme in terms of
system bit error rate (BER) and convergence performance. Furthermore, by jointly considering
channel estimation error and the a priori information from the channel decoder,
we develop low-complexity turbo equalization schemes conditioned on channel estimate for
MIMO systems. Our proposed methods generalize the expressions used for MMSE and
MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special
cases. In addition, we extend the SQRD-based soft interference cancelation scheme
to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show
an improved system BER performance of the proposed turbo detection scheme over the
conventional MMSE-based detection scheme.
In the large-scale MIMO scenario, we focus on low-complexity detection schemes because
computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive
MIMO detection schemes. We demonstrate the applicability of the stair matrix through
the study of the convergence conditions. We then investigate the system performance and
demonstrate that the convergence rate and the system BER are much improved over the
diagonal matrix based approach with the same system configuration. We further investigate
low-complexity and fast processing detection schemes for massive MIMO systems where a
block diagonal matrix is utilized in the development. Using a parallel processing structure,
the processing time can be much reduced. We investigate the convergence performance
through both the probability that the convergence conditions are satisfied and the convergence
rate, and evaluate the system performance in terms of computational complexity,
system BER, and the overall processing time. Using our proposed approach, we extend
the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic
(UWA) communications. By utilizing a recently proposed computational efficient statistic
UWA channel model, we show that the proposed scheme can effectively approach the system
performance of the original Gauss-Seidel method, but with much reduced processing delay