239 research outputs found
Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems
In this paper, we propose a MIMO receiver algorithm that exploits {\em
channel hardening} that occurs in large MIMO channels. Channel hardening refers
to the phenomenon where the off-diagonal terms of the matrix
become increasingly weaker compared to the diagonal terms as the size of the
channel gain matrix increases. Specifically, we propose a message
passing detection (MPD) algorithm which works with the real-valued matched
filtered received vector (whose signal term becomes ,
where is the transmitted vector), and uses a Gaussian approximation
on the off-diagonal terms of the matrix. We also propose a
simple estimation scheme which directly obtains an estimate of (instead of an estimate of ), which is used as an effective
channel estimate in the MPD algorithm. We refer to this receiver as the {\em
channel hardening-exploiting message passing (CHEMP)} receiver. The proposed
CHEMP receiver achieves very good performance in large-scale MIMO systems
(e.g., in systems with 16 to 128 uplink users and 128 base station antennas).
For the considered large MIMO settings, the complexity of the proposed MPD
algorithm is almost the same as or less than that of the minimum mean square
error (MMSE) detection. This is because the MPD algorithm does not need a
matrix inversion. It also achieves a significantly better performance compared
to MMSE and other message passing detection algorithms using MMSE estimate of
. We also present a convergence analysis of the proposed MPD
algorithm. Further, we design optimized irregular low density parity check
(LDPC) codes specific to the considered large MIMO channel and the CHEMP
receiver through EXIT chart matching. The LDPC codes thus obtained achieve
improved coded bit error rate performance compared to off-the-shelf irregular
LDPC codes
Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models
In this paper, we deal with low-complexity near-optimal
detection/equalization in large-dimension multiple-input multiple-output
inter-symbol interference (MIMO-ISI) channels using message passing on
graphical models. A key contribution in the paper is the demonstration that
near-optimal performance in MIMO-ISI channels with large dimensions can be
achieved at low complexities through simple yet effective
simplifications/approximations, although the graphical models that represent
MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1)
use of Markov Random Field (MRF) based graphical model with pairwise
interaction, in conjunction with {\em message/belief damping}, and 2) use of
Factor Graph (FG) based graphical model with {\em Gaussian approximation of
interference} (GAI). The per-symbol complexities are and
for the MRF and the FG with GAI approaches, respectively, where
and denote the number of channel uses per frame, and number of transmit
antennas, respectively. These low-complexities are quite attractive for large
dimensions, i.e., for large . From a performance perspective, these
algorithms are even more interesting in large-dimensions since they achieve
increasingly closer to optimum detection performance for increasing .
Also, we show that these message passing algorithms can be used in an iterative
manner with local neighborhood search algorithms to improve the
reliability/performance of -QAM symbol detection
Space time transceiver design over multipath fading channels
Imperial Users onl
Code optimization and analysis for multiple-input and multiple-output communication systems
Design and analysis of random-like codes for various multiple-input and multiple-output communication systems are addressed in this work. Random-like codes have drawn significant interest because they offer capacity-achieving performance. We first consider the analysis and design of low-density parity-check (LDPC) codes for turbo multiuser detection in multipath CDMA channels. We develop techniques for computing the probability density function (pdf) of the extrinsic messages at the output of the soft-input soft-output (SISO) multiuser detectors as a function of the pdf of input extrinsic messages, user spreading codes, channel impulse responses, and signal-to-noise ratios. Using these techniques, we are able to accurately compute the thresholds for LDPC codes and design good irregular LDPC codes. We then apply the tools of density evolution with mixture Gaussian approximations to optimize irregular LDPC codes and to compute minimum operational signal-to-noise ratios for ergodic MIMO OFDM channels. In particular, the optimization is done for various MIMO OFDM system configurations which include different number of antennas, different channel models and different demodulation schemes. We also study the coding-spreading tradeoff in LDPC coded CDMA systems employing multiuser joint decoding. We solve the coding-spreading optimization based on the extrinsic information SNR evolution curves for the SISO multiuser detectors and the SISO LDPC decoders. Both single-cell and multi-cell scenarios will be considered. For each of these cases, we will characterize the extrinsic information for both finite-size systems and the so-called large systems where asymptotic performance results must be evoked. Finally, we consider the design optimization of irregular repeat accumulate (IRA) codes for MIMO communication systems employing iterative receivers. We present the density evolution-based procedure with Gaussian approximation for optimizing the IRA code ensemble. We adopt an approximation method based on linear programming to design an IRA code with the extrinsic information transfer (EXIT) chart matched to that of the soft MIMO demodulator
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