471 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
Graph-Based Decoding in the Presence of ISI
We propose an approximation of maximum-likelihood detection in ISI channels
based on linear programming or message passing. We convert the detection
problem into a binary decoding problem, which can be easily combined with LDPC
decoding. We show that, for a certain class of channels and in the absence of
coding, the proposed technique provides the exact ML solution without an
exponential complexity in the size of channel memory, while for some other
channels, this method has a non-diminishing probability of failure as SNR
increases. Some analysis is provided for the error events of the proposed
technique under linear programming.Comment: 25 pages, 8 figures, Submitted to IEEE Transactions on Information
Theor
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