Proximal Decoding for LDPC-coded Massive MIMO Channels

We propose a novel optimization-based decoding algorithm for LDPC-coded massive MIMO channels. The proposed decoding algorithm is based on a proximal gradient method for solving an approximate maximum a posteriori (MAP) decoding problem. The key idea is the use of a code-constraint polynomial penali...

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Bibliographic Details
Published in:2021 IEEE International Symposium on Information Theory (ISIT) pp. 232 - 237
Main Authors: Wadayama, Tadashi, Takabe, Satoshi
Format: Conference Proceeding
Language:English
Published: IEEE 12.07.2021
Subjects:
Approximation algorithms
Decoding
Detection algorithms
Detectors
Gradient methods
Linear programming
Massive MIMO
Online Access:Get full text
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Summary:We propose a novel optimization-based decoding algorithm for LDPC-coded massive MIMO channels. The proposed decoding algorithm is based on a proximal gradient method for solving an approximate maximum a posteriori (MAP) decoding problem. The key idea is the use of a code-constraint polynomial penalizing a vector far from a codeword as a regularizer in the approximate MAP objective function. The code proximal operator is naturally derived from code-constraint polynomials. The proposed algorithm, called proximal decoding, can be described by a simple recursion consisting of the gradient descent step for a negative log-likelihood function and the code proximal operation. Several numerical experiments show that the proposed algorithm outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding.
DOI:10.1109/ISIT45174.2021.9517988