Proximal Decoding for LDPC-coded Massive MIMO Channels

TL;DR

This paper introduces a new proximal decoding algorithm for LDPC-coded massive MIMO channels that leverages a code-constraint polynomial regularizer, improving detection performance over existing methods.

Contribution

It presents a novel optimization-based decoding method using proximal gradient techniques with code-constraint polynomials for massive MIMO systems.

Findings

Outperforms MMSE detector with belief propagation decoding in simulations.

Uses a simple recursion combining gradient descent and code proximal operations.

Demonstrates improved detection accuracy in massive MIMO channels.

Abstract

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.