A Fully Bayesian Approach for Massive MIMO Unsourced Random Access

TL;DR

This paper introduces a fully Bayesian method for massive MIMO unsourced random access that jointly decodes information and estimates channels, outperforming existing strategies in spectral efficiency and robustness.

Contribution

It presents a novel Bayesian framework with a three-layer message passing algorithm for joint decoding and channel estimation in massive MIMO URA systems.

Findings

Significantly improves spectral efficiency over existing methods.

Enhances robustness against codeword collisions.

Effectively estimates channels in complex environments.

Abstract

In this paper, we propose a novel fully Bayesian approach for the massive multiple-input multiple-output (MIMO) massive unsourced random access (URA). The payload of each user device is coded by the sparse regression codes (SPARCs) without redundant parity bits. A Bayesian model is established to capture the probabilistic characteristics of the overall system. Particularly, we adopt the core idea of the model-based learning approach to establish a flexible Bayesian channel model to adapt the complex environments. Different from the traditional divide-and-conquer or pilot-based massive MIMO URA strategies, we propose a three-layer message passing (TLMP) algorithm to jointly decode all the information blocks, as well as acquire the massive MIMO channel, which adopts the core idea of the variational message passing and approximate message passing. We verify that our proposed TLMP significantly enhances the spectral efficiency compared with the state-of-the-arts baselines, and is more robust to the possible codeword collisions.