Two-phase Unsourced Random Access in Massive MIMO: Performance Analysis and Approximate Message Passing Decoder

TL;DR

This paper introduces a two-phase unsourced random access scheme for massive MIMO systems, combining joint channel and information bit acquisition with a compressed sensing approach, supported by a theoretical physics-based analysis and an AMP decoder.

Contribution

A novel two-phase URA scheme that improves spectral efficiency and reduces complexity, with a theoretical framework and an AMP decoder demonstrating superior performance.

Findings

Performance metrics linked to system parameters via free entropy

The proposed scheme outperforms recent counterparts in simulations

Theoretical analysis confirms the effectiveness of the AMP decoder

Abstract

In this paper, we design a novel two-phase unsourced random access (URA) scheme in massive multiple input multiple output (MIMO). In the first phase, we collect a sequence of information bits to jointly acquire the user channel state information (CSI) and the associated information bits. In the second phase, the residual information bits of all the users are partitioned into sub-blocks with a very short length to exhibit a higher spectral efficiency and a lower computational complexity than the existing transmission schemes in massive MIMO URA. By using the acquired CSI in the first phase, the sub-block recovery in the second phase is cast as a compressed sensing (CS) problem. From the perspective of the statistical physics, we provide a theoretical framework for our proposed URA scheme to analyze the induced problem based on the replica method. The analytical results show that the performance metrics of our URA scheme can be linked to the system parameters by a single-valued free entropy function. An AMP-based recovery algorithm is designed to achieve the performance indicated by the proposed theoretical framework. Simulations verify that our scheme outperforms the most recent counterparts.