SPARC-LDPC Coding for MIMO Massive Unsourced Random Access

TL;DR

This paper introduces a novel coding scheme combining SPARC and LDPC for MIMO massive unsourced random access, outperforming traditional covariance-based methods in efficiency and complexity.

Contribution

The paper proposes a new joint SPARC-LDPC coding scheme that improves spectral efficiency and reduces complexity compared to existing covariance-based detection methods.

Findings

Outperforms CB-ML in scenarios with fewer antennas than active users.

Achieves nearly 15 times higher spectral efficiency.

Lower computational complexity than CB-ML scheme.

Abstract

A joint sparse-regression-code (SPARC) and low-density-parity-check (LDPC) coding scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) is proposed in this paper. Different from the state-of-the-art covariance-based maximum likelihood (CB-ML) detection scheme, we first split users' messages into two parts. The former part is encoded by SPARCs and tasked to recover part of the messages, the corresponding channel coefficients as well as the interleaving patterns by compressed sensing. The latter part is coded by LDPC codes and then interleaved by the interleave-division multiple access (IDMA) scheme. The decoding of the latter part is based on belief propagation (BP) joint with successive interference cancellation (SIC). Numerical results show our scheme outperforms the CB-ML scheme when the number of antennas at the base station is smaller than that of active users. The complexity of our scheme is with the order $\mathcal{O}\left(2^{B_p}ML+\widehat{K}ML\right)$ and lower than the CB-ML scheme. Moreover, our scheme has higher spectral efficiency (nearly $15$ times larger) than CB-ML as we only split messages into two parts.