Design and Analysis of Massive Uncoupled Unsourced Random Access with Bayesian Joint Decoding

TL;DR

This paper proposes a high-efficiency, low-complexity Bayesian joint decoding scheme for massive uncoupled unsourced random access in 6G networks, improving spectral efficiency without using parity bits.

Contribution

It introduces a novel uncoupled framework and a Bayesian decoding algorithm that exploits channel statistics, enhancing performance in massive machine-type communications.

Findings

Spectral efficiency is improved by avoiding parity bits.

The Bayesian decoding algorithm effectively detects codewords with low complexity.

Simulation results confirm the scheme's effectiveness in 6G scenarios.

Abstract

In this paper, we investigate unsourced random access for massive machine-type communications (mMTC) in the sixth-generation (6G) wireless networks. Firstly, we establish a high-efficiency uncoupled framework for massive unsourced random access without extra parity check bits. Then, we design a low-complexity Bayesian joint decoding algorithm, including codeword detection and stitching. In particular, we present a Bayesian codeword detection approach by exploiting Bayes-optimal divergence-free orthogonal approximate message passing in the case of unknown priors. The output long-term channel statistic information is well leveraged to stitch codewords for recovering the original message. Thus, the spectral efficiency is improved by avoiding the use of parity bits. Moreover, we analyze the performance of the proposed Bayesian joint decoding-based massive uncoupled unsourced random access scheme in terms of computational complexity and error probability of decoding. Furthermore, by asymptotic analysis, we obtain some useful insights for the design of massive unsourced random access. Finally, extensive simulation results confirm the effectiveness of the proposed scheme in 6G wireless networks.