Stochastic Binning and Coded Demixing for Unsourced Random Access

TL;DR

This paper introduces a stochastic binning and coded demixing approach for unsourced random access, improving message decoding by partitioning users into groups with low cross-coherence, leading to better error performance.

Contribution

It proposes a novel scheme combining stochastic grouping and coded demixing to enhance support recovery in unsourced random access systems.

Findings

Support recovery improves with more groups up to an optimal point.

Numerical simulations confirm reduced error probability.

Partitioning reduces complexity on factor graphs.

Abstract

Unsourced random access is a novel communication paradigm designed for handling a large number of uncoordinated users that sporadically transmit very short messages. Under this model, coded compressed sensing (CCS) has emerged as a low-complexity scheme that exhibits good error performance. Yet, one of the challenges faced by CCS pertains to disentangling a large number of codewords present on a single factor graph. To mitigate this issue, this article introduces a modified CCS scheme whereby active devices stochastically partition themselves into groups that utilize separate sampling matrices with low cross-coherence for message transmission. At the receiver, ideas from the field of compressed demixing are employed for support recovery, and separate factor graphs are created for message disambiguation in each cluster. This reduces the number of active users on a factor graph, which improves performance significantly in typical scenarios. Indeed, coded demixing reduces the probability of error as the number of groups increases, up to a point. Findings are supported with numerical simulations.