An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random Access

TL;DR

This paper introduces a two-stage decoding scheme for SPARC in massive MIMO unsourced random access, achieving near-optimal performance with low complexity through thresholding and maximum-likelihood refinement.

Contribution

It proposes a novel two-stage decoding method combining thresholding and maximum-likelihood estimation for efficient SPARC decoding in massive MIMO systems.

Findings

Achieves near-optimal decoding performance.

Reduces computational complexity significantly.

Provides theoretical bounds for active user support.

Abstract

In this paper, we study a concatenate coding scheme based on sparse regression code (SPARC) and tree code for unsourced random access in massive multiple-input and multiple-output systems. Our focus is concentrated on efficient decoding for the inner SPARC with practical concerns. A two-stage method is proposed to achieve near-optimal performance while maintaining low computational complexity. Specifically, a one-step thresholding-based algorithm is first used for reducing large dimensions of the SPARC decoding, after which a relaxed maximum-likelihood estimator is employed for refinement. Adequate simulation results are provided to validate the near-optimal performance and the low computational complexity. Besides, for covariance-based sparse recovery method, theoretical analyses are given to characterize the upper bound of the number of active users supported when convex relaxation is considered, and the probability of successful dimension reduction by the one-step thresholding-based algorithm.