Asynchronous MIMO-OFDM Massive Unsourced Random Access with Codeword Collisions

TL;DR

This paper presents a novel asynchronous MIMO-OFDM massive unsourced random access scheme that effectively estimates channels and offsets using a sparse Bayesian learning algorithm, improving performance and reducing complexity in challenging fading environments.

Contribution

It introduces a dual sparsity-based message-passing algorithm for joint channel, timing, and frequency offset estimation in asynchronous MIMO-OFDM URA systems, addressing codeword collisions and phase rotations.

Findings

Superior performance in channel and offset estimation.

Significant complexity reduction compared to existing methods.

Enhanced data recovery over state-of-the-art URA schemes.

Abstract

This paper investigates asynchronous multiple-input multiple-output (MIMO) massive unsourced random access (URA) in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framework segregates the data into two components, namely, preamble and coding parts, with the former being tree-coded and the latter LDPC-coded. By leveraging the dual sparsity of the equivalent channel across both codeword and delay domains (CD and DD), we develop a message-passing-based sparse Bayesian learning algorithm, combined with belief propagation and mean field, to iteratively estimate DD channel responses, TO, and delay profiles. Furthermore, by jointly leveraging the observations among multiple slots, we establish a novel graph-based algorithm to iteratively separate the superimposed channels and compensate for the phase rotations. Additionally, the proposed algorithm is applied to the flat fading scenario to estimate both TO and CFO, where the channel and offset estimation is enhanced by leveraging the geometric characteristics of the signal constellation. Extensive simulations reveal that the proposed algorithm achieves superior performance and substantial complexity reduction in both channel and offset estimation compared to the codebook enlarging-based counterparts, and enhanced data recovery performances compared to state-of-the-art URA schemes.