Asynchronous MIMO-OFDM Massive Unsourced Random Access With Codeword Collisions

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.

I. Introduction

Massive machine-type communications (mMTC), recognized as one of the three pivotal application scenarios in fifth-generation (5G) wireless communications, facilitate connectivity for burgeoning communication services [2]. The two distinct features in mMTC, namely, sporadic traffic patterns and short data payloads, render traditional random access schemes inadequate due to their inefficient handshake procedures. As a promising technology to further improve efficiency, grant-free random access (GF-RA) has garnered remarkable attention for its low signaling overhead and reduced latency [3]. In many literature, GF-RA is commonly referred to as sourced random access (SRA) since it assigns a unique pilot to each user to identify the source of the message [4], [5]. Nevertheless, assigning unique pilots or encoders to accommodate the vast number of users is deemed impractical. As a prospective paradigm, unsourced random access (URA), first introduced by Polyanskiy in [6], mitigates this challenge by handling massive uncoordinated users through a shared common codebook, where the receiver is tasked to recover messages up to permutations, regardless of user identities.