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.

翻译：本文研究了在存在定时偏移（TO）和载波频率偏移（CFO）以及不可忽略的码字碰撞情况下，正交频分复用（OFDM）系统在频率选择性衰落信道上的异步多输入多输出（MIMO）大规模无源随机接入（URA）问题。所提出的编码框架将数据分为前导码和编码两部分，前者采用树编码，后者采用LDPC编码。通过利用等效信道在码字域（CD）和时延域（DD）的双重稀疏性，我们开发了一种基于消息传递的稀疏贝叶斯学习算法，结合置信传播和平均场理论，以迭代方式估计DD信道响应、TO和时延分布。此外，通过联合利用多个时隙的观测数据，我们建立了一种新颖的基于图的算法，以迭代方式分离叠加信道并补偿相位旋转。另外，所提算法被应用于平坦衰落场景以估计TO和CFO，其中通过利用信号星座图的几何特性来增强信道和偏移估计。大量仿真结果表明，与基于码本扩展的对比方案相比，所提算法在信道和偏移估计方面实现了更优的性能和显著的复杂度降低，并且与最先进的URA方案相比，数据恢复性能也得到了提升。