This paper investigates the multiple-input-multiple-output (MIMO) massive unsourced random access in an asynchronous orthogonal frequency division multiplexing (OFDM) system, with both timing and frequency offsets (TFO) and non-negligible user collisions. The proposed coding framework splits the data into two parts encoded by sparse regression code (SPARC) and low-density parity check (LDPC) code. Multistage orthogonal pilots are transmitted in the first part to reduce collision density. Unlike existing schemes requiring a quantization codebook with a large size for estimating TFO, we establish a \textit{graph-based channel reconstruction and collision resolution (GB-CR$^2$)} algorithm to iteratively reconstruct channels, resolve collisions, and compensate for TFO rotations on the formulated graph jointly among multiple stages. We further propose to leverage the geometric characteristics of signal constellations to correct TFO estimations. Exhaustive simulations demonstrate remarkable performance superiority in channel estimation and data recovery with substantial complexity reduction compared to state-of-the-art schemes.

翻译：本文研究了异步正交频分复用(OFDM)系统中时序与频率偏移(TFO)及不可忽略用户冲突条件下的多输入多输出(MIMO)大规模无源随机接入问题。所提出的编码框架将数据分为两部分，分别采用稀疏回归码(SPARC)和低密度奇偶校验码(LDPC)进行编码。第一部分传输多级正交导频以降低冲突密度。与现有需要大规模量化码本估计TFO的方案不同，我们建立了一种**基于图的信道重建与冲突解决(GB-CR$^2$)**算法，在多阶段间联合迭代重建信道、解决冲突并补偿TFO旋转。我们进一步提出利用信号星座图的几何特征修正TFO估计。充分仿真结果表明，与现有先进方案相比，该方法在显著降低复杂度的同时，在信道估计和数据恢复方面展现出卓越的性能优势。