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：本文研究异步正交频分复用（OFDM）系统中多输入多输出（MIMO）大规模无源随机接入问题，同时考虑定时与频率偏移（TFO）以及不可忽略的用户碰撞。所提出的编码框架将数据分为两部分，分别由稀疏回归码（SPARC）和低密度奇偶校验（LDPC）码进行编码。第一部分传输多级正交导频以降低碰撞密度。与需要大尺寸量化码本估计TFO的现有方案不同，我们建立了一种**基于图的信道重建与碰撞解决（GB-CR$^2$）**算法，该算法在多阶段之间联合迭代重建信道、解决碰撞并补偿TFO旋转。我们进一步提出利用信号星座图的几何特性校正TFO估计。大量仿真表明，与现有最优方案相比，所提方案在信道估计和数据恢复方面具有显著性能优势，同时大幅降低了复杂度。