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)系统中存在时间与频率偏移(TFO)及不可忽略用户碰撞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，分别由稀疏回归码(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碰撞密度。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**基于图的信道重构与碰撞解决(GB-CR$^2$)**算法，通过多阶段联合迭代重构信道、解决碰撞并补偿TFO旋转。进一步提出利用信号星座图的几何特征修正TFO估计。全面仿真表明，与现有最优方案相比，该方法在信道估计与数据恢复方面具有显著性能优势，同时大幅降低了计算复杂度。