Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice. Therefore, this paper considers asynchronous grant-free massive RA systems and develops novel algorithms for joint user activity detection, synchronization delay detection, and channel estimation. In particular, the framework of orthogonal approximate message passing (OAMP) is first utilized to deal with the non-independent and identically distributed (i.i.d.) pilot matrix in asynchronous grant-free massive RA systems, and an OAMP-based algorithm capable of leveraging the common sparsity among the received pilot signals from multiple base station antennas is developed. To reduce the computational complexity, a memory AMP (MAMP)based algorithm is further proposed that eliminates the matrix inversions in the OAMP-based algorithm. Simulation results demonstrate the effectiveness of the two proposed algorithms over the baseline methods. Besides, the MAMP-based algorithm reduces 37% of the computations while maintaining comparable detection/estimation accuracy, compared with the OAMP-based algorithm.

翻译：现有关于免授权大规模随机接入（RA）系统中联合活动检测与信道估计的大多数研究都假设所有活跃用户之间完全同步，这在实践中难以实现。因此，本文考虑了异步免授权大规模RA系统，并开发了用于联合用户活动检测、同步延迟检测和信道估计的新算法。具体而言，首先利用正交近似消息传递（OAMP）框架来处理异步免授权大规模RA系统中非独立同分布（i.i.d.）的导频矩阵，并开发了一种能够利用来自多个基站天线接收导频信号中共同稀疏性的基于OAMP的算法。为降低计算复杂度，进一步提出了一种基于记忆AMP（MAMP）的算法，该算法消除了基于OAMP算法中的矩阵求逆。仿真结果表明，所提出的两种算法优于基线方法。此外，与基于OAMP的算法相比，基于MAMP的算法在保持相当检测/估计精度的同时，降低了37%的计算量。