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Most existing studies on joint activity detection and channel estimation forgrant-free massive random access (RA) systems assume perfect synchronizationamong all active users, which is hard to achieve in practice. Therefore, thispaper considers asynchronous grant-free massive RA systems and develops novelalgorithms for joint user activity detection, synchronization delay detection,and channel estimation. In particular, the framework of orthogonal approximatemessage passing (OAMP) is first utilized to deal with the non-independent andidentically distributed (i.i.d.) pilot matrix in asynchronous grant-freemassive RA systems, and an OAMP-based algorithm capable of leveraging thecommon sparsity among the received pilot signals from multiple base stationantennas is developed. To reduce the computational complexity, a memory AMP(MAMP)based algorithm is further proposed that eliminates the matrix inversionsin the OAMP-based algorithm. Simulation results demonstrate the effectivenessof the two proposed algorithms over the baseline methods. Besides, theMAMP-based algorithm reduces 37% of the computations while maintainingcomparable detection/estimation accuracy, compared with the OAMP-basedalgorithm.

Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access