Massive access has been challenging for the fifth generation (5G) and beyond since the abundance of devices causes communication overload to skyrocket. In an uplink massive access scenario, device traffic is sporadic in any given coherence time. Thus, channels across the antennas of each device exhibit correlation, which can be characterized by the row sparse channel matrix structure. In this work, we develop a bilinear generalized approximate message passing (BiGAMP) algorithm based on the row sparse channel matrix structure. This algorithm can jointly detect device activities, estimate channels, and detect signals in massive multiple-input multiple-output (MIMO) systems by alternating updates between channel matrices and signal matrices. The signal observation provides additional information for performance improvement compared to the existing algorithms. We further analyze state evolution (SE) to measure the performance of the proposed algorithm and characterize the convergence condition for SE. Moreover, we perform theoretical analysis on the error probability of device activity detection, the mean square error of channel estimation, and the symbol error rate of signal detection. The numerical results demonstrate the superiority of the proposed algorithm over the state-of-the-art methods in DADCE-SD, and the numerical results are relatively close to the theoretical analysis results.

翻译：海量接入已成为第五代（5G）及未来通信系统的挑战，因为设备数量的激增会导致通信负载急剧上升。在上行海量接入场景中，任何给定相干时间内的设备流量都是突发的。因此，每个设备各个天线之间的信道呈现相关性，这种相关性可通过行稀疏信道矩阵结构来表征。本文基于行稀疏信道矩阵结构，开发了一种双线性广义近似消息传递（BiGAMP）算法。该算法通过信道矩阵与信号矩阵的交替更新，能够在海量多输入多输出（MIMO）系统中联合检测设备活动、估计信道并检测信号。与现有算法相比，信号观测值提供了额外的信息以提升性能。我们进一步分析了状态演化（SE），以衡量所提算法的性能，并刻画了SE的收敛条件。此外，我们对设备活动检测的误检概率、信道估计的均方误差以及信号检测的符号错误率进行了理论分析。数值结果表明，所提算法在DADCE-SD问题中优于现有最优方法，且数值结果与理论分析结果较为接近。