Grant-free random access is promising for massive connectivity with sporadic transmissions in massive machine type communications (mMTC), where the hand-shaking between the access point (AP) and users is skipped, leading to high access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we explore the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging our recently developed unitary approximate message passing based matrix factorization (UAMP-MF) algorithm, we design a message passing based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme.

翻译：免授权随机接入因跳过接入点与用户间的握手过程，可实现大规模机器类通信中零星传输的高效接入，具有显著优势。在免授权随机接入中，接入点需识别活跃用户并完成信道估计与信号检测。传统方案需通过导频信号实现用户活跃度检测及信道估计后再进行信号检测，这仍可能带来较大开销与延迟。为进一步降低开销与延迟，本文研究毫米波多输入多输出系统中无导频信号的免授权随机接入问题，使接入点能够完成盲联合用户活跃度检测、信道估计与信号检测。研究表明，盲联合用户活跃度检测-信道估计-信号检测可建模为带约束的复合矩阵分解问题，通过利用信道矩阵与信号矩阵的结构特征可求解该问题。基于我们近期发展的酉近似消息传递矩阵分解算法，设计了一种基于消息传递的贝叶斯算法以求解盲联合用户活跃度检测-信道估计-信号检测问题。大量仿真结果验证了所提盲免授权随机接入方案的有效性。