This paper studies the user activity detection and channel estimation problem in a temporally-correlated massive access system where a very large number of users communicate with a base station sporadically and each user once activated can transmit with a large probability over multiple consecutive frames. We formulate the problem as a dynamic compressed sensing (DCS) problem to exploit both the sparsity and the temporal correlation of user activity. By leveraging the hybrid generalized approximate message passing (HyGAMP) framework, we design a computationally efficient algorithm, HyGAMP-DCS, to solve this problem. In contrast to only exploit the historical estimations, the proposed algorithm performs bidirectional message passing between the neighboring frames for activity likelihood update to fully exploit the temporally-correlated user activities. Furthermore, we develop an expectation maximization HyGAMP-DCS (EM-HyGAMP-DCS) algorithm to adaptively learn the hyperparameters during the estimation procedure when the system statistics are unknown. In particular, we propose to utilize the analysis tool of state evolution to find the appropriate hyperparameter initialization of EM-HyGAMP-DCS. Simulation results demonstrate that our proposed algorithms can significantly improve the user activity detection accuracy and reduce the channel estimation error.

翻译：本文研究时域相关大规模接入系统中的用户活动检测与信道估计问题，其中大量用户间歇性地与基站通信，且每个用户一旦被激活，有很大概率在多个连续帧上传输数据。我们将该问题建模为动态压缩感知（DCS）问题，以利用用户活动的稀疏性和时域相关性。通过混合广义近似消息传递（HyGAMP）框架，我们设计了一种计算高效的算法——HyGAMP-DCS来解决该问题。与仅利用历史估计的方法不同，该算法在相邻帧之间进行双向消息传递以更新活动似然，从而充分利用时域相关的用户活动。此外，我们开发了一种期望最大化HyGAMP-DCS（EM-HyGAMP-DCS）算法，在系统统计信息未知时，能在估计过程中自适应学习超参数。特别地，我们提出利用状态演化分析工具来寻找EM-HyGAMP-DCS的适当超参数初始化。仿真结果表明，所提算法能显著提高用户活动检测精度并降低信道估计误差。