This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.

翻译：本文研究了具有时间相关活动的协作式多小区大规模接入系统中的活动检测与信道估计问题，其中所有接入点通过前传链路连接至中央单元。为减轻计算负担，我们提出以用户为中心的接入点协作方案，并引入广义滑动窗口检测策略以充分利用活动的时间相关性。通过建立与因子图表示相关的概率模型，我们从贝叶斯推断角度提出一种可扩展的基于动态压缩感知的多测量向量广义近似消息传递（DCS-MMV-GAMP）算法。该算法通过在时空域中对活动进行标准消息传递来优化活动似然估计，并采用GAMP实现高效信道估计。此外，针对有限前传容量场景，我们基于DCS-MMV-GAMP发展了量化转发（QF）与检测转发（DF）两种方案，并在多种系统限制下进行了广泛评估。数值结果验证了所提方法相较于基准方案的显著优势。研究表明，当天线数量较少时，QF通常能实现更优性能；而配备大规模天线阵列时，若前传容量受限，DF则更占优势。