  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.
