  This paper studies massive access in cell-free massive multi-input
multi-output (MIMO) based Internet of Things and solves the challenging active
user detection (AUD) and channel estimation (CE) problems. For the uplink
transmission, we propose an advanced frame structure design to reduce the
access latency. Moreover, by considering the cooperation of all access points
(APs), we investigate two processing paradigms at the receiver for massive
access: cloud computing and edge computing. For cloud computing, all APs are
connected to a centralized processing unit (CPU), and the signals received at
all APs are centrally processed at the CPU. While for edge computing, the
central processing is offloaded to part of APs equipped with distributed
processing units, so that the AUD and CE can be performed in a distributed
processing strategy. Furthermore, by leveraging the structured sparsity of the
channel matrix, we develop a structured sparsity-based generalized approximated
message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the
quantization accuracy of the processed signals is taken into account. Based on
the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE
scheme is further developed under two paradigms for reduced access latency.
Simulation results validate the superiority of the proposed approach over the
state-of-the-art baseline schemes. Besides, the results reveal that the edge
computing can achieve the similar massive access performance as the cloud
computing, and the edge computing is capable of alleviating the burden on CPU,
having a faster access response, and supporting more flexible AP cooperation.
