This paper considers the support of grant-free massive access and solves the
challenge of active user detection and channel estimation in the case of a
massive number of users. By exploiting the sparsity of user activities, the
concerned problems are formulated as a compressive sensing problem, whose
solution is acquired by approximate message passing (AMP) algorithm.
Considering the cooperation of multiple access points, for the deployment of
AMP algorithm, we compare two processing paradigms, cloud computing and fog
computing, in terms of their effectiveness in guaranteeing ultra reliable
low-latency access. For cloud computing, the access points are connected in a
cloud radio access network (C-RAN) manner, and the signals received at all
access points are concentrated and jointly processed in the cloud baseband
unit. While for fog computing, based on fog radio access network (F-RAN), the
estimation of user activity and corresponding channels for the whole network is
split, and the related processing tasks are performed at the access points and
fog processing units in proximity to users. Compared to the cloud computing
paradigm based on traditional C-RAN, simulation results demonstrate the
superiority of the proposed fog computing deployment based on F-RAN.Comment: 7 pages, 7 figures, accepted by IEEE International Conference on
Communications (ICC) 2020, Dublin, Irelan