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This paper considers the support of grant-free massive access and solves thechallenge of active user detection and channel estimation in the case of amassive number of users. By exploiting the sparsity of user activities, theconcerned problems are formulated as a compressive sensing problem, whosesolution is acquired by approximate message passing (AMP) algorithm.Considering the cooperation of multiple access points, for the deployment ofAMP algorithm, we compare two processing paradigms, cloud computing and fogcomputing, in terms of their effectiveness in guaranteeing ultra reliablelow-latency access. For cloud computing, the access points are connected in acloud radio access network (C-RAN) manner, and the signals received at allaccess points are concentrated and jointly processed in the cloud basebandunit. While for fog computing, based on fog radio access network (F-RAN), theestimation of user activity and corresponding channels for the whole network issplit, and the related processing tasks are performed at the access points andfog processing units in proximity to users. Compared to the cloud computingparadigm based on traditional C-RAN, simulation results demonstrate thesuperiority of the proposed fog computing deployment based on F-RAN.

Compressive Massive Access for Internet of Things: Cloud Computing or Fog Computing?