Distributed optimization in fog radio access networks — channel estimation and multi-user detection

Abstract

In this paper, we consider the channel estimation and multi-user detection problems in fog radio access networks (F-RANs). Based on block coordinate descent algorithm, we propose two methods to solve a mixed ℓ2,1-regularization functional which exploits both the sparsity of user activities and the spatial sparsity of user signals in F-RAN. Both of our methods split the computation and corresponding data into multiple units of a cluster and solve the problem in a distributed manner. Hence they can be deployed flexibly at the distributed logical edges as well as the cloud baseband unit pool in F-RAN. The differences between the two methods are that the first one operates in a serial manner and is guaranteed to converge, while the second one works in parallel and under empirical guidance. Deployment details are also provided. Numerical results demonstrate the effectiveness of the proposed methods.