Delay-Optimal Joint Processing in Computation-Constrained Fog Radio Access Networks

Abstract

With fog radio access networks (F-RANs), the computation capability is provided in the physical proximity of the users, which can significantly lower the delay and mitigate the heavy traffic over backhaul links. As the number of remote radio head (RRH) increases, the computational complexity becomes a severe issue and it is necessary to group the RRHs into multiple clusters. In this paper, we optimize the joint processing strategy, including the RRH clustering and the RRH-server matching to minimize the delay for F-RANs. We model the delay-optimal joint processing problem in computation-constrained F-RANs as a Markov decision process (MDP) problem. By deriving the optimality condition of this MDP, we obtain a per-slot weighted sum rate maximization problem, in which the RRH clustering and the RRH-server matching are solved jointly. Specifically, we transform the weighted sum rate maximization problem into a combinatorial auction problem (CAP). Since the CAP is NP-hard, we greedily obtain an initial solution of CAP and gradually improve the performance by adopting local α-good improvement algorithm based on the weighted independent set problem (WISP). Furthermore, we theoretically derive the bound of the performance of the proposed algorithm.