Resource Optimization via Markov Approximation in Cloud Radio Access Networks

초록

In this paper, we investigate the joint user association and resource allocation problem in the downlink of cloud radio access networks (CRAN). The central cloud performs the scheduling of spectrum resource across different base stations (BSs) to user equipments (UEs) for the maximal network utility under practical network constraints. Nevertheless, the problem is a combinatorial optimization problem, which is intractable by the traditional exhaustive search when the network size is large. We propose a new scheduling selection method by introducing Markov approximation, which synthesizes algorithm to achieve approximate optimization by forming a reversible continuoustime Markov chain. This method adapts to not only the static network situation but also the dynamic network situation caused by users joining or departure. Besides, this method solves the two network subproblems of user association and resource allocation concurrently, which reduces the computing complexity. Simulation results demonstrate the convergence of Markov approximation and the validity of the proposed algorithm. Index Terms?Cloud radio access networks, Markov approximation, user association, resource allocation, static and dynamic network