Computational resource constrained multi-cell joint processing in cloud radio access networks

Centralized joint processing has been demonstrated to achieve significant performance gain over the conventional per-cell processing by avoiding the inter-cell interference in cloud radio access networks (C-RANs), but full scale cooperation over the entire network is infeasible due to its huge computational complexity. The goal of this paper is to design remote radio head (RRH) clustering and the associated power allocation algorithm under computational resource constraint for C-RANs, which is challenging due to the combinatorial clustering problem with non-convex constraints. To overcome the challenge, our design consists of two technical steps. 1) Considering the maximization of the sum data rate, we reformulate both the objective function and the computational resource constraint via relaxation and successive convex approximation (SCA) technique. 2) By exploiting the structure of this problem, we propose a low-complexity algorithm to decouple RRH clustering and power allocation and solve them iteratively. Furthermore, we prove the convergence property of the proposed iterative algorithm. Finally, our proposed algorithm is evaluated by simulation to show its superiority on throughput performance over conventional algorithms.