Two-Timescale User-Centric RRH Clustering and Precoding Optimization for Cloud RAN via Local Stochastic Cutting Plane

Abstract

In a cloud radio access network (C-RAN), many distributed remote radio heads (RRHs) are connected to a centralized baseband unit pool via high-speed fronthaul links. Such an architecture improves the spectral efficiency but suffers from huge implementation costs. We propose a mixed timescale radio interference processing framework to optimize the tradeoff between the average weighted sum rate and the implementation cost in the C-RAN downlink. The radio interference processing is decomposed into short-term precoding and long-term user-centric RRH clustering (UCRC) subproblems. The short-term precoding subproblem can be solved using a modified weighted minimum mean squared error approach. To solve the challenging UCRC subproblem, we first propose a novel approximate stochastic cutting plane algorithm . Then, we bound the optimality gap of the proposed overall solution, and establish its asymptotic optimality in the weak interference and high SNR regimes. Simulations show that the proposed two-timescale solution achieves a better tradeoff performance than the baselines.