LAUSR

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.