Robust Beamforming for Cache-Enabled Cloud Radio Access Networks

Popular content caching is expected to play a major role in efficiently reducing backhaul congestion and achieving user satisfaction in next generation mobile radio systems. Consider the downlink of a cache-enabled cloud radio access network (CRAN), where each cache-enabled base-station (BS) is equipped with limited-size local cache storage. The central computing unit (cloud) is connected to the BSs via capacity-limited backhaul links and serves a set of single-antenna mobile users (MUs). This paper assumes that only imperfect channel state information (CSI) is available at the cloud. The paper then focuses on the problem of minimizing the total network power and backhaul cost so as to determine the beamforming vector of each user across the network, and the quantization noise covariance matrix across the backhaul links, subject to imperfect channel state information, per-BS power constraint, and fixed cache placement assumption. The paper proposes solving such a difficult, non-convex \ell-{0}-norm-based optimization problem using the semi-definite relaxation (SDR) and the S-procedure methods. The paper first uses a fine-tuned \ell-{0}-norm approximation so as to find the surrogate function that majorizes the cost function. It then provides a stationary point to the problem using the majorization-minimization (MM) approach. Simulation results show the convergence of the proposed algorithm and highlight how the cache-enabled network significantly improves the backhaul cost as compared to conventional cache-less CRANs, especially at high signal-to-interference-plus-noise ratio (SINR) values.