User-Centric Base Station Clustering and Sparse Beamforming for Cache-Enabled Cloud RAN

Cloud radio access network (Cloud RAN) is able to significantly improve the network capacity and energy efficiency through centralized processing of multiple distributed base stations (BSs). Yet it also places tremendous burden on backhaul links that connect these BSs. In this paper, we introduce a cache-enabled Cloud RAN, in which each BS is equipped with a local cache with limited storage capacity. If the content requested by a user is not cached at the serving BSs of the user, it will be distributed to the serving BSs via the backhaul links from the central processor (CP). We investigate the dynamic user-centric BS clustering and sparse beamforming by taking both channel condition and cache status into account. We formulate an optimization problem with the objective of minimizing the weighted sum of backhaul cost and transmit power cost subject to an individual signal-to-interference-and-noise ratio (SINR) constraint for each user. This problem is a mixed-integer non-linear programming (MINLP) problem. We apply the iterative reweighted l(1)-norm technique to find an approximate solution. Theoretical analysis also shows that all the BSs which cache the content requested by a user can always be included in the BS cluster of this user, regardless of their channel conditions. Based on this finding, we propose a cache-aware greedy selection BS clustering algorithm. Simulation results show that caching can greatly improve the tradeoff between backhaul capacity and transmit power. It is also demonstrated that popularity-aware caching is superior to random caching in Cloud RAN.