The massive scale of future wireless networks will create computational bottlenecks in performance optimization. In this paper, we study the problem of connecting mobile traffic to Cloud RAN (C-RAN) stations. To balance station load, we steer the traffic by designing device association rules. The baseline association rule connects each device to the station with the strongest signal, which does not account for interference or traffic hot spots, and leads to load imbalances and performance deterioration. Instead, we can formulate an optimization problem to decide centrally the best association rule at each time instance. However, in practice this optimization has such high dimensions, that even linear programming solvers fail to solve. To address the challenge of massive connectivity, we propose an approach based on the theory of optimal transport, which studies the economical transfer of probability between two distributions. Our proposed methodology can further inspire scalable algorithms for massive optimization problems in wireless networks.
Cloud radio access networks (C-RANs) have been regarded as a promising architecture for energy-efficient fifth generation systems. In this paper, a new joint remote radio head (RRH) activation, user-RRH pairing and resource allocation strategy is proposed for heterogeneous C-RANs (H-CRANs). We first formulate an optimization problem to maximize the energy efficiency of H-CRANs. Then, a low-complexity suboptimal solution is developed. Our proposed mechanism consists of three key procedures: 1) RRH activation is performed based on greedy RRH selection, 2) user-RRH pairing is performed based on the channel quality, 3) the resource allocation problem is solved by dual decomposition. Simulation results show that the proposed strategy can improve energy efficiency significantly.