Self-optimizing energy management in heterogeneous cellular networks

Abstract

In this paper, we develop and evaluate a distributed algorithm to efficiently balance the trade-off between network throughput and energy consumption in a heterogeneous cellular network. We formulate the problem as a joint optimization of base station activation, power control and user association. To solve the problem, which is a non-convex optimization problem, we design a self-optimizing algorithm based on Gibbs sampling in which each base station individually optimizes its configuration without the involvement of any central controller. In our algorithm, base stations only need to exchange information in a locally defined neighborhood, yet the network state eventually converges to the global optimal. Simulation results are also provided, which show that, i) the proposed algorithm indeed converges to a state that is close to optimal, and ii) by dynamically activating base stations, we see about 10% reduction in network energy consumption without penalizing the network throughput.