A Cluster-Based Resource Allocation Strategy with Energy Harvesting in Dense Small-Cell Networks

Abstract

This paper focuses on heterogeneous dense small-cell networks (Dense-SCNs), consisting of a macro cell base station (MBS) and multiple small cell base stations (SBSs). In the Dense-SCNs, the MBS is a centralized energy trading center and SBSs receive the required energy resource from the trading center. Due to the SBSs' randomly deployment, resulting in the diversity of energy acquisition price, we have an incentive to agree on maximizing the throughput-benefit per unit energy cost, which problem is proved to be non-convex and cannot be solved within polynomial time. Thus, we propose an algorithm to reduce the computation complexity by decomposing the optimization problem into clustering strategy and iterative resource allocation method. The obtain simulation results show that the proposed adaptive clustering strategy considering both interference and minimum data rate demand can alleviate the negative impact of the multiple strong interference SBSs and balance dynamic traffic load by the selection of appropriate weight factor. What is more, translating a non-convex optimization problem into a series of convex optimization problems and linear problems, the proposed iterative resource allocation method can achieve a better performance of the benefit and computation complexity.