Dynamic Clustering and Sleep Mode Strategies for Small Cell Networks

TL;DR

This paper introduces a dynamic clustering and sleep mode strategy for small cell networks that improves energy efficiency and load balancing through spectral clustering and a game-theoretic approach, achieving significant energy savings.

Contribution

It proposes a novel cluster-based, dynamic sleep-wake mechanism combined with a noncooperative game model for autonomous decision-making in small cell networks.

Findings

Energy consumption reduced by up to 40%

Load reduced by up to 23%

Enhanced energy efficiency and performance gains

Abstract

In this paper, a novel cluster-based approach for optimizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism based on the spectral clustering technique is proposed to dynamically form clusters of small cell base stations. Such clustering enables intra-cluster coordination among the base stations for optimizing the downlink performance through load balancing, while satisfying users' quality-of-service requirements. In the proposed approach, the clusters use an opportunistic base station sleep-wake switching mechanism to strike a balance between delay and energy consumption. The inter-cluster interference affects the performance of the clusters and their choices of active or sleep state. Due to the lack of inter-cluster communications, the clusters have to compete with each other to make decisions on improving the energy efficiency. This competition is formulated as a noncooperative game among the clusters that seek to minimize a cost function which captures the tradeoff between energy expenditure and load. To solve this game, a distributed learning algorithm is proposed using which the clusters autonomously choose their optimal transmission strategies. Simulation results show that the proposed approach yields significant performance gains in terms of reduced energy expenditures up to 40% and reduced load up to 23% compared to conventional approaches.