Dynamic Clustering and ON/OFF Strategies for Wireless Small Cell Networks

TL;DR

This paper introduces a dynamic clustering approach combined with game-theoretic strategies to enhance energy efficiency in wireless small cell networks, achieving significant reductions in energy use and transfer time.

Contribution

It proposes a novel cluster-based, dynamic mechanism with a distributed learning algorithm for optimizing energy efficiency in small cell networks.

Findings

Up to 36% reduction in energy expenditures.

Up to 41% reduction in fractional transfer time.

Convergence to an epsilon-coarse correlated equilibrium.

Abstract

In this paper, a novel cluster-based approach for maximizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to group locally-coupled small cell base stations (SBSs) into clusters based on location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users' quality-of-service requirements. Due to the lack of inter-cluster communications, clusters compete with one another in order to improve the overall network's energy efficiency. This inter-cluster competition is formulated as a noncooperative game between clusters that seek to minimize their respective cost functions. To solve this game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution which constitutes an epsilon-coarse correlated equilibrium for the studied game. Simulation results show that the proposed approach yields significant performance gains reaching up to 36% of reduced energy expenditures and up to 41% of reduced fractional transfer time compared to conventional approaches.