Energy-Efficient Resource Allocation in Wireless Networks: An Overview of Game-Theoretic Approaches

TL;DR

This paper reviews game-theoretic methods for energy-efficient resource allocation in wireless networks, emphasizing non-cooperative games that optimize individual utility under QoS constraints, and analyzes their impact on network performance.

Contribution

It introduces a comprehensive game-theoretic framework for energy-efficient resource management in wireless networks with QoS, including analysis of strategies and equilibrium states.

Findings

Game theory effectively models resource allocation with QoS constraints.

Power control and modulation strategies significantly impact energy efficiency.

Nash equilibrium analysis guides optimal resource strategies.

Abstract

An overview of game-theoretic approaches to energy-efficient resource allocation in wireless networks is presented. Focusing on multiple-access networks, it is demonstrated that game theory can be used as an effective tool to study resource allocation in wireless networks with quality-of-service (QoS) constraints. A family of non-cooperative (distributed) games is presented in which each user seeks to choose a strategy that maximizes its own utility while satisfying its QoS requirements. The utility function considered here measures the number of reliable bits that are transmitted per joule of energy consumed and, hence, is particulary suitable for energy-constrained networks. The actions available to each user in trying to maximize its own utility are at least the choice of the transmit power and, depending on the situation, the user may also be able to choose its transmission rate, modulation, packet size, multiuser receiver, multi-antenna processing algorithm, or carrier allocation strategy. The best-response strategy and Nash equilibrium for each game is presented. Using this game-theoretic framework, the effects of power control, rate control, modulation, temporal and spatial signal processing, carrier allocation strategy and delay QoS constraints on energy efficiency and network capacity are quantified.