A GAME-THEORETIC CLUSTERING FRAMEWORK FOR ENHANCING ENERGY EFFICIENCY AND THROUGHPUT IN GSM NETWORKS

Abstract

The increasing density of users, constraints on energy resources, and heightened radio interference in GSM networks have made the need for intelligent, adaptive methods to optimize clustering structures more apparent than ever. Traditional methods like LEACH and K-Means have limited effectiveness in real-world scenarios due to their lack of consideration for network dynamics, the rational behavior of nodes, and traffic variations. This paper proposes a game theory-based method as a novel approach, aiming to optimize cluster head selection, reduce energy consumption, and enhance link quality under variable network conditions. To evaluate the performance of the proposed method, a comprehensive simulation framework was designed, comparing four clustering methods—Game Theory, LEACH, K-Means, and Random—under identical conditions. The evaluation was based on multiple metrics, including energy consumption, average SINR, spectral efficiency, throughput, cluster stability, and network lifetime. The simulation results demonstrate that the proposed game theory-based method outperforms the others in most metrics. By modeling user interactions as a repeated game and gradually updating the payoff function, this method achieves a 25% to 35% reduction in energy consumption, a 7 to 12 dB increase in average SINR, a 15% to 25% improvement in packet delivery rate, a significant enhancement in spectral efficiency, and a 1.5 to 1.8 times increase in network lifetime compared to conventional methods. Furthermore, the cluster structure in the proposed method exhibits greater stability, minimizing sudden changes in cluster head selection. These results highlight the superiority of the proposed approach.