Game Theoretic Approach For Base Stations Energy Saving In Wireless Networks

With the rapid development of wireless communication and the increase of the number of users, a large number of base stations lead to a sharply increase of network energy consumption and carbon emissions. Currently, energy conservation has become a core issue of green communications. In a typical cellular network,base stations(BSs) are responsible for 60-80% of the whole cellular network energy consumption.Moreover, a base station working in the normal state consumes more than 90% of its peak energy even when there is little traffic. Therefore, to improve energy utilization, it is crucial to reduce BSs’ energy consumption in green wireless cellular networks.Game theory is a collection of mathematical models formulated to study situations of conflict and cooperation. It may generally be categorized as non-cooperative game theory and cooperative game theory. The main focus of non-cooperative game theory is individual rationality and individual optimal strategy. "Nash equilibrium(NE)" is the general equilibrium of game result. Under this equilibrium, no user may gain by unilaterally deviating, so no one has any incentive to change strategy. The study of cooperative game theory is whether it can reach a binding commitment. Cooperative game theory emphasizes collective rationality, fairness and effectiveness. A common solution of cooperative game theory is called "Nash Bargaining Solution(NBS)". Its goal is to maximize the collective interest, and fairly, effectively distribute the benefits of participants using a binding commitment.To ensure the quality of service of mobile users, base stations are designed to support peak time traffic. A typical “tidal effect” exists in mobile communication networks, the utilization of base stations can be very inefficient during off-peak time because the traffic profile is time varying. Therefore, this paper proposes a method based on non-cooperative game theory and a method based on cooperative game theory, they use game theory to decide the best number of base stations that can be switched off. To save energy on base station without compromising the offered Quality of Service, numerical results are then presented to illustrate the efficacy of the proposed schemes.