Dynamic resource allocation for OFDMA femtocell networks: a game-theoretic approach

Femtocells consisting of small femto base stations have emerged as an efficient solution for improving the capacity and coverage of wireless cellular networks. However, due to limited wireless radio resources, resource allocation is a key issue in two-tier femtocell networks. Motivated by this challenge, in this paper, we propose a resource allocation approach which satisfies the quality of service requirements and maximizes social welfare. Users compete with each other for a serving base station that fulfills their quality of service requirements, and the serving base stations prefer to serve more users to make more revenue. We model the competition among these rational decision makers as the Vickrey–Clarke–Groves auction game theory in which each user as a buyer submits a bid for resources, and each base station as a seller decides which users will win the auction and how much the winning users should pay and then it assigns the resources to the winning users. Unlike the previous studies, we also take into account macro user’s activity as cross-tier interference in the resource allocation process. We develop an algorithm based on Q-learning in which each user gradually learns from its own past information and adjusts its bid value to achieve the Nash equilibrium as the solution of the game without any interaction with other users. We also investigate the existence and uniqueness of the Nash equilibrium. Simulation results verify the accuracy of the numerical results obtained from the proposed model.