Network selection in heterogeneous wireless networks using learning automata

A main target of the next generation wireless networks is to integrate multiple wireless access technologies to provide seamless mobility to mobile users with high-speed wireless connectivity. Network selection is one of the most significant challenges for load balancing to avoid network congestion and performance degradation because of the heterogeneous wireless access environment in next-generation wireless heterogenous networks. In this paper, we present an efficient network selection mechanism to guarantee mobile users selecting a most appropriate wireless network to connect from the heterogenous wireless networks using the theory of games. We prove the network selection game is a potential game, and the solution to the sum rate maximization problem constitutes a feasible pure strategy Nash equilibrium (NE) of the formulated game. Furthermore, a distributed algorithm to learn the NE of the proposed game with limited feedback is presented based on the concept of learning automata. Simulations show that this algorithm which outperforms random selection algorithm in a highly efficient manner can converge to the NE with our designed utility function and achieve near optimal or optimal sum rate performance.