A Novel Network Selection Approach in 5G Heterogeneous Networks Using Q-Learning

With the development of heterogeneous wireless networks, it is particularly important to build a reasonable network selection mechanism of user in the 5G heterogeneous networks. In this paper, we improve the reward function in Q-Learning using the AHP (Analytic Hierarchy Process) method and make a simple analysis about network resources competition in the case of multi-agent scenario. Then we propose two network selection algorithms: SANSA (single agent network selection algorithm) and MANSA (multi-agent network selection algorithm) which are based on Q-Learning and Nash Q-Learning respectively to deal with the network selection problem. Simulations show that our proposed algorithms have a better performance of network load balancing than the contrast scheme. In addition, the MANSA can effectively reduce the system total power consumption.