User Association and Power Allocation Based on Q-Learning in Ultra Dense Heterogeneous Networks

Abstract

Ultra dense heterogeneous network (UDHN) has become one of the main frameworks of 5G. Traditional user association methods are difficult to satisfy this new scenario for load balancing. On the other hand, the concept of green communication requires the network to increase energy efficiency. Therefore, it is necessary to study power allocation and user association in UDHN. This paper focuses on load balancing and energy efficiency of UDHN. The joint user association and power allocation is modelled as an appropriate optimization problem. Then we introduce reinforcement learning and propose a multiagent Q-learning based algorithm for solving the optimization problem. According to analysis of simulation result, the convergence of the proposed scheme is verified and the proposed approach is effective on achieving load balancing and enhancing energy efficiency in UDHN.