Low Power Wireless Communication via Reinforcement Learning

This paper examines the application of reinforcement learning to a wireless communication problem. The problem requires that channel utility be maximized while simultaneously minimizing battery usage. We present a solution to this multi-criteria problem that is able to significantly reduce power consumption. The solution uses a variable discount factor to capture the effects of battery usage. 1 Introduction Reinforcement learning (RL) has been applied to resource allocation problems in telecommunications, e.g., channel allocation in wireless systems, network routing, and admission control in telecommunication networks [1, 2, 8, 10]. These have demonstrated reinforcement learning can find good policies that significantly increase the application reward within the dynamics of the telecommunication problems. However, a key issue is how to treat the commonly occurring multiple reward and constraint criteria in a consistent way. This paper will focus on power management for wireless ..