REINFORCEMENT LEARNING FOR ENERGY-EFFICIENT WIRELESS TRANSMISSION

We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. We propose a rigorous and unified framework for simultaneously utilizing both physical-layer centric and system-level techniques to minimize energy consumption, under delay constraints, in the presence of stochastic and unknown traffic and channel conditions. We formulate the problem as a Markov decision process and solve it online using reinforcement learning. The advantages of the proposed online method are that it exploits partial information about the system and it obviates the need for action exploration. Consequently, it significantly outperforms existing reinforcement learning solutions.