Fast Reinforcement Learning for Energy-Efficient Wireless Communication

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 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 i) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal physical-layer and system-level power management strategies; ii) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and iii) it obviates the need for action exploration, which severely limits the adaptation speed and run-time performance of conventional reinforcement learning algorithms.