Reinforcement learning in MIMO wireless networks with energy harvesting

Energy harvesting wireless nodes provide much longer lifetime and higher energy efficiency for wireless networks compared to battery operated systems. In this paper, we study a MIMO wireless communication link in which the nodes are equipped with energy harvesters and rechargeable batteries that are continuously charging from a renewable energy source. Since the harvested energy arrival and thus the future remaining energy of the nodes is not deterministic in practice, we propose a learning approach in order to find the most efficient transmission policy for data communication that maximizes throughput. The problem is formulated as a Markov Decision Process (MDP) with unknown transition probabilities. A Q-Learning approach is proposed to solve the MDP model and find the optimal transmission policy.