Institute of Electrical & Electronic Engineers
In this paper, the authors propose a distributed Reinforcement Learning (RL) technique called Distributed Power Control using Q-learning (DPC-Q) to manage the interference caused by the femtocells on macro-users in the downlink. The DPC-Q leverages Q-Learning to identify the sub-optimal pattern of power allocation, which strives to maximize femtocell capacity, while guaranteeing macrocell capacity level in an underlay cognitive setting. They propose two different approaches for the DPC-Q algorithm: namely, independent, and cooperative. In the former, femtocells learn independently from each other, while in the latter, femtocells share some information during learning in order to enhance their performance.