Improved decentralized Q-learning algorithm for interference reduction in LTE-femtocells

Femtocells are receiving considerable interest in mobile communications as a strategy to overcome the indoor coverage problems as well as to improve the efficiency of current macrocell systems. Nevertheless, the detrimental factor in such networks is co-channel interference between macrocells and femtocells, as well as among neighboring femtocells which can dramatically decrease the overall capacity of the network. In this paper we propose a Reinforcement Learning (RL) framework, based on an improved decentralized Q-learning algorithm for femtocells sharing the macrocell spectrum. Since the major drawback of Q-learning is its slow convergence, we propose a smart initialization procedure. The proposed algorithm will be compared with a basic Q-learning algorithm and some power control (PC) algorithms from literature, e.g., fixed power allocation, received power based PC. The goal is to show the performance improvement and enhanced convergence.