  Femtocell networks have become a promising solution in supporting high data
rates for 5G systems, where cell densification is performed using the small
femtocells. However, femtocell networks have many challenges. One of the major
challenges of femtocell networks is the interference management problem, where
deployment of femtocells in the range of macro-cells may degrade the
performance of the macrocell. In this paper, we develop a new platform for
studying interference management in distributed femtocell networks using
reinforcement learning approach. We design a complete MAC protocol to perform
distributed power allocation using Q-Learning algorithm, where both independent
and cooperative learning approaches are applied across network nodes. The
objective of the Q-Learning algorithms is to maximize aggregate femtocells
capacity, while maintaining the QoS for the Macrocell users. Furthermore, we
present the realization of the algorithms using GNURadio and USRP platforms.
Performance evaluation are conducted in terms of macrocell capacity convergence
to a target capacity and improvement of aggregate femtocells capacity.
