Abstract-In this paper, we 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. We 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. Simulation results show that the independent approach is capable of mitigating the interference generated by the femtocells on macro-users. Moreover, the results show that cooperation enhances the performance of the femtocells in terms fairness and aggregate femtocell capacity.
To minimize the interference caused by femtocells to MUEs in the downlink, a distributed Reinforcement Learning (RL) technique is used in REF , which is also called Distributed Power Control using Q-learning (DPC-Q).
Distributed Cooperative Q-Learning for Power Allocation in Cognitive Femtocell Networks
2012 IEEE Vehicular Technology Conference (VTC Fall)
2012 IEEE Vehicular Technology Conference (VTC Fall)
Mathematics Computer Science