Distributed Cooperative Q-learning for Power Allocation in Cognitive Femtocell Networks

TL;DR

This paper introduces a distributed reinforcement learning method called DPC-Q for power control in cognitive femtocell networks, aiming to optimize femtocell capacity while protecting macrocell users.

Contribution

It presents a novel distributed Q-learning based algorithm with independent and cooperative approaches for interference management in femtocell networks.

Findings

Cooperative approach improves convergence speed and fairness.

Independent approach effectively mitigates interference.

Simulation results validate the effectiveness of the proposed methods.

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 of speed of convergence, fairness and aggregate femtocell capacity.