A Cooperative Q-learning Approach for Real-time Power Allocation in Femtocell Networks

TL;DR

This paper proposes a cooperative multi-agent Q-learning method for distributed power control in femtocell networks, improving interference management and network capacity while maintaining scalability and robustness.

Contribution

It introduces a cooperative Q-learning scheme for femtocell power control that balances performance and practicality, outperforming independent learning methods.

Findings

Cooperative Q-learning achieves near-optimal femtocell capacity.

The cooperative scheme is scalable to large networks.

The method is robust to network dynamics and femtocell deployment.

Abstract

In this paper, we address the problem of distributed interference management of cognitive femtocells that share the same frequency range with macrocells (primary user) using distributed multi-agent Q-learning. We formulate and solve three problems representing three different Q-learning algorithms: namely, centralized, distributed and partially distributed power control using Q-learning (CPC-Q, DPC-Q and PDPC-Q). CPCQ, although not of practical interest, characterizes the global optimum. Each of DPC-Q and PDPC-Q works in two different learning paradigms: Independent (IL) and Cooperative (CL). The former is considered the simplest form for applying Qlearning in multi-agent scenarios, where all the femtocells learn independently. The latter is the proposed scheme in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. In terms of performance, the simulation results showed that the CL paradigm outperforms the IL paradigm and achieves an aggregate femtocells capacity that is very close to the optimal one. For the practical relevance issue, we evaluate the robustness and scalability of DPC-Q, in real time, by deploying new femtocells in the system during the learning process, where we showed that DPC-Q in the CL paradigm is scalable to large number of femtocells and more robust to the network dynamics compared to the IL paradigm