Intercell Interference Management in OFDMA Networks: A Decentralized Approach Based onReinforcement Learning

Authors:
F. Bernardo;R. Agustí;J. Pérez-Romero;O. Sallent
Affiliations:
Department of Signal Theory and Communications, Universidad de Sevilla, Seville, Spain;Universitat Politècnica de Catalunya, Barcelona, Spain;Universitat Politècnica de Catalunya, Barcelona, Spain;Universitat Politècnica de Catalunya, Barcelona, Spain
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Year:
2011

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Abstract

This paper presents a decentralized framework for dynamic spectrum assignment in multicell orthogonal frequency division multiple access (OFDMA) networks. The proposed framework allows each cell to autonomously decide the frequency resources it should use through a procedure that incorporates concepts from self-organization and machine learning in multiagent systems (MASs). Simulation results have been obtained for several scenarios, including both macrocells (MCs) and femtocells (FCs), revealing important improvements in terms of spectral efficiency and intercell interference mitigation over reference approaches, and close performance with the one obtained by a centralized strategy. Results also suggest that the framework would be practical for future FC cellular deployments where a high degree of independence of the network nodes is expected to reduce operational costs.