Title :
Distributed Q-Learning for Interference Control in OFDMA-Based Femtocell Networks
Author :
Galindo-Serrano, Ana ; Giupponi, Lorenza
Author_Institution :
Parc Mediterrani de la Tecnol., Centre Tecnol. de Telecomunicacions de Catalunya (CTTC), Barcelona, Spain
Abstract :
This paper proposes a self-organized power allocation technique to solve the interference problem caused by a femtocell network operating in the same channel as an orthogonal frequency division multiple access cellular network. We model the femto network as a multi-agent system where the different femto base stations are the agents in charge of managing the radio resources to be allocated to their femtousers. We propose a form of real-time multi-agent reinforcement learning, known as decentralized Q-learning, to manage the interference generated to macro-users. By directly interacting with the surrounding environment in a distributed fashion, the multi-agent system is able to learn an optimal policy to solve the interference problem. Simulation results show that the introduction of the femto network increases the system capacity without decreasing the capacity of the macro network.
Keywords :
OFDM modulation; cellular radio; frequency division multiple access; interference suppression; learning (artificial intelligence); multi-agent systems; telecommunication computing; OFDMA-based femtocell networks; distributed Q-learning; interference control; multiagent system; orthogonal frequency division multiple access cellular network; real-time multiagent reinforcement learning; self-organized power allocation technique; Base stations; Femtocell networks; Frequency conversion; Interference; Land mobile radio cellular systems; Multiagent systems; Power system management; Power system modeling; Radio spectrum management; Resource management;
Conference_Titel :
Vehicular Technology Conference (VTC 2010-Spring), 2010 IEEE 71st
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2518-1
Electronic_ISBN :
1550-2252
DOI :
10.1109/VETECS.2010.5493950