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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Galindo-Serrano, A. Giupponi, L. |
| Copyright Year | 2010 |
| 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. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 316456 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424425181 |
| ISSN | 15502252 |
| DOI | 10.1109/VETECS.2010.5493950 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-16 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Interference Femtocell networks Multiagent systems Resource management Frequency conversion Land mobile radio cellular systems Power system modeling Base stations Radio spectrum management Power system management |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Electrical and Electronic Engineering Computer Science Applications |
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