Research Article
Distributed spectrum management based on reinforcement learning
@INPROCEEDINGS{10.1109/CROWNCOM.2009.5189161, author={Francisco Bernardo and Ramon Agusti and Jordi Perez-Romero and Oriol Sallent}, title={Distributed spectrum management based on reinforcement learning}, proceedings={4th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2009}, month={8}, keywords={Spectrum Management Reinforcement Learning Cognitive Radio Self-organization Autonomic Systems OFDMA.}, doi={10.1109/CROWNCOM.2009.5189161} }- Francisco Bernardo
Ramon Agusti
Jordi Perez-Romero
Oriol Sallent
Year: 2009
Distributed spectrum management based on reinforcement learning
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2009.5189161
Abstract
This paper presents a novel distributed framework to decide the spectrum assignment in a primary cellular radio access network. The distributed nature of the framework allows each cell to autonomously decide (by means of machine learning procedures) the best frequencies to use in order to maximize spectral efficiency, preserve quality-of-service, and generate spectrum gaps, so that secondary cognitive radio networks can improve overall spectrum usage. The proposed distributed framework has been validated over a downlink multicell OFDMA radio access network, showing comparable performance results with respect to its centralized counterpart and superior performance with respect to fixed frequency planning schemes.
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