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.
CitationBernardo, F. [et al.]. Distributed spectrum management based on reinforcement learning. A: International Conference on Cognitive Radio Oriented Wireless Networks and Communications. "4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications". Hannover: 2009, p. 1-6.
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