In this work the feasibility of Reinforcement Learning
(RL) for Dynamic Spectrum Management (DSM) in the context
of next generation multicell Orthogonal Frequency Division
Multiple Access (OFDMA) networks is studied. An RL-based
algorithm is proposed and it is shown that the proposed scheme is
able to dynamically find spectrum assignments per cell depending
on the spatial distribution of the users over the scenario. In
addition the proposed scheme is compared with other fixed and
dynamic spectrum strategies showing the best tradeoff between
spectral efficiency and Quality-of-Service (QoS).
CitationBernardo, F. [et al.]. A novel framework for dynamic spectrum management in multiCell OFDMA networks based on reinforcement learning. A: IEEE Wireless Communications and Networking Conference. "IEEE Wireless Communications and Networking Conference (WCNC 2009)". Budapest: 2010, p. 1-6.
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