In this paper, we design distributed spectrum access mechanisms with both complete and incomplete network information. We propose an evolutionary spectrum access mechanism with complete network information, and show that the mechanism achieves an equilibrium that is globally evolutionarily stable. With incomplete network information, we propose a distributed learning mechanism, where each user utilizes local observations to estimate the expected throughput and learns to adjust its spectrum access strategy adaptively over time. We show that the learning mechanism converges to the same evolutionary equilibrium on the time average. Numerical results show that the proposed mechanisms achieve up to 35 percent performance improvement over the distributed reinforcement learning mechanism in the literature, and are robust to the perturbations of users' channel selections.
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- Evolutionarily Stable Spectrum Access
- Chen, Xu (Author)
- Huang, Jianwei (Author)
- Ira A. Fulton Schools of Engineering (Contributor)
- Digital object identifier: 10.1109/TMC.2012.94
- Identifier TypeInternational standard serial numberIdentifier Value1536-1233
- This is the authors' final manuscript as accepted. © © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”, opens in a new window
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Xu Chen; Jianwei Huang, "Evolutionarily Stable Spectrum Access," Mobile Computing, IEEE Transactions on , vol.12, no.7, pp.1281,1293, July 2013 doi: 10.1109/TMC.2012.94 Published article at URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6185553&isnumber=6517173