研究生: |
徐悠博 Hsu, Yu-Po |
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論文名稱: |
感知無線電網路中頻道選擇與會面問題的聯合決策研究 A Joint Problem of Rendezvous and Channel Selection in Cognitive Radio Networks |
指導教授: |
李端興
Lee, Duan-Shin |
口試委員: |
張正尚
Chang, Cheng-Shang 林華君 Lin, Hwa-Chun |
學位類別: |
碩士 Master |
系所名稱: |
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論文出版年: | 2018 |
畢業學年度: | 106 |
語文別: | 英文 |
論文頁數: | 28 |
中文關鍵詞: | 賽局理論 、奈許平衡 、會面問題 |
外文關鍵詞: | game theory, Nash equilibrium, rendezvous problem |
相關次數: | 點閱:1 下載:0 |
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在這篇論文中,我們首先以賽局理論來分析會面問題,並找出該賽局的奈許平衡。接著提出一個強化學習方法,希望在沒有額外資訊的情況下讓次要使用者能在閒置機率較高的頻道上見面。透過數學分析證明此強化學習方法會收斂,且其行為等同於純策略的奈許平衡。最後經由與其他方法的比較,發現此方法收斂的結果最為接近最佳解。
In cognitive radio networks, rendezvous algorithms play an important role. They aim to promise secondary users to meet on the same channel in a bounded time. But these works often neglect the influence of primary user activities. On the other way, there may be more collisions as the number of secondary users raised up. The aim of this paper is to design a method to let secondary users keep meeting on a better channel. We first model rendezvous problem as a game, then approximate the desire equilibriums using reinforcement learning. The convergence in some simple cases is proved. Some extra mechanisms are applied to handle the case of multiple secondary user pairs. Through simulations, we show the presented reinforcement learning technique is able to converge to channels with higher channel idle probabilities.
[1] I. F. Akyildiz,W.-Y. Lee, M. C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer networks, vol. 50, no. 13, pp. 2127–2159, 2006.
[2] H. Liu, Z. Lin, X. Chu, and Y.-W. Leung, “Jump-stay rendezvous algorithm for cognitive radio networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 10, pp. 1867–1881, 2012.
[3] C.-S. Chang, Y.-C. Chang, and J.-P. Sheu, “A fast multi-radio rendezvous algorithm in heterogeneous cognitive radio networks,” preprint, 2017.
[4] J.-P. Sheu and J.-J. Lin, “A multi-radio rendezvous algorithm based on chinese remainder theorem in heterogeneous cognitive radio networks,” IEEE Transactions on Mobile Computing, 2018.
[5] C. Claus and C. Boutilier, “The dynamics of reinforcement learning in cooperative multiagent systems,” AAAI/IAAI, vol. 1998, pp. 746– 752, 1998.
[6] J. F. Nash et al., “Equilibrium points in n-person games,” Proceed-ings of the national academy of sciences, vol. 36, no. 1, pp. 48–49,1950.
[7] I. Erev and A. E. Roth, “Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria,” American economic review, pp. 848–881, 1998.
[8] V. Kuleshov and D. Precup, “Algorithms for multi-armed bandit problems,” arXiv preprint arXiv:1402.6028, 2014.
[9] C. L. Watson and S. Biswas, “Q-learning and p-persistent csma based rendezvous protocol for cognitive radio networks operating with shared spectrum activity,” in Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2014, vol. 9096. International Society for Optics and Photonics, 2014, p. 909602.