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.

中文摘要......................................i
Abstract....................................ii
Acknowledgements...........................iii
List of Figures..............................v
List of Tables.............................vii
1 Introduction...............................1
2 System Model and Game Analysis.............3
3 A Reinforcement Learning Approach..........9
4 Extra Mechanisms..........................18
5 Simulation Results........................22
6 Conclusions...............................26
Bibliography................................27

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