臺灣博碩士論文加值系統

為了提升在無線網路中的頻譜使用效率，具有能透過頻譜探測來有效地利用閒置頻譜的感知無線電網路開始受到重視。在感知無線電網路中，有一群具有優先權的主要用戶端能夠隨時獲得該用戶端頻道的使用權，而另一群具有頻譜探測功能的次要用戶端能夠藉由探測主要用戶端的存在來使用閒置中的頻道。如果主要用戶端收回了該頻道的使用權，受影響的次要用戶端必須進行頻譜切換從而造成額外的頻道切換延遲以及對主要用戶端傳輸的干擾。頻譜切換的問題在當次要用戶端使用頻譜聚合的情況下顯得更加嚴重。頻譜聚合為一項允許次要用戶端能夠同時利用多個頻道進行資料傳輸以提高頻寬的技術。但是，使用越多的頻道意味著在傳輸的過程中被主要用戶端中斷的機會也會提高。有鑑於此，如何能在由主要用戶端主導的感知無線系統中降低次要用戶端進行頻譜切換的次數，以確保其傳輸品質為一項重要的議題。
在本篇論文中，我們探討了在多頻道選擇中頻譜聚合與頻譜切換之間的議題。這項議題是為了替次要用戶端尋找一群適合的頻道供其傳輸，同時這些頻道能在被主要用戶端收回之前盡可能的傳輸更多的資料，以減少在次要用戶端的傳輸過程中需要進行頻譜切換的次數，同時降低次要用戶端的傳輸延遲。在多頻道選擇中，我們提出了多個在感知無線電網路上考量頻譜切換的動態頻譜聚合策略。在實驗模擬中，我們在具有異質性頻道的網路環境上比較了我們提出的頻道聚合演算法。實驗結果顯示我們所提出的方法能夠有效地降低頻譜切換的影響，同時也能提升次要用戶端約35%的效能產出。

To enhance spectrum utilization in wireless connections, the concept of cognitive radio networks has been proposed to effectively utilize unoccupied spectrum through spectrum cognition. In cognitive radio networks, a group of primary users are privileged to access its licensed channel, while another group of secondary users equipped with cognitive radio can detect the activities of primary users and utilize idle channels. If primary user reclaims a channel used by a secondary transmission, the secondary user has to perform spectrum handoff which generates channel switching delay and interferes the quality of primary transmissions. The spectrum handoff issue becomes more serious when spectrum aggregation is utilized in secondary networks. Spectrum aggregation is known as a technique enabling secondary users to transmit on multiple idle channels simultaneously to achieve higher data rates. However, using additional channel may increase the chance of interrupt by primary users during secondary transmissions. To this end, it is important to reduce the number of spectrum handoff for a secondary user in order to provide stable transmission under the dominance of primary users in cognitive radio systems.
In this thesis, we considered the tradeoff between spectrum aggregation and spectrum handoff in multi-channel selection problem. The problem is to find an appropriate set of channels for secondary users who can transmit more data before the channels being reclaimed by primary users. We have to reduce the number of spectrum handoff required and the propagation delay for secondary transmission. For multi-channel selection, we propose multiple dynamic spectrum aggregation strategies with the consideration of spectrum handoff in cognitive radio networks. In the simulation, we evaluate multiple spectrum aggregation algorithms under heterogeneous channel characteristics. Simulation results demonstrate that the proposed algorithm can reduce the effect of spectrum handoff and enhance throughout at about 35% for secondary transmissions.

摘要 I
Abstract III
誌謝 V
List of tables IX
List of figures X
Chapter 1 Introduction 1
Chapter 2 Background 4
2.1 The concept of cognitive radio networks 4
2.2 Spectrum management in cognitive radio network 6
2.2.1 Spectrum Sensing 6
2.2.2 Spectrum Decision 7
2.2.3 Spectrum Mobility 7
2.2.4 Spectrum Sharing 7
2.3 Major issues in cognitive radio networks 7
2.3.1 Spectrum handoff 8
2.3.2 Spectrum aggregation 8
2.4 Simulation tool in cognitive radio networks 10
Chapter 3 Related Work 12
3.1 Spectrum aggregation under PU collision constraints 12
3.1.1 Ergodic capacity maximization 13
3.1.2 Analysis on collision probability 13
3.1.3 Optimal solution 14
3.2 Spectrum aggregation with channel switching minimization 15
3.2.1 Channel switching minimization 15
3.2.2 Sub-optimal solution 16
Chapter 4 Proposed Scheme 17
4.1 Notations 17
4.2 System model 18
4.3 Problem formulation 26
4.3.1. Idle-period-based maximization 27
4.3.2. Probability-based maximization 27
4.3.3. Holding-time-based maximization 28
4.4 Dynamic spectrum aggregation algorithm 29
4.4.1 Idle-period-based dynamic spectrum aggregation (IPDSA) 30
4.4.2 Probability-based dynamic spectrum aggregation (PRDSA) 33
4.4.3 Holding-time-based dynamic spectrum aggregation (HTDSA) 36
Chapter 5 Performance evaluation 39
5.1 Simulation environment 39
5.2 Simulation result 42
5.2.1 Effect of different SU nodes 43
5.2.2 Effect of different SU connections 45
5.2.3 Effect of PU transition rate α 47
5.2.4 Effect of PU transition rate β 49
Chapter 6 Conclusion 51
Bibliography 52

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