臺灣博碩士論文加值系統

本論文探討感知無線網路的頻譜管理問題。在此網路中，來自主要使用者的『多次中斷』將大大地影響次要使用者的通訊效能。每當次要使用者被主要使用者中斷時，次要使用者必須選擇一個適合的通道進行頻譜切換，以便繼續未完成的傳輸。很明顯地，『多次中斷』將造成多次的頻譜切換，並且增加次要使用者連線的傳輸延遲。為了從一個宏觀的角度來分析感知無線網路下『多次中斷』行為對『次要使用者連線』所造成的傳輸延遲，本論文提出一個優先權排隊理論的分析模型替感知無線網路的頻譜使用行為進行建模。藉由此模型，我們分析次要使用者的一個重要服務品質參數：『完整系統時間』。
在此論文中，基於排隊理論分析模型，我們發展具服務品質考量的頻譜管理機制，其中包括 (1) 頻譜選擇機制、(2) 頻譜切換機制、和 (3) 頻譜分享機制的設計與討論。針對這些機制的具體研究成果敘述如下：(1) 針對頻譜選擇問題，我們提出一個具有負載平衡功效的頻譜選擇機制來優化次要使用者的『完整系統時間』；(2) 針對頻譜切換問題，我們量化在多通道下多次頻譜切換對次要使用者所造成的『完整系統時間』增加量；(3) 針對頻譜分享問題，我們提出一個允入控制機制來避免主要使用者被次要使用者干擾並優化次要使用者的『完整系統時間』。我們完整探討這三種頻譜管理機制對次要使用者所造成的傳輸延遲。基於這些分析結果，在不同資料到達率與服務時間分佈下，我們可以設計相對應的頻譜管理機制來增強次要使用者連線的傳輸品質。
總而言之，本論文的主要貢獻是提出一個以排隊理論為基礎的分析模型並用多樣化的角度與觀點來對感知無線網路效能進行分析。本論文所建議之模型可以提供一個很好的感知無線網路效能之分析架構。

In this dissertation, we investigate spectrum management techniques in cognitive radio (CR) networks with quality of service (QoS) provisioning. One fundamental issue in enhancing QoS performance for the secondary users is the multiple interruptions from the primary users during each secondary user's connection. These interruptions from the primary users result in the phenomenon of multiple spectrum handoffs within one secondary user's connection. Thus, a set of target channels for spectrum handoffs are needed to be selected sequentially. In order to characterize the general channel usage behaviors with multiple handoffs from a macroscopic viewpoint, an analytical framework based on the preemptive resumption priority (PRP) M/G/1 queueing theory is introduced. Based on the PRP M/G/1 queueing network model, we can evaluate the effects of multiple handoffs on the overall system time, which is an important QoS performance measure for the secondary connections in CR networks.

The proposed analytical framework can provide important insights into the design of spectrum management techniques in CR networks. In order to demonstrate the effectiveness of this analytical model, we discuss various spectrum management techniques, consisting of spectrum decision, spectrum sharing, and spectrum mobility. For the \emph{spectrum decision} issue, we show how to determine which channels are required to probe and transmit. For the \emph{spectrum mobility} issue, we illustrate how to characterize the effects of multiple handoffs, where the secondary users can have different operating channels before and after spectrum handoff. For the \emph{spectrum sharing} issue, we explore how to determine the optimal admission probability to avoid the interference between primary and secondary users in the presence of false alarm and missed detection. From numerical results, we can develop traffic-adaptive spectrum management policies to enhance the QoS performance of the secondary users in CR networks with various traffic arrival rates and service distributions.

To summarize, the main contribution of this dissertation is to investigate the modeling techniques for CR networks from a macroscopic viewpoint based on the queueing theory. The proposed analytical framework can help analyze the performances of CR networks and provide important insights into the design of various spectrum management techniques with enhanced QoS performances.

1 Introduction 1
1.1 Problems and Solutions . . . . . . . . . . . . . . . . . . . . . . 6
1.1.1 Modeling Techniques for Cognitive Radio Networks . . 6
1.1.2 Load-Balancing Spectrum Decision . . . . . . . . . . . 7
1.1.3 Proactive Spectrum Handoff . . . . . . . . . . . . . . . 8
1.1.4 Optimal Proactive Spectrum Handoff . . . . . . . . . . 9
1.1.5 Reactive Spectrum Handoff . . . . . . . . . . . . . . . 10
1.1.6 Interference-Avoiding Spectrum Sharing . . . . . . . . 10
1.2 Dissertation Outlines . . . . . . . . . . . . . . . . . . . . . . . 11
2 Background and Literature Survey 14
2.1 Modeling Techniques for Cognitive Radio Networks . . . . . . 14
2.2 Load-Balancing Spectrum Decision . . . . . . . . . . . . . . . 16
2.2.1 Probability-based Spectrum Decision . . . . . . . . . . 16
2.2.2 Sensing-based Spectrum Decision . . . . . . . . . . . . 20
2.3 Proactive Spectrum Handoff . . . . . . . . . . . . . . . . . . . 20
2.4 Optimal Proactive Spectrum Handoff . . . . . . . . . . . . . . 24
2.5 Reactive Spectrum Handoff . . . . . . . . . . . . . . . . . . . 25
2.6 Interference-Avoiding Spectrum Sharing . . . . . . . . . . . . 28
2.6.1 Admission Control with Perfect Sensing . . . . . . . . 28
2.6.2 Admission Control without Perfect Sensing . . . . . . . 31
3 Queueing-Theoretical Modeling Techniques for Cognitive Radio Networks 32
3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2 Transmission Processes with Multiple Handoffs for the Secondary Users' Connections . . . . . . . . . . . . . . . . . . . . 34
3.3 Queueing Theoretical Framework for Spectrum Management . 37
3.3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.2 Overview of the PRP M/G/1 Queueing Network Model 37
3.3.3 Modeling of the Connection-based Channel Usage Behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.4 Two Auxiliary Parameters . . . . . . . . 42
3.3.5 Constraint . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4 Load-Balancing Spectrum Decision 46
4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.2 Spectrum Decision Behavior Model . . . . . . . . . . . 49
4.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 Performance Metric: Overall System Time . . . . . . . 51
4.3.2 Overall System Time Minimization Problem for Probability-based Channel Selection Scheme . . . . . . . . . . . . . 51
4.3.3 Overall System Time Minimization Problem for Sensing-based Channel Selection Scheme . . . . . . . . . . . . . 53
4.3.4 Performance Model . . . . . . . . . . . . . . . . . . . . 54
4.4 Analysis of Overall System Time . . . . . . . . . . . . . . . . 57
4.4.1 Extended Data Delivery Time . . . . . . . . . . . . . . 57
4.4.2 Waiting Time . . . . . . . . . . . . . . . . . . . . . . . 59
4.5 Effects of Sensing Errors . . . . . . . . . . . . . . . . . . . . . 62
4.5.1 False Alarm . . . . . . . . . . . . . . . . . . . . . . . . 63
4.5.2 Missed Detection . . . . . . . . . . . . . . . . . . . . . 64
4.6 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.6.1 Probability-based Spectrum Decision Scheme . . . . . . 66
4.6.2 Sensing-based Spectrum Decision Scheme . . . . . . . . 70
4.6.3 Comparison between Different Spectrum Decision Schemes 75
5 Proactive Spectrum Handoff 77
5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2.2 Illustrative Example of Proactive Multiple Handoffs with Multiple Interruptions . . . . . . . . . . . . . . . 80
5.3 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.4 Analysis of Extended Data Delivery Time . . . . . . . . . . . 84
5.5 Applications to Performance Analysis in IEEE 802.22 . . . . . 92
5.5.1 Derivation of Extended Data Delivery Time . . . . . . 92
5.5.2 An Example for Homogeneous Traffic Loads . . . . . . 93
5.6 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . 95
5.6.2 Effects of Various Service Time Distributions for Primary Connections . . . . . . . . . . . . . . . . . . . . . 96
5.6.3 Traffic -adaptive Target Channel Selection Principle . . 98
5.6.4 Performance Comparison between Different Channel Selection Methods . . . . . . . . . . . . . . . . . . . . 103
6 Optimal Proactive Spectrum Handoff 107
6.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 108
6.2 Cumulative Handoff Delay Analysis . . . . . . . . . . . . . . . 109
6.3 An Optimal Dynamical Programming Algorithm . . . . . . . . 112
6.3.1 State Diagram for Target Channel Sequences . . . . . . 113
6.3.2 Optimal Substructure Property . . . . . . . . . . . . . 115
6.3.3 Dynamic-Programming-Based Target Channel Selection Algorithm . . . . . . . . . . . . . . . . . . . . . . 116
6.4 A Suboptimal Low-Complexity Greedy Algorithm . . . . . . . 117
6.4.1 Greedy Target Channel Selection Strategy . . . . . . . 117
6.4.2 Greedy Target Channel Selection Algorithm . . . . . . 123
6.5 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.5.1 Effects of Traffic Statistics for Arriving Secondary User's Service Time . . . . . . . . . . . . . . . . . . . . . . . 124
6.5.2 Effects of Traffic Statistics of Existing Secondary Users' Connections . . . . . . . . . . . . . . . . . . . . . . . . 125
6.5.3 Effects of Traffic Statistics of Existing Primary Users' Connections . . . . . . . . . . . . . . . . . . . . . . . . 131
7 Reactive Spectrum Handoff 134
7.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.1.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 136
7.1.2 Illustrative Example of Reactive Multiple Handoffs with
Multiple Interruptions . . . . . . . . . . . . . . . . . . 136
7.2 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . 138
7.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . 139
7.3 Analysis of Channel Utilization Factor . . . . . . . . . . . . . 143
7.3.1 Derivations of $\omega_{i,\eta}^{(k)}$ and $\textbf{E}[\Phi_{i,\eta}^{(k)}]$ . . . . . . . . . . . . . . 145
7.3.2 An Example for the Exponentially Distributed Service Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
7.4 Analysis of Extended Data Delivery Time . . . . . . . . . . . 149
7.4.1 Derivations of $\textbf{Pr}\{\textbf{S}^{(\eta)}=\mbox{\boldmath$s$}_N\}$ . . . 149
7.4.2 An Example for the Exponentially Distributed Service Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
7.5 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . 155
7.5.1 Simulation Setting . . . . . . . . . . . . . . . . . . . . 155
7.5.2 Effects of Various Arrival Rates for the Secondary Users' Connections . . . . . . . . . . . . . . . . . . . . . . . . 155
7.5.3 Effects of Heterogeneous Arrival Rates for the Primary Users' Connections . . . . . . . . . . . . . . . . . . . . 158
7.5.4 Effects of Handoff Processing Time . . . . . . . . . . . 161
7.5.5 Comparison between Proactive and Reactive Spectrum Handoff Scheme . . . . . . . . . . . . . . . . . . . . . . 164
8 Interference-Avoiding Spectrum Sharing 168
8.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
8.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
8.2.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 171
8.2.2 Admission Control Mechanism . . . . . . . . . . . . . . 172
8.3 Problem Formulation and Analytical Model . . . . . . . . . . 172
8.3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . 172
8.3.2 Analytical Model . . . . . . . . . . . . . . . . . . . . . 174
8.4 Analysis of Constraint Functions in the Utilization Maximization Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
8.4.1 Analysis of Actual Service Time of the Primary Connection in the Physical Channel . . . . . . . . . . . . . 175
8.4.2 Analysis of Overall System Time of the Secondary Connections . . . . . . . . . . . . . . . . . . . . . . . . . . 178
8.5 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . 181
9 Conclusions 185
9.1 Modeling Techniques for Cognitive Radio Networks . . . . . . 186
9.2 Load-Balancing Spectrum Decision . . . . . . . . . . . . . . . 187
9.3 Proactive Spectrum Handoff . . . . . . . . . . . . . . . . . . . 188
9.4 Optimal Proactive Spectrum Handoff . . . . . . . . . . . . . . 188
9.5 Reactive Spectrum Handoff . . . . . . . . . . . . . . . . . . . 189
9.6 Interference-Avoiding Spectrum Sharing . . . . . . . . . . . . 190
9.7 Suggestions for Future Research . . . . . . . . . . . . . . . . . 190
Bibliography 194
Appendices 209

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