臺灣博碩士論文加值系統

隨著無線通訊需求的增加與通訊技術快速的發展，頻寬和數據傳輸速率的要求亦飛躍性地成長。另一方面，可用的頻寬不僅非常稀少也沒有被有效率地使用。為了改善無線電頻譜的使用率，有人提出了感知無線電(CR)的概念。感知無線電允許次要用戶(SU)能夠機會式地進接到和主要用戶(PU)相同的頻帶中的頻譜空洞，並提供一個有效率的方法來提升新的服務對於空閒通道的使用。傳統上主要用戶擁有對於次要用戶進接通道的佔先優先權，因此不能保證次要用戶可以取得未充分利用的通道。為了改善次要用戶的服務品質，我們考慮了多重速率的感知無線電網路(CRN)，其中不僅提供一個緩衝器給被佔先的次要用戶使用，而且對於所有次要用戶更允許頻譜租用，我們也開發了三種允許頻譜租用的連結允入控制方法，分別是CAC1、CAC2和CAC3。此外我們也提供了兩種CAC3的變型，也就是CAC3a和CAC3b。在CAC1中，各個次要用戶使用一個通道，在CAC2中，各個次要用戶的通道使用數目為大於一的定值，而在CAC3中，各個次要用戶使用的通道數是可變動的，它會在一個上下限之間。此外，因為實施了對於次要用戶的頻譜租用，我們將有執照的頻帶分割成兩個部分，也就是已租用頻譜跟未租用頻譜，並且對於所有的CAC方法主要用戶都只有在未租用頻譜中擁有佔先優先權。除此之外，為了減少對於被佔先的次要用戶的中斷機率，我們利用一個緩衝器去容納被佔先的次要用戶，直到超過他們的最大等待時間。對於考慮的所有CAC方法，針對有或無緩衝器的情況下，我們推導出相對應的解析模型，並開發了一個疊代演算法去找出穩態機率分布和計算出感興趣的效能指標。這些效能指標包含主要用戶和次要用戶的阻塞機率、成功送達率、系統平均人數和系統平均延遲，還有次要用戶的中斷機率、成本和效用。我們除了比較有無緩衝器的系統效能，還分別評估和比較各種CAC方法的效能。最後但並非最不重要的，我們使用Visual Studio 2012來撰寫系統模擬以驗證解析結果的準確性。

With the increasing requirement for wireless communications and the rapid development of communication technologies, the demands for bandwidth and data transmission rate are growing. On the other hand, the available spectrum is not only scarce but also under-utilized. To improve the utilization of radio spectrum, the concept of Cognitive Radio (CR) is proposed. Cognitive radio, which allows secondary users (SUs) to opportunistically access a spectrum hole which is in the same frequency bands as primary users (PUs), provides an efficient approach to increase the availability of idle spectrum to new services. Traditionally, PUs have the preemptive priority over SUs, and hence the acquisition of underutilized channels by SUs is not guaranteed. To improve the quality of service (QoS) of SUs, we consider multi-rate cognitive radio networks (CRNs) with not only a buffer for preempted SUs but also spectrum leasing for all SUs. Three call admission control (CAC) schemes with spectrum leasing are also developed: CAC1, CAC2, and CAC3. Moreover, we also propose two variations of CAC3, i.e., CAC3a and CAC3b. With CAC1, the bandwidth requirement of each SU is one channel, that with CAC2 is a constant bandwidth which is greater than one, and that with CAC3 is a variable bandwidth which is between a lower bound and an upper bound. In addition, spectrum leasing for SUs are enforced, where the licensed spectrum band is divided into two parts, i.e., the leased spectrum and unleased spectrum, and for all CAC schemes PUs only have the preemptive priority over SUs in the unleased spectrum. Furthermore, to mitigate the dropping probability of preempted SUs, a buffer is utilized to accommodate the preempted SUs till their maximum waiting time expires. The analytical models are derived for all considered CAC schemes with or with buffer. We develop an iterative algorithm to find the steady state probability and evaluate the performance measures of interest. The performance measures of interest include the blocking probability, throughput, the average number in the system, and the average system delay for PUs and SUs, respectively, the dropping probability for SUs, the SU cost, and the SU utility. Furthermore, we compare the performance of the system with buffer and that without buffer. We also compare the performance of CAC1, CAC2, and CAC3a (CAC3b). Last but not least, computer simulation is written in Visual Studio 2012 to validate the accuracy of the derived analytical models.

摘要
ABSTRACT
CONTENTS
List of Tables
List of Figures
1. Introduction
2. System Model
2.1 CAC1
2.2 CAC2
2.3 CAC3
3. Analytical Model
3.1 CAC1
3.1.1 Balance Equations
3.1.2 Steady State Probability Distribution
3.1.3 Performance Measures
3.2 CAC2
3.2.1 Balance Equations
3.2.2 Steady State Probability Distribution
3.2.3 Performance Measures
3.3 CAC3a
3.3.1 Balance Equations
3.3.2 Steady State Probability Distribution
3.3.3 Performance Measures
3.4 CAC3b
3.4.1 Balance Equations
3.4.2 Steady State Probability Distribution
3.4.3 Performance Measures
4. Simulation Model
4.1 CAC1
4.1.1 Main Program
4.1.2 Timing Subprogram
4.1.3 PU Arrival Subprogram
4.1.4 SU Arrival Subprogram
4.1.5 PU Departure Subprogram
4.1.6 SU Departure Subprogram
4.1.7 Buffer Drop Subprogram
4.1.8 Performance Measures
4.2 CAC2
4.2.1 Main Program
4.2.2 Timing Subprogram
4.2.3 PU Arrival Subprogram
4.2.4 SU Arrival Subprogram
4.2.5 PU Departure Subprogram
4.2.6 SU Departure Subprogram
4.2.7 Buffer Drop Subprogram
4.2.8 Performance Measures
4.3 CAC3a
4.3.1 Main Program
4.3.2 Timing Subprogram
4.3.3 PU Arrival Subprogram
4.3.4 SU Arrival Subprogram
4.3.5 PU Departure Subprogram
4.3.6 SU Departure Subprogram
4.3.7 Buffer Drop Subprogram
4.3.8 Performance Measures
4.4 CAC3b
4.4.1 Main Program
4.4.2 Timing Subprogram
4.4.3 PU Arrival Subprogram
4.4.4 SU Arrival Subprogram
4.4.5 PU Departure Subprogram
4.4.6 SU Departure Subprogram
4.4.7 Buffer Drop Subprogram
4.4.8 Performance Measures
5. Numerical Results
5.1 Comparison of CAC1 and CAC2
5.1.1 PU Mean Arrival Rate
5.1.2 PU Mean Service Rate
5.1.3 SU Mean Arrival Rate
5.1.4 SU Mean Service Rate
5.1.5 Buffer Mean Dropping Rate
5.2 Comparison of CAC1, CAC2, and CAC3a
5.2.1 PU Mean Arrival Rate
5.2.2 PU Mean Service Rate
5.2.3 SU Mean Arrival Rate
5.2.4 SU Mean Service Rate
5.2.5 Buffer Mean Dropping Rate
5.3 Comparison of CAC1, CAC2, and CAC3b
5.3.1 PU Mean Arrival Rate
5.3.2 PU Mean Service Rate
5.3.3 SU Mean Arrival Rate
5.3.4 SU Mean Service Rate
5.3.5 Buffer Mean Dropping Rate
6. Conclusions
References

[1] FCC, “Spectrum Policy Task Force Report,” Tech. Rep. Report of the Spectrum Efficiency Working Group, no. 02-155, Washington, DC, USA, Nov. 2002.
[2] J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: Making software radios more personal,” IEEE Personal Commun., Vol. 6 (4), pp. 13-18, Aug. 1999.
[3] J. M. Peha, “Sharing Spectrum Through Spectrum Policy Reform and Cognitive Radio,” Proc. Of The IEEE, vol. 97, no. 4, pp. 708-719, Apr. 2009.
[4] M. Sherman, A. N. Mody, R. Martinez, and C. Rodriguez, “IEEE Standards Supporting Cognitive Radio and Networks, Dynamic Spectrum Access, and Coexistence,” IEEE Commun. Mag., vol. 46, no. 7, pp. 72-79, Jul. 2008.
[5] Lee, J., So, J., “Analysis of Cognitive Radio Networks with Channel Aggregation,” In Proc. IEEE WCNC, Sydney, Australia, Apr. 2010.
[6] L. Jiao, F. Y. Li, and V. Pla, “Dynamic Channel Aggregation Strategies in Cognitive Radio Networks with Spectrum Adaptation,” 2011 IEEE GLOBECOM.
[7] L. Jiao, F. Y. Li and V. Pla, “Greedy Versus Dynamic Channel Aggregation Strategy in CRNs: Markov Models and Performance Evaluation,” Networking 2011 Workshops, LNCS vol. 6827, pp. 22-31, May 13, 2011.
[8] X. Mao, H. Ji, V. C. Leung, and M. Li, “Performance Enhancement for Unlicensed Users in Coordinated Cognitive Radio Networks Via Channel Reservation,” IEEE GLOBECOM, Miami, USA, Dec. 2010.
[9] I. A. M. Balapuwaduge, L. Jiao, V. Pla and F. Y. Li, “Channel Assembling with Priority-based Queues in Cognitive Radio Networks: Strategies and Performance Evaluation,” IEEE Trans. on Wireless Commun., vol. 13, no. 2, pp. 630-645, Feb. 2014.
[10] Yu-Shuen Zheng, Performance Evaluation of Call Admission Control with Buffer in Cognitive Radio Networks, Master Thesis, NTUST, 2016.
[11] A. R. Syed and K.-L. A. Yau, “Spectrum Leasing in Cognitive Radio Networks: A Survey,” Int. Journal of Distributed Sensor Networks, vol. 2014, pp. 1-22, Feb. 2014.
[12] Mario A. Ramirez Reyna, Felipe A. Cruz Pérez, Mario E. Rivero Ángeles and Genaro Hernández Valdez, “Performance Analysis of Dynamic Spectrum Leasing Strategies in Overlay Cognitive Radio Networks,” VTC2014-Fall, Vancouver, Canada, pp. 1-6, Sep. 14-17, 2014.
[13] Mario A. Ramirez Reyna, Felipe A. Cruz Pérez, Mario E. Rivero Ángeles and Genaro Hernández Valdez, “Dynamic Spectrum Leasing Strategies for Coordinated Cognitive Radio Networks with Delay-Tolerant Traffic,” PIMRC 2014, Washington, DC, USA, pp. 1200-1205, Sep. 2-5, 2014.
[14] I. A. M. Balapuwaduge, A. Rajanna, M. Kaveh and F. Y. Li, “Performance Evaluation of Three Dynamic Channel Access Strategies for Spectrum leasing in CRNs,” 2015 ICC, London, U.K., pp. 7570-7575, Jun. 8-12, 2015.
[15] I. A. M. Balapuwaduge, F. Y. Li, A. Rajanna, and M. Kaveh, “Channel Occupancy-Based Dynamic Spectrum Leasing in Multichannel CRNs: Strategies and Performance Evaluation,” IEEE Trans. on Commun., vol. 64, no. 3, pp. 1313-1328, Mar. 2016.