感知無線電系統具備主動頻譜偵測能力，感測無線環境與頻譜使用情形，動態存取閒置頻譜進而改善整體頻譜使用效率，近年來相當受到關注。感知無線電系統的關鍵技術之一是如何選擇適當的頻譜偵測時間以及判斷頻譜擁有者是否存在之決策門檻來達到傳輸吞吐量最大化，同時確保頻譜擁有者受到妥善保護。我們首先探討在寬頻感知無線電系統中，權衡頻譜偵測能力與傳輸吞吐量之最佳化問題。在給定一個正確偵測機率做為目標之下，進行傳輸吞吐量最大化的研究。與既有的研究不同之處在於我們的方法考慮了所有可能發生的事件在我們的目標函數中，以此目標函數進行傳輸吞吐量最大化之分析。研究結果顯示適當的決策頻譜偵測時間可以使得感知無線電系統之傳輸吞吐量達到最佳，同時能確保頻譜擁有者所受到干擾之機率低於門檻值。
在另一方面，由於無線通到環境以及頻譜擁有者之動態隨時間在改變。是故，感知無線電使用者必須動態的調整其傳輸速率以使其系統之吞吐量達到最佳。在本篇論文的第二部分，我們探討在不同環境條件下，設計滿足使用者服務品質需求的傳輸速率控制機制。我們綜合考量頻譜偵測結果以及無線通道統計特性，建構一個傳輸吞吐量最大化之目標函數；藉由此目標函數可計算出最適當的傳輸速率使得感知無線電系統之傳輸吞吐量達到最佳。在傳輸吞吐量之外，封包延遲也是使用者服務品質的重要指標之一；我們提出一個低封包延遲的感知無線電傳輸管理機制。感知無線電使用者根據其封包延遲需求以及封包排程長度來決定其傳輸時機。藉由此機制，感知無線電使用者動態的調整其存取頻譜時機以及傳輸速率來達到低封包延遲之傳輸效能，同時避免主要使用者受到嚴重的干擾。我們透過電腦模擬驗證了我們所提出的方法，不僅能達到低封包延遲以及最佳傳輸吞吐量，且能滿足頻譜擁有者受到干擾的機率低於給定之門檻值。

In this dissertation, we present a new look at spectrum management with quality-of-service (QoS) provisioning for wideband cognitive radio (CR) networks. To maximize the throughput performance of a CR network while maintaining the protection of primary users (PUs), the sensing time and the decision threshold for spectrum sensing must be determined appropriately. In the first part of this dissertation, we formulate an optimization problem to balance the sensing-throughput tradeoff for a CR network with wideband spectrum sensing. Our objective is to maximize the throughput of a CR user’s link under the constraint of a given target detection probability. Different from previous works, our approach adopts an objective function with a combined aggregate throughput for both possible scenarios of the CR network, thus achieving the maximum throughput performance under a given interference imposed on the primary network.
Due to the nature of wireless fading channels and PU activities, it is necessary to adjust the transmission rate of a CR user according to the channel capacity. In the second part of this dissertation, we present a transmission rate control policy to satisfy QoS requirements of CR users, where no interference constraints of PUs are considered. We consider the spectrum sensing results and the statistical behaviors of Rayleigh fading channels jointly to determine a transmission rate such that the effective throughput of a CR user’s link is maximized, where the effective throughput is defined as the data rate successfully received by the destination. Simulation results demonstrate that a CR user’s link with the proposed transmission rate control policy achieves the maximum effective throughput performance under Rayleigh fading channels.
Furthermore, we propose a low-latency transmission strategy for CR networks under an interference constraint of PUs. The proposed scheme dynamically regulates the transmission rate and the spectrum access policy for a CR user according to PU activities and the CR transmitter’s queue length; that is, a CR transmitter with a larger queue length will have a higher transmission opportunity such that the high-latency and buffer-overflow problems could be controlled. In the proposed scheme, two cost functions (i.e., transmit or suspend) are formulated to confine the QoS performance and the interference probability to an acceptable level; the CR user chooses the lower-cost action for spectrum access. Simulation results show that the proposed low-latency transmission control strategy achieves the maximum effective throughput performance with a low packet delay while maintaining the interference probability.

[1] Federal Communications Commission (FCC), “Spectrum policy task force report,” Rep. ET Docket no. 02–155, November 2002.
[2] M. M. Buddhikot, “Understanding dynamic spectrum access: models, taxonomy and challenges,” in Proceedings of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), April 2007, pp. 649–663.
[3] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.
[4] J. Mitola and G. Q. Maguire, “Cognitive radios: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, Aug. 1999.
[5] F. K. Jondral, “Software-defined radio: basics and evolution to cognitive radio,” EURASIP Journal on Wireless Communications and Networking, vol. 5, no. 3, pp. 275–283, 2005.
[6] R. V. Prasad, P. Pawlczak, J. A. Hoffmeyer, and H. S. Berger, “Cognitive functionality in next generation wireless networks: standardization efforts,” IEEE Communications Magazine, vol. 46, no. 4, pp. 72–78, 2008.
[7] A. Goldsmith, S. A. Jafar, I. Maric, and S. Srinivasa, “Breaking spectrum gridlock with cognitive radios: an information theoretic perspective,” IEEE Proceedings, vol. 97, no. 5, May 2009.
[8] Shared Spectrum Company. (2005, Aug.). Comprehensive spectrum occupancy measurements over six different locations. [Online]. Available: http://www.sharedspectrum.com/?section=nsf_summary
[9] B. A. Fette and B. Fette, Cognitive Radio Technology, Newnes, 2006.
[10] E. Hossan, D. Niyato, and Z. Han. Dynamic Spectrum Access and Management in Cognitive Radio Networks, Cambridge, 2009.
[11] IEEE 802.22, Working Group on Wireless Regional Area Networks (WRAN), http://grouper.ieee.org/groups/802/22/.
[12] IEEE 802.16-2004, IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems, 7/2004.
[13] R. V. Prasad, P. Pawlczak, J. A. Hoffmeyer, and H. S. Berger, “Cognitive functionality in next generation wireless networks: standardization efforts,” IEEE Communications Magazine, vol. 46, no. 4, pp. 72–78, 2008.
[14] Q. Zhao and M. Sadler, “A survey of dynamic spectrum access,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79–89, 2007.
[15] W. Lehr and J. Crowcroft, “Managing shared access to a spectrum commons,” in Proceedings of IEEE Internal Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), November 2005, pp. 420–444.
[16] D. Hatfield and P. Weiser, “Property rights in spectrum: taking the next step,” in Proceedings of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), November 2005, pp. 43–55.
[17] Q. Pang and V. C. M. Leung, “Channel clustering and probabilistic channel visiting techniques for WLAN interference mitigation on Bluetooth device,” IEEE Transactions on Electromagnetic Compatibility, vol. 49, no. 4, pp. 914–923, 2007.
[18] S. Jeng, C. Tsung, and F. Chang, “WLAN smart antenna with Bluetooth interface reduction,” IET Communications, vol. 2, no. 8, pp. 1198–1207, 2008.
[19] L. Angrisani, M. Bertocco, D. Dortin, and A. Sona, “Experimental study of coexistence issues between IEEE 802.11b and IEEE 802.15.4 wireless networks,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 8, pp. 1514–1523, 2008.
[20] S. Pollin, I. Tan, B. Hodge, C. Chun, and A. Bahai, “Harmful coexistence between 802.15.4 and 802.11: a measurement-based study,” in Proceedings of International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), May 2008.
[21] W. Yuan, X. Wang, and J.-P. Linnartz, “A coexistence model of IEEE 802.14.5 and IEEE 802.11b/g,” in Proceedings of IEEE Symposium on Communications and Vehicular Technology in the Benelux, November 2007.
[22] C. Cordeiro, K. Challapali, and M. Ghosh, “Cognitive PHY and MAC layers for dynamic spectrum access and sharing of TV bands,” in Proceedings of International Workshop on Technology and Policy for Accessing Spectrum (TAPAS). ACM, 2006, pp. 3.
[23] M. Biagi and F. Cuomo, “An opportunistic access scheme through distributed interference control for MIMO cognitive nodes,” IEEE Transactions on Wireless Communications, vol. 12, no. 12, pp. 6500–6513, 2013.
[24] Z. Shi, T. Tan, K. C. Teh, and K. H. Li, “Energy efficient cognitive radio network based on multiband sensing and spectrum sharing,” IET Communications, vol. 8, no. 9, pp. 1499–1507, 2013.
[25] S. Akin and M. C. Gursoy, “Performance analysis of cognitive radio systems under QoS constraints and channel uncertainty,” IEEE Transactions on Wireless Communications, vol. 10, no. 9, pp. 2883–2895, 2011.
[26] R. Zhang, F. Gao, and Y.-C. Liang, “Cognitive beamforming made practical: effective interference channel and learning-throughput tradeoff,” IEEE Transactions on Communications, vol. 58, no. 2, pp. 706–718, 2010.
[27] C. G. Tsinos and K. Berberidis, “Blind opportunistic interference alignment in MIMO cognitive radio systems,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 3, no. 4, pp. 626–639, 2013.
[28] M. H. Hassan and M. J. Hossain, “Cooperative beamforming for cognitive radio systems with asynchronous interference to primary user,” IEEE Transactions on Wireless Communications, vol. 12, no. 11, pp. 5468–5479, 2013.
[29] B. Gao, J.-M. Park, Y. Tang, and S. Roy, “A taxonomy of coexistence mechanisms for heterogeneous cognitive radio networks operating in TV white spaces,” IEEE Wireless Communications, vol. 19, no. 4, pp. 41–48, 2012.
[30] B. Gao, J.-M. Park, and Y. Tang, “Uplink soft frequency reuse for self-coexistence of cognitive radio networks,” IEEE Transactions on Mobile Computing, vol. 13, no. 6, pp. 1366–1378, 2014.
[31] V. Gardellin, S. K. Das, and L. Lenzini, “Self-coexistence in cellular cognitive radio networks based on the IEEE 802.22 standard,” IEEE Wireless Communications, vol. 20, no. 2, pp. 52–59, 2013.
[32] W. Zhang and U. Mitra, “Spectrum shaping: a new perspective on cognitive radio-part I: coexistence with coded legacy transmission,” IEEE Transactions on Communications, vol. 58, no. 6, pp. 1857–1867, 2010.
[33] Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 4, pp. 1326–1337, 2008.
[34] S. Stotas and A. Nallanathan, “Overcoming the sensing-throughput tradeoff in cognitive radio networks,” in Proceedings of IEEE International Conference on Communications (ICC), 2010, pp. 1–5.
[35] E. C. Y. Peh, Y.-C. Liang, Y. L. Guan, and Y. Zeng, “Optimization of cooperative sensing in cognitive radio networks: a sensing-throughput tradeoff view,” IEEE Transactions on Vehicular Technology, vol. 58, no. 9, pp. 5294–5299, 2009.
[36] E. C. Y. Peh, Y.-C. Liang, and Y. L. Guan, “Optimization of cooperative sensing in cognitive radio networks: a sensing-throughput tradeoff view,” in Proceedings of IEEE International Conference on Communications (ICC), 2009, pp. 1–5.
[37] T. Clancy and W. Arbaugh, “Measuring interference temperature,” in Proceedings of Virginia Tech Wireless Personal Communications Symposium, June 2006.
[38] T. Clancy, “Achievable capacity under the interference temperature model,” in Proceedings of IEEE International Conference on Computer Communications (INFOCOM), May 2007, pp. 794–802.
[39] F. F. Digham, M. S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” in Proceedings of 2002 IEEE International Conference on Communications (ICC 2003), May 2003, pp. 3575–3579.
[40] J. Hillenbrand, T. A. Weiss, and F. Jondral, “Calculation of detection and false alarm probabilities in spectrum pooling systems,” IEEE Communication Letter, vol. 9, no. 4, pp. 349–351, Apr. 2005.
[41] H. Urkowitz, “Energy detection of unknown deterministic signals,” IEEE Proceedings, vol. 55, pp. 523–531, Apr. 1967.
[42] Z. Ye, G. Memilk, and J. Grosspietsch, “Energy detection using estimated noise variance for spectrum sensing in cognitive radio networks,” in Proceedings 2008 IEEE Wireless Communications and Networking Conference (WCNC 2008), Mar. 2009, pp. 711–716.
[43] F. F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Transactions on Communications, vol. 55, no. 1, pp. 3575–3579, Jan. 2007.
[44] A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proceedings of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, USA, Nov. 2005.
[45] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 6th ed. San Diego, CA: Academic, 2000.
[46] M. H. Ahmed, “Call admission control in wireless networks: a comprehensive survey,” IEEE Communications Surveys and Tutorial, vol. 7, no. 1, pp. 49–68, 2005.
[47] M. Ghaderi and R. Boutaba, “Call admission control in mobile cellular networks: a comprehensive survey,” Wireless Communications and Mobile Computing, vol. 6, no. 1, pp. 69–93, 2006.
[48] D. Hong and S. S. Rappaport, “Traffic model and performance analysis for cellular mobile radio telephone systems with prioritized and nonprioritized hand procedures,” IEEE Transactions on Vehicular Technology, vol. 35, no. 3, pp. 77–92, 1986.
[49] L. L. H. Andrew, S. V. Hanly, and R. G. Mukhtar, “Active queue management for fair resource allocation in wireless networks,” IEEE Transactions on Mobile Computing, vol. 7, no. 2, pp. 231–246, 2008.
[50] W.-C. Feng, K. G. Shin, D. D. Kandiur, and D. Saha, “The blue active queue management algorithms,” IEEE Transactions on Networking, vol. 10, no. 4, pp. 513–528, 2002.
[51] L. Le, J. Aikat, K. Jeffay, and F. D. Smith, “The effects of active queue management and explicit congestion notification on web performance,” IEEE Transactions on Networking, vol. 15, no. 6, pp. 1217–1230, 2007.
[52] D. Niyato and E. Hossain, “Queue-aware uplink bandwidth allocation and rate control for polling service in IEEE 802.16 broadband wireless networks,” IEEE Transactions on Mobile Computing, vol. 5, no. 6, pp. 668–679, 2006.
[53] Q. Du and X. Zhan. “Queue-aware spectrum sensing for interference-constrained transmissions in cognitive radio networks,” in Proceedings of IEEE International Conference on Communications (ICC), May 2010.
[54] C.-S. Chang, Performance Guarantees in Communication Networks, Springer-Verlag, London, 2000.
[55] F. Chapeau-Blondeau and A. Monir, “Numerical evaluation of the Lambert W function and application to generation of generalized Gaussian noise with exponent 1/2,” IEEE Transactions on Signal Processing, vol. 50, no. 9, pp. 2160–2165, Sep. 2002.
[56] D. Hamza and S. Aissa, “An optimal probabilistic multiple-access scheme for cognitive radios,” IEEE Transactions on Vehicular Technology, vol. 61, no. 7, Sep. 2012
[57] Z. Quan, S. Cui, A. H. Sayed, and H. V. Poor, “Optimal multiband joint detection for spectrum sensing in cognitive radio network,” IEEE Transactions on Signal Processing, vol. 57, no. 3, pp. 1128–1140, Mar. 2009.
[58] P. Paysarvi-Hoseini and N. C. Beaulieu, “Optimal wideband spectrum sensing framework for cognitive radio systems,” IEEE Transactions on Signal Processing, vol. 59, no. 3, pp. 1170–1183, Feb. 2011.
[59] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2003.
[60] E. Biglieri, J. Proakis, and S. Shamai, “Fading channels: information-theoretic and communications aspects,” IEEE Transactions on Information Theory, vol. 44, no. 6, pp. 2619–2692, Oct. 1998.
[61] L. Gao, P. Wu, and S. Cui, “Power and rate control with dynamic programming for cognitive radios,” in Proceedings of IEEE Global Telecommunications Conference (GLOBECOM), Nov. 2007, pp. 1699–1703.
[62] R. M. Corless, G. H. Gonnet, D. E. G. Hare, D. J. Jeffrey, and D. E. Knuth, “On the Lambert W function,” Advances in Computational Mathematics., vol. 5, no. 4, pp. 329–359, 1996.
[63] L. Kleinrock, Queuing Systems. Vol. I: Theory. New York: Wiley, 1975.
[64] R. Loynes, “The stability of a queue with nonindependent inter-arrival and service times,” Proceedings of Cambridge Philosophical Society, vol. 58, pp. 497–520, 1962.
[65] S. Meyn and R. Tweedie, Markov Chains and Stochastic Probability. Berlin, Germany: Springer-Verlag, 1993.
[66] L. Zhang, H.-C. Yang, and M. O. Hasna, “Area spectral efficiency of underlay cognitive radio transmission over Rayleigh fading channels,” in Proceedings of 2013 IEEE Wireless Communications and Networking Conference (WCNC 2013), Apr. 2013, pp. 2988–2992.
[67] H. Hu and Q. Zhu, “Dynamic spectrum access in underlay cognitive radio system with SINR constraints,” in Proceedings of International Conference on Wireless Communications, Networking and Mobile Computing (WiCom 2009), Sep. 2009, pp. 1–4.
[68] J. Oh and W. Choi, “A hybrid cognitive radio system: a combination of underlay and overlay approaches,” in Proceedings of 2010 IEEE Vehicular Technology Conference - Fall (VTC 2010-Fall), Oct. 2010, pp. 1–5.
[69] P.-Y. Chen, S.-M. Cheng, W. C. Ao, and K.-C. Chen, “Multi-path routing with end-to-end statistical QoS provisioning in underlay cognitive radio networks,” in Proceedings of 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Mar. 2012, pp. 7–12.