感知無線電 (CR) 技術, 可以提高珍貴頻譜的利用效率並且最近已經受到多方關注。 頻譜感測技術確保頻譜帶是否空閒與否是 CR 的核心技術。 在本文中, 我們將集中在頻譜感測問題在分佈式認知無線電網絡, 其中有一個主使用者和多個次要使用者 (SU), 且通道是有干擾的。 基於能量檢測器, 主使用者使用狀況的分散式偵測機制和次要使用者的吞吐量分析將會被 AND,OR, 和 majority 規則所實現。 在限制總次使用者的發射功率下我們討論了次使用者個數增加和其吞吐量之間的權衡問題。 另外一個在感知無線電網路中有趣的問題就是次要使用者的傳輸能量分配和在限制總次要使用者的傳輸能量和對主使用者的干擾下去最大化吞吐量也在這篇論文中被研究。 PBPO 方法被用於解決上述優化問題。 被用來評估我們提出問題的性能的數值模擬被灌徹於實例中。

The cognitive radio(CR) technology that can raise the utilization efficiency of precious frequency spectrum has been received much attention recently. The spectrum sensing technology determining whether a spectrum band is idle or not is the heart of CR techniques. In this thesis, we will focus on the spectrum sensing issue in a distributed cognitive radio network where there are one primary channel and multiple secondary user (SU) pairs, and the co-channel interference is present. Based on the energy detector, the distributed detection scheme of primary channel usage is derived and the throughput analysis of secondary user pairs is then carried
out by employing the AND, OR, and Majority fusion rules. Under the constraint on total
transmission power of SUs, we discuss the tradeoff problem between the increase of the number of SUs and the SU throughput. Another interesting issue of the secondary users’ transmission power allocation and throughput maximization in a distributed cognitive radio system is also studied under the constraints on the total SU transmit power and the interference thresholds on the PU and other SUs. The person by person optimization (PBPO) approach is used to solve the above optimization problem. Numerical simulations to evaluate the performance of the proposed issues are carried out for illustrations.

Approval Sheet for Thesis i
Acknowledgements ii
Chinese Abstract iii
English Abstract iv
1 Introduction 1
1.1 Overveiw of Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 System model 6
2.1 Spectrum Sensing In a Distributed CR Network . . . . . . . . . . . . . . . . . . 6
2.2 Decision Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Markov Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Performance Analysis 14
3.1 Reduce Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . . 14
Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 Tradeoff between the throughput and number of SUs . . . . . . . . . . . . . . . 24
3.4 Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4 Conclusion 39

[1] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal
on Selected Areas in Communications, vol. 23, pp. 201–220, February 2005.
[2] I. F. Akyildiz, W.-Y. Leea, M. C. Vuran, and S. Mohantya, “Next generation/dynamic
spectrum access/cognitive radio wireless networks a survey,” Comput. Netw, vol. 50, pp.
2127–2159, September 2006.
[3] S. Gupta and A. Kumar, “Spectrum sensing techniques and issues in cognitive radio,”
International Jurnal of Advanced Technology in Engineering an Science, vol. 2, no. 5, pp.
75–83, May 2014.
[4] X. Xing, T. Jing, W. Cheng, Y. Huo, and X. Cheng, “Spectrum prediction in cognitive
radio networks,” IEEE Wireless Communications, vol. 20, no. 2, pp. 90–96, April 2013.
[5] I. F. Akyiliz, “Cognitive radio networks,” School of Electrical and computer Engineering,
Georgia Institute of Technology, Class slides, 2007.
[6] A. Dandawat and G. Giannakis, “Statistical test for presence of cyclostationarity,” IEEE
Transactions on Signal Processing, vol. 42, no. 9, pp. 2355–2369, September 1994.
[7] Z. Ye, J. Grosspietsch, and G. Memik, “Spectrum sensing using cyclostationary spectrum
density for cognitive radios,” in Proc. of the 2007 IEEE Workshop on Signal Processing
Systems, Shanghai, China, October 2007, pp. 1–6.
[8] G. Huang and J. Tugnait, “On cyclostationarity based spectrum sensing under uncertain
41
Gaussian noise,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 2042–2054,
April 2013.
[9] H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceedings of the
IEEE, vol. 55, no. 4, pp. 523–531, 1967.
[10] A. Bagwari, G. Tomar, and S. Verma, “Cooperative spectrum sensing based on two-stage
detectors with multiple energy detectors and adaptive double threshold in cognitive radio
networks,” Canadian Journal of Electrical and Computer Engineering, vol. 36, no. 4, pp.
172–180, 2013.
[11] S. M. Mishra, A. Sahai, and R. W. Brodersen, “Cooperative sensing among cognitive
radios,” in Proc. of the IEEE ICC 2006, Glasgow, Scotland, UK, October 2007, pp. 2534–
2539.
[12] 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, April 2008.
[13] 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, November 2009.
[14] G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio– part I: Two user
networks,” IEEE Transactions on Wireless Communications, vol. 6, no. 6, pp. 2204–2213,
June 2007.
[15] ——, “Cooperative spectrum sensing in cognitive radio– part II: Multiuser networks,”
IEEE Transactions on Wireless Communications, vol. 6, no. 6, pp. 2214–2222, June 2007.
[16] J. Unnikrishnan and V. V. Veeravalli, “Cooperative sensing for primary detection in cogni-
tive radio,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 18–27,
February 2008.

[17] G. Noh, J. Lee, H. Wang, S. Kim, S. Choi, and D. Hong, “Throughput analysis and op-
timization of sensing-based cognitive radio systems with Markovian traffic,” IEEE Trans-
actions on Vehicular Technology, vol. 59, no. 8, pp. 4163–4169, October 2010.
[18] J. Lund´en, V. Koivunen, A. Huttunen, and H. V. Poor, “Collaborative cyclostationary
spectrum sensing for cognitive radio systems,” IEEE Transactions on Signal Processing,
vol. 57, no. 11, pp. 4182–4195, November 2009.
[19] J. Shen, T. Jiang, S. Liu, and Z. Zhang, “Maximum channel throughput via cooperative
spectrum sensing in cognitive radio networks,” IEEE Transactions on Wireless Commu-
nications, vol. 8, no. 10, pp. 5166–5175, October 2009.
[20] L. B. Le and E. Hossain, “Resource allocation for spectrum underlay in cognitive radio
networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 12, pp. 5306–5315,
December 2008.
[21] K. Cumanan, R. Krishna, L. Musavian, and S. Lambotharan, “Joint beamforming and
user maximization techniques for cognitive radio networks based on branch and bound
method,” IEEE Transactions on Wireless Communications, vol. 9, no. 10, pp. 3082–3092,
October 2010.
[22] H. Yao, Z. Zhou, H. Liu, and L. Zhang, “Optimal power allocation in joint spectrum
underlay and overlay cognitive radio networks,” in Proc. of IEEE Cognitive Radio Oriented
Wireless Networks and Communications (CrownCom ’09), Hannover, Germany, June 2009,
pp. 1–5.
[23] E. Jorswieck and J. Lv, “Spatial shaping in cognitive MIMO MAC with coded legacy
transmission,” in Proc. of the IEEE 12th International Workshop on Signal Processing
Advances for Wireless Communications (SPAWC 2011), San Francisco, USA, June 2011,
pp. 451–455.
[24] S. Gmira, A. Kobbane, and E. Sabir, “A new optimal hybrid spectrum access in cognitive
radio: Overlay-underlay mode,” International 2015 Conference on Wireless Networks and
Mobile Communications, pp. 1–7, 2015.
[25] V. Chakravarthy, X. Li, M. A. T. Z. Wu, F. Garber, R. Kannan, and A. Vasilakos, “Novel
overlay/underlay cognitive radio waveforms using sd-smse framework to enhance spec-
trum efficiency? part i: Theoretical framework and analysis in awgn channel,” IEEE
Trans.Commu., vol. 57, pp. 3794–3804, December 2009.
[26] S. Parsaeefard and A. Sharafat, “Robust distributed power control in cognitive radio net-
works,” IEEE Transactions on Mobile Computing, vol. 12, no. 4, pp. 609–620, April 2013.
[27] C.-C. Chien, H.-J. Su, and H.-J. Li, “Joint beamforming and power allocation for MIMO
relay broadcast channel with individual SINR constraints,” IEEE Transactions Vehicular
Technology, vol. 63, no. 4, pp. 1660–1667, May 2014.
[28] D. Shiung, Y.-Y. Yang, and C.-S. Yang, “Transmit power allocation for cognitive radios
under rate protection constraints: A signal coverage approach,” IEEE Transactions on
Vehicular Technology, vol. 62, no. 8, pp. 3685–3699, August 2013.
[29] M. Javan and A. Sharafat, “Distributed joint resource allocation in primary and cognitive
wireless networks,” IEEE Transactions on Communications, vol. 61, no. 5, pp. 1708–1719,
May 2013.
[30] I. Sahnoun, I. Kammoun, and M. Siala, “Energy allocation optimization for coopera-
tivecognitive network,” 2015 IEEE Wireless Communications and Networking Conference
(WCNC), pp. 932–936, March 2015.
[31] S. Baksi and D. C. Popescu, “Distributed power allocation forrate maximization in cog-
nitive radio networks with horizontal spectrum sharing,” 2014 IEEE 15th International
Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 189–
193, June 2014.
[32] G. Sivaranjani and S. Sarika, “Allocation of power for secondary users in cognitive radio
network,” International Journal of Emerging Trends in Electrical and Electronics, vol. 2,
pp. 5–8, April 2013.
[33] M.-F. Hsu, “Performance investigation of single-user and multiuser detectors for spectrum
sensing in cognitive radio networks,” Ph.D. dissertation, Institute of Communications En-
gineering National Sun Yat-sen University, 2015.
[34] C. Grinstead and J. Snell, Introduction to Probability. Amer Mathematical Society, 1997.
[35] C. Buratti, M. Martal`o, R. Verdone, and G. Ferrari, Sensor Networks with IEEE 802.15.4
Systems: Distributed Processing, MAC, and Connectivity. Springer, 2011.
[36] M. C. C. W. Tan and R. Srikant, “Maximizing sum rate and minimizing mse on mul-
tiuser downlink: Optimality, fast algorithms and equivalence via max-min sinr,” IEEE
Transactions on Signal Processing, vol. 59, pp. 6127–6143, December 2011.
[37] X. Gong, S. A. Vorobyov, and C. C. Tellambura, “Joint bandwidth and power allocation
in wireless multi-user decode-and-forward relay networks,” in Proc. of IEEE International
Conference on Acoustics Speech and Signal Processing, Dallas, Texas, USA, March 2010,
pp. 2498–2501.
[38] T. Nadkar, V. Thumar, G. P. S. Tej, S. N. Merchant, and U. B. Desai, “Distributed
power allocation for secondary users in a cognitive radio scenario taskeen nadkar,” IEEE
Transactions on Wireless Communications, vol. 11, pp. 1576–1586, April 2012.