基于OFDM的认知无线电频谱分配算法
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摘要
无线频谱的紧缺是限制无线通信与服务应用持续发展的瓶颈。认知无线电(Cognitive Radio)技术被认为是解决无线频谱紧缺问题的一种新方法。认知无线电技术是无线移动通信领域的一种革命性技术,无线用户利用该技术可以智能地感知周围环境,搜索可用频谱资源,并进行动态的频谱接入,从而提高通信系统的容量和频谱利用率。
本文将认知无线电与正交频分复用(OFDM)相结合,有效地提高了认知用户频谱使用效率,增加了认知用户利用授权频谱资源的可能性。
首先,本文回顾了认知无线电技术背景和发展现状,并介绍了认知无线电的频谱分配原则、分配方式以及常见的几种分配模型,重点介绍了干扰温度模型以及OFDM自适应分配模型,为本文研究提供了理论依据。
其次,针对基于OFDM的认知无线电频谱感知作了研究,通过建立系统感知模型,得到虚警概率、检测概率和误检概率。仿真表明:增加感知符号数可以提高认知无线电的检测概率,但同时也会减小数据传输时间,因此需要根据信道效率选择最佳的感知符号数。
再次,根据系统的检测概率,基于干扰温度模型对单认知用户频谱分配算法作了研究。对拉格朗日最优分配算法、贪婪算法进行了研究并对贪婪算法进行了改进。仿真表明:拉格朗日最优分配算法信道容量最大,但是计算复杂并且分配的比特是分数;贪婪算法减小了计算量,能够保证子载波分配的比特是整数;改进的算法可以设置每个子载波分配的比特上限,并且进一步减小了计算量。
最后,在单用户分配的基础上,研究了多用户频谱分配算法。对拉格朗日松弛算法作了研究并给出一种基于比例公平的改进算法。仿真表明:本文的两种算法由于采用了OFDM的自适应分配策略,因此要优于OFDM的固定频谱分配方案。当用户数目达到40时,拉格朗日松弛算法信道总容量比固定频谱分配方案有250比特的性能提升。改进算法针对认知用户频谱资源分配不均衡的问题,采用“两部分法”并且引入比例分配因子,仿真显示,改进算法最小用户信道容量较拉格朗日松弛算法以及固定频谱分配方案有了一定的提高。
The scarceness of wireless spectrum hampers the development of wireless communication services. Cognitive Radio (CR) technology is a revolutionary technology of wireless mobile communication, which is considered as a novel method to solve the problem of the shortage of wireless spectrum. Smart users can use CR technology to sense the environment, search for available spectrum resources, and access spectrum dynamically, so that the efficiency of spectrum is improved and the capacity of wireless communication system is increased.
This thesis combines CR with OFDM, improving the utilization of spectrum effectively. It increases the possibility for the CR users to access the spectrum of authorized users.
At first, this article introduce the rule and fashion of spectrum allocation for CR, and some familiar allocation models. We make emphases on the interference temperature model and the auto adapted allocation model based on OFDM, and make them the theory for this article.
At second, aiming at the CR’s spectrum detection based on OFDM, and through building the model of spectrum sensing, we can get the alarm possibility, detection possibility and the possibility of mission detection. Simulation shows: increasing the number of sensing symbols can improve the detection possibility, but can reduce the time of data transmission. We must choose the appropriate number of symbols according to the channel efficiency.
At third, according to the detection possibility, based on the interference temperature, makes research on the spectrum allocation algorithm for single user. The optimization algorithm based on Lagrange theorem, the Greedy algorithm and the ameliorative Greedy algorithm is proposed. Simulation shows: the optimization algorithm can make the channel capacity reach the largest, but has larger computation load, and the number of allocation bits is fraction. The Greedy algorithm reduces the computation load and the number of allocation bits is integer . The amelioration algorithm can limit the bits of every sub-carrier, and reduce more computation load.
At last, this article investigates the spectrum allocation for multi-user. The flabby Lagrange algorithm and the amelioration algorithm based on proportion impartiality are proposed. Simulation shows: The two algorithms adopt the auto adapted rule, so their performances are better than the fixed spectrum allocation scheme of OFDM. When the number of users reaches 40, the channel capacity of the flabby Lagrange algorithm can increase 250 bits. The ameliorative algorithm aims at the disequilibrium of spectrum allocation, proposes the two-step allocation and import the proportion gene. Simulation shows: the minimum user’s channel capacity of ameliorative algorithm has more improvement than that of the flabby Lagrange algorithm and that of the fixed spectrum allocation scheme.
本文将认知无线电与正交频分复用(OFDM)相结合,有效地提高了认知用户频谱使用效率,增加了认知用户利用授权频谱资源的可能性。
首先,本文回顾了认知无线电技术背景和发展现状,并介绍了认知无线电的频谱分配原则、分配方式以及常见的几种分配模型,重点介绍了干扰温度模型以及OFDM自适应分配模型,为本文研究提供了理论依据。
其次,针对基于OFDM的认知无线电频谱感知作了研究,通过建立系统感知模型,得到虚警概率、检测概率和误检概率。仿真表明:增加感知符号数可以提高认知无线电的检测概率,但同时也会减小数据传输时间,因此需要根据信道效率选择最佳的感知符号数。
再次,根据系统的检测概率,基于干扰温度模型对单认知用户频谱分配算法作了研究。对拉格朗日最优分配算法、贪婪算法进行了研究并对贪婪算法进行了改进。仿真表明:拉格朗日最优分配算法信道容量最大,但是计算复杂并且分配的比特是分数;贪婪算法减小了计算量,能够保证子载波分配的比特是整数;改进的算法可以设置每个子载波分配的比特上限,并且进一步减小了计算量。
最后,在单用户分配的基础上,研究了多用户频谱分配算法。对拉格朗日松弛算法作了研究并给出一种基于比例公平的改进算法。仿真表明:本文的两种算法由于采用了OFDM的自适应分配策略,因此要优于OFDM的固定频谱分配方案。当用户数目达到40时,拉格朗日松弛算法信道总容量比固定频谱分配方案有250比特的性能提升。改进算法针对认知用户频谱资源分配不均衡的问题,采用“两部分法”并且引入比例分配因子,仿真显示,改进算法最小用户信道容量较拉格朗日松弛算法以及固定频谱分配方案有了一定的提高。
The scarceness of wireless spectrum hampers the development of wireless communication services. Cognitive Radio (CR) technology is a revolutionary technology of wireless mobile communication, which is considered as a novel method to solve the problem of the shortage of wireless spectrum. Smart users can use CR technology to sense the environment, search for available spectrum resources, and access spectrum dynamically, so that the efficiency of spectrum is improved and the capacity of wireless communication system is increased.
This thesis combines CR with OFDM, improving the utilization of spectrum effectively. It increases the possibility for the CR users to access the spectrum of authorized users.
At first, this article introduce the rule and fashion of spectrum allocation for CR, and some familiar allocation models. We make emphases on the interference temperature model and the auto adapted allocation model based on OFDM, and make them the theory for this article.
At second, aiming at the CR’s spectrum detection based on OFDM, and through building the model of spectrum sensing, we can get the alarm possibility, detection possibility and the possibility of mission detection. Simulation shows: increasing the number of sensing symbols can improve the detection possibility, but can reduce the time of data transmission. We must choose the appropriate number of symbols according to the channel efficiency.
At third, according to the detection possibility, based on the interference temperature, makes research on the spectrum allocation algorithm for single user. The optimization algorithm based on Lagrange theorem, the Greedy algorithm and the ameliorative Greedy algorithm is proposed. Simulation shows: the optimization algorithm can make the channel capacity reach the largest, but has larger computation load, and the number of allocation bits is fraction. The Greedy algorithm reduces the computation load and the number of allocation bits is integer . The amelioration algorithm can limit the bits of every sub-carrier, and reduce more computation load.
At last, this article investigates the spectrum allocation for multi-user. The flabby Lagrange algorithm and the amelioration algorithm based on proportion impartiality are proposed. Simulation shows: The two algorithms adopt the auto adapted rule, so their performances are better than the fixed spectrum allocation scheme of OFDM. When the number of users reaches 40, the channel capacity of the flabby Lagrange algorithm can increase 250 bits. The ameliorative algorithm aims at the disequilibrium of spectrum allocation, proposes the two-step allocation and import the proportion gene. Simulation shows: the minimum user’s channel capacity of ameliorative algorithm has more improvement than that of the flabby Lagrange algorithm and that of the fixed spectrum allocation scheme.
引文
1 A. Sahai, N. Hoven, R. Tandra. Some Fundamental Limits on Cognitive Radio. Allerton Conf. on Commununication, Control and Computing, USA. October 2004: 1-6
2 Yiping Xing, R. Chandramouli, S. Mangold. Dynamic Spectrum Access in Open Spectrum Wireless Networks. IEEE Journal on Selected Areas in Communications. March 2006, 24(3): 626-637
3 Carlos Cordeiro, Kiran Challapali, Dagnachew Birru, and Sai Shankar N. IEEE 802.22: The First Worldwide Wireless Standard based on Cognitive Radios. The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). 2005: 328-337
4 W. Krenik, A. Batra. Cognitive Radio Techniques for Wide Area Networks. Design Automation Conference, California,USA. June 2005: 409-412
5 J. Mitola. Cognitive Radio for Flexible Mobile Multimedia Communications. Mobile Networks and Applications. 2001, (6): 435-441
6周小飞,张宏纲.认知无线电原理及应用.北京邮电大学出版社, 2007: 17-38
7 J. Mitola, G. Maguire. Cognitive Radio: Making Software Radios more Personal. IEEE Personal Communication Magazine. 1999, 6(4): 13-18
8 Simon Haykin. Cognitive Radio: Brain-Empowered Wireless Communications. IEEE journal on Selected Areas in Communications. February 2005, 23(2): 201-220
9 T. A. Weiss, F. K. Jondral. Spectrum pooling: an Innovative Strategy for the Enhancement of Spectrum Efficiency. IEEE Radio Communications. March 2004, 22(3): 8-14
10 P. Leaves, D. Grandblaise, D. Bourse, et al. Dynamic Spectrum Allocation in Composite Reconfigurable Wireless Networks. IEEE Communications Magzine. May 2004, 22(5): 72-81
11 R. W. Broderson, D. Cabriv, I. D. O'Donnell, et al. Spectrum Sharing Radios, The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). November 2005: 371-378
12 M. M. Buddhikot, P. Kolodzy, S. Miller, et al. DTMSTTMNet: New Directions in Wireless Networking Using Coordinated Dynamica Spectrum Access. WoWMOM 2005. June 2005: 78-85
13冯文江,郭瑜,胡志远.认知无线电中的频谱感知技术.重庆大学学报. 2007, 30(11): 46-48
14杨志伟.认知无线电中的空洞探测.空间电子技术. 2006, (8): 35-38
15杨志伟,杨家玮.认知无线电中的一种干扰温度估计算法.电子技术应用. 2006, 18(12): 43-47
16丁小峰,韩方景,王振伟. OFDM系统信道估计与仿真.计算机仿真技术. 2006, 22(8): 123-126
17仲玮,曾繁声,张陆勇.超宽带认知无电的关键技术研究.中国无线电. 2007, 8(3): 20-24
18 C. Y. Wong, R. S. Cheng, K. B. Lataief, R. D. Murch, et al. Multi-user OFDM with Adaptive Subcarrier, Bit, and Power Allocation. IEEE Journal on Selected Areas in Communications. 1999, 17(10): 1747-1758
19 Guoqing Li, Hui Liu. Downlink Dynamic Resource Allocation for Multi-cell OFDMA System. IEEE Proceedings of Vehicular Technology Conference. Orlando, Florida, USA. 2003: 1698-1702
20 M. J. Marcus. Real Time Spectrum Markets and Interruptible Spectrum: New Concepts of Spectrum Use Enabled by Cognitive Radio. The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). 2005: 512-517
21 Song Qijun, Han Yunan, Huang Yan. A Centre Controlled Strategy for Adaptive Spectrum Access. Environmental Electromagnetics, The 2006 4th Asia-Pacific Conference on August. 2006: 771-774
22 Paul Houze, Sana Ben Jemaa, Pascal Cordier. Dynamic Spectrum Allocation Algorithm for Cognitive Networks. The Third IEEE International Conference on Wireless and Mobile Communications (ICWMC). March 2007: 25-27
23 Wei Wang, Xin Liu. List-Coloring Based Channel Allocation for Open-Spectrum Wireless Networks. The 62nd IEEE Vehicular TechnologyConference (VTC). 2005, (1):690-694
24 E. Altman, Z. Altman. S-Modular Games and Power Control in Wireless Networks. IEEE Transactions on Automatic Control, Vol. 48. May 2003: 839-842
25 D. Grandblaise, C. Kloeck, K. Moessner, et.al. Techno-economic of Collaborative based Secondary Spectrum Usage E2R Research Project Outcomes Overview. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). Nov 2005: 318-327
26 T. Charles Clancy, A. William. Interference Temperature Multiple Access: A New Paradigm for Cognitive Radio Networks. IEEE Communication Society. 2005, 26(5): 369-375
27 Yiping Xing, N. Chetan. Priority Based Dynamic Spectrum Access with QoS and Interference Temperature Constraints. IEEE International Conference on Communication (ICC’06). 2006, (10): 4420-4425
28 Z. Han, Z. Ji, K. J. Ray Liu, et al. Power Minimization for Multi-cell OFDM Networks Using Distributed Non-cooperative Game Approach. Proceedings of IEEE Global Telecommunications Conference Dallas, USA: IEEE Communication Society. 2004: 3742-3747
29 Li Zhen, Zhu Geqing, Wang Weihua, et al. Improved Algorithm of Multiuser Dynamic Subcarrier Allocation in OFDM System. Proceedings of ICCT. Beijing, China: Beijing University of Posts and Telecommunication Press. 2003: 1144-1147
30霍永青,彭启琮. MIMO-OFDM系统中LDPC编码和自适应比特功率分配算法研究.电子与信息学报. 2008, (02): 404-407
31 Rosenberg, Louis W. Stern. Conflict Measurement in The Distribution Channels. Journal of Marketing Research. 1971, 8(4): 437-442
32 Voorneveld, M. P. Borm. Congestion Games and Potentials Reconsidered. International Game Theory Review. 1999, (1): 283-299
33 Wong, Shin Hong, I. J. Wassell. Application of Game Theory for Distributed Dynamic Channel Allocation. IEEE 55th Vehicular Technology Conference Spring. 2002, (1): 404-408
34 Dov Monderer. Potential Games. Games and Economic Behavior. 1996, (14): 124-143
35 A. Jafari Amy, R. Greenwald. On No-Regret Learning, Fictitious Play, and Nash Equilibrium. Proceedings of the Eighteenth International Conference on Machine Learning. 2001: 226-233
36 Danijela Cabric. Implementation Issues in Spectrum Sensing for Cognitive Radios. Signals Systems and Computers. Conference Record of the Thirty-Eighth Asilomar Conference. 2004, 1(1): 772-776
37 H. Urkowitz. Energy Detection of Unknown Deterministic Signals. Proceedings of the IEEE. April 1967, 55(4): 337-380
38 Jiandong Wang, Tongwen Chen, Biao Huang. On Spectral Theory of Cyclostationary Signals in Multirate Systems. IEEE Transactions on Signal Processing. July 2005, 53(7): 2421-2431
39 W. A. Gardner. Signal Interception: A Unifying Theoretical Framework for Feature Detection. IEEE Transaction on Communications. August 1988, 36(8): 897-906
40 W. A. Gardner. Signal Interception: Performance Advantages of Cyclic-feature Detectors. IEEE Transaction on Communications. 1992, 40(1): 149-159
41刘峰飞,陆松年,杨树堂.多用户并发控制的入侵检测漏警实验系统.信息技术. 2008, (01): 29-31
42 Yongxue Wang, Fangjiong Chen, Gang Wei, et al. Adaptive Sub-carrier and Bit Allocation for Multiuser OFDM System based on Genetic Algorithm. Proceedings of International Conference on Communications, Circuits and Systems. Hong Kong, China: IEEE. 2005: 242-246
43 Yingjun Zhang, K. B. Letaief. An Efficient Resource Allocation Scheme for Spatial Multiuser Access in MIMO-OFDM Systems. IEEE Transactions on Communications. 2005, 53(1): 107-116
44 J. Campello. Optimal Discrete Bit Loading for Multicarrier Modulation Systems. International Symposium on Information Theory. Cambridge, MA,USA: IEEE. 1998: 193-193
45 R. F. H. Fischer, J. B. Huber. A New Loading Algorithm for Discrete Multitone Transmission. Proceedings of Global Telecommunications Conference. London: IEEE. 1996: 724-728
46 Changho Suh, Seunghoon, Youngkwon Cho. Efficient Algorithm forProportional Failness Scheduling in Multicast OFDM Systems. Vehicular Technology Coference. 2005, (3): 1880-1884
47 S. Sanayei, A. Nosratinia, N. Aldhahir, et al. Opportunistic Dynamic Sub-channel Allocation in Multiuser OFDM Networks with Limited Feedback. IEEE Information Theory Workshop. San Antonio, USA: IEEE. 2004: 182-186
48 C. Peng, H. Zheng, B. Y. Zhao. Utilization and Fairness in Spectrum Assignment for Opportunistic Spectrum Access. ACM Mobile Networks and Applications (MONET). 2006, 11(4): 555-576
2 Yiping Xing, R. Chandramouli, S. Mangold. Dynamic Spectrum Access in Open Spectrum Wireless Networks. IEEE Journal on Selected Areas in Communications. March 2006, 24(3): 626-637
3 Carlos Cordeiro, Kiran Challapali, Dagnachew Birru, and Sai Shankar N. IEEE 802.22: The First Worldwide Wireless Standard based on Cognitive Radios. The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). 2005: 328-337
4 W. Krenik, A. Batra. Cognitive Radio Techniques for Wide Area Networks. Design Automation Conference, California,USA. June 2005: 409-412
5 J. Mitola. Cognitive Radio for Flexible Mobile Multimedia Communications. Mobile Networks and Applications. 2001, (6): 435-441
6周小飞,张宏纲.认知无线电原理及应用.北京邮电大学出版社, 2007: 17-38
7 J. Mitola, G. Maguire. Cognitive Radio: Making Software Radios more Personal. IEEE Personal Communication Magazine. 1999, 6(4): 13-18
8 Simon Haykin. Cognitive Radio: Brain-Empowered Wireless Communications. IEEE journal on Selected Areas in Communications. February 2005, 23(2): 201-220
9 T. A. Weiss, F. K. Jondral. Spectrum pooling: an Innovative Strategy for the Enhancement of Spectrum Efficiency. IEEE Radio Communications. March 2004, 22(3): 8-14
10 P. Leaves, D. Grandblaise, D. Bourse, et al. Dynamic Spectrum Allocation in Composite Reconfigurable Wireless Networks. IEEE Communications Magzine. May 2004, 22(5): 72-81
11 R. W. Broderson, D. Cabriv, I. D. O'Donnell, et al. Spectrum Sharing Radios, The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). November 2005: 371-378
12 M. M. Buddhikot, P. Kolodzy, S. Miller, et al. DTMSTTMNet: New Directions in Wireless Networking Using Coordinated Dynamica Spectrum Access. WoWMOM 2005. June 2005: 78-85
13冯文江,郭瑜,胡志远.认知无线电中的频谱感知技术.重庆大学学报. 2007, 30(11): 46-48
14杨志伟.认知无线电中的空洞探测.空间电子技术. 2006, (8): 35-38
15杨志伟,杨家玮.认知无线电中的一种干扰温度估计算法.电子技术应用. 2006, 18(12): 43-47
16丁小峰,韩方景,王振伟. OFDM系统信道估计与仿真.计算机仿真技术. 2006, 22(8): 123-126
17仲玮,曾繁声,张陆勇.超宽带认知无电的关键技术研究.中国无线电. 2007, 8(3): 20-24
18 C. Y. Wong, R. S. Cheng, K. B. Lataief, R. D. Murch, et al. Multi-user OFDM with Adaptive Subcarrier, Bit, and Power Allocation. IEEE Journal on Selected Areas in Communications. 1999, 17(10): 1747-1758
19 Guoqing Li, Hui Liu. Downlink Dynamic Resource Allocation for Multi-cell OFDMA System. IEEE Proceedings of Vehicular Technology Conference. Orlando, Florida, USA. 2003: 1698-1702
20 M. J. Marcus. Real Time Spectrum Markets and Interruptible Spectrum: New Concepts of Spectrum Use Enabled by Cognitive Radio. The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). 2005: 512-517
21 Song Qijun, Han Yunan, Huang Yan. A Centre Controlled Strategy for Adaptive Spectrum Access. Environmental Electromagnetics, The 2006 4th Asia-Pacific Conference on August. 2006: 771-774
22 Paul Houze, Sana Ben Jemaa, Pascal Cordier. Dynamic Spectrum Allocation Algorithm for Cognitive Networks. The Third IEEE International Conference on Wireless and Mobile Communications (ICWMC). March 2007: 25-27
23 Wei Wang, Xin Liu. List-Coloring Based Channel Allocation for Open-Spectrum Wireless Networks. The 62nd IEEE Vehicular TechnologyConference (VTC). 2005, (1):690-694
24 E. Altman, Z. Altman. S-Modular Games and Power Control in Wireless Networks. IEEE Transactions on Automatic Control, Vol. 48. May 2003: 839-842
25 D. Grandblaise, C. Kloeck, K. Moessner, et.al. Techno-economic of Collaborative based Secondary Spectrum Usage E2R Research Project Outcomes Overview. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). Nov 2005: 318-327
26 T. Charles Clancy, A. William. Interference Temperature Multiple Access: A New Paradigm for Cognitive Radio Networks. IEEE Communication Society. 2005, 26(5): 369-375
27 Yiping Xing, N. Chetan. Priority Based Dynamic Spectrum Access with QoS and Interference Temperature Constraints. IEEE International Conference on Communication (ICC’06). 2006, (10): 4420-4425
28 Z. Han, Z. Ji, K. J. Ray Liu, et al. Power Minimization for Multi-cell OFDM Networks Using Distributed Non-cooperative Game Approach. Proceedings of IEEE Global Telecommunications Conference Dallas, USA: IEEE Communication Society. 2004: 3742-3747
29 Li Zhen, Zhu Geqing, Wang Weihua, et al. Improved Algorithm of Multiuser Dynamic Subcarrier Allocation in OFDM System. Proceedings of ICCT. Beijing, China: Beijing University of Posts and Telecommunication Press. 2003: 1144-1147
30霍永青,彭启琮. MIMO-OFDM系统中LDPC编码和自适应比特功率分配算法研究.电子与信息学报. 2008, (02): 404-407
31 Rosenberg, Louis W. Stern. Conflict Measurement in The Distribution Channels. Journal of Marketing Research. 1971, 8(4): 437-442
32 Voorneveld, M. P. Borm. Congestion Games and Potentials Reconsidered. International Game Theory Review. 1999, (1): 283-299
33 Wong, Shin Hong, I. J. Wassell. Application of Game Theory for Distributed Dynamic Channel Allocation. IEEE 55th Vehicular Technology Conference Spring. 2002, (1): 404-408
34 Dov Monderer. Potential Games. Games and Economic Behavior. 1996, (14): 124-143
35 A. Jafari Amy, R. Greenwald. On No-Regret Learning, Fictitious Play, and Nash Equilibrium. Proceedings of the Eighteenth International Conference on Machine Learning. 2001: 226-233
36 Danijela Cabric. Implementation Issues in Spectrum Sensing for Cognitive Radios. Signals Systems and Computers. Conference Record of the Thirty-Eighth Asilomar Conference. 2004, 1(1): 772-776
37 H. Urkowitz. Energy Detection of Unknown Deterministic Signals. Proceedings of the IEEE. April 1967, 55(4): 337-380
38 Jiandong Wang, Tongwen Chen, Biao Huang. On Spectral Theory of Cyclostationary Signals in Multirate Systems. IEEE Transactions on Signal Processing. July 2005, 53(7): 2421-2431
39 W. A. Gardner. Signal Interception: A Unifying Theoretical Framework for Feature Detection. IEEE Transaction on Communications. August 1988, 36(8): 897-906
40 W. A. Gardner. Signal Interception: Performance Advantages of Cyclic-feature Detectors. IEEE Transaction on Communications. 1992, 40(1): 149-159
41刘峰飞,陆松年,杨树堂.多用户并发控制的入侵检测漏警实验系统.信息技术. 2008, (01): 29-31
42 Yongxue Wang, Fangjiong Chen, Gang Wei, et al. Adaptive Sub-carrier and Bit Allocation for Multiuser OFDM System based on Genetic Algorithm. Proceedings of International Conference on Communications, Circuits and Systems. Hong Kong, China: IEEE. 2005: 242-246
43 Yingjun Zhang, K. B. Letaief. An Efficient Resource Allocation Scheme for Spatial Multiuser Access in MIMO-OFDM Systems. IEEE Transactions on Communications. 2005, 53(1): 107-116
44 J. Campello. Optimal Discrete Bit Loading for Multicarrier Modulation Systems. International Symposium on Information Theory. Cambridge, MA,USA: IEEE. 1998: 193-193
45 R. F. H. Fischer, J. B. Huber. A New Loading Algorithm for Discrete Multitone Transmission. Proceedings of Global Telecommunications Conference. London: IEEE. 1996: 724-728
46 Changho Suh, Seunghoon, Youngkwon Cho. Efficient Algorithm forProportional Failness Scheduling in Multicast OFDM Systems. Vehicular Technology Coference. 2005, (3): 1880-1884
47 S. Sanayei, A. Nosratinia, N. Aldhahir, et al. Opportunistic Dynamic Sub-channel Allocation in Multiuser OFDM Networks with Limited Feedback. IEEE Information Theory Workshop. San Antonio, USA: IEEE. 2004: 182-186
48 C. Peng, H. Zheng, B. Y. Zhao. Utilization and Fairness in Spectrum Assignment for Opportunistic Spectrum Access. ACM Mobile Networks and Applications (MONET). 2006, 11(4): 555-576