认知移动通信系统中基于频谱空洞分类的频谱分配策略研究
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摘要
随着通信技术与社会经济的发展,频谱资源对于现代社会无线通信领域尤其是移动通信系统来说是一种不可或缺的宝贵资源。由于现有固定的频谱分配策略的限制,许多授权频谱的利用率比较低,使得频率资源更加紧张。为了提高授权频谱的利用率,人们提出了认知无线电技术。
隐蔽站问题和暴露站问题是认知无线电频谱检测和频谱分配中的具有挑战性的问题,直接影响了认知无线电能否正常、高效的发挥作用。本文针对认知无线电中的隐蔽站和暴露站问题,分析了主从系统之间的相互干扰。根据从用户发射机和接收机对可用频率的不同要求,将频谱空洞进行了分类,分为发射频谱空洞和接收频谱空洞。进一步分析了两种频谱空洞在频谱检测方面的差异性,以及这种频谱空洞分类对于不同双工系统所带来的影响。
进一步,基于频谱空洞分类的思想,在认知系统为移动通信系统的场景下,本文研究并提出了一种基于频谱空洞分类的频谱分配方案。其中对从系统小区内的频谱分配、小区间的频谱分配以及频谱空洞退让和复检进行了详细分析,保证从系统之间以及主从系统之间均不会产生频谱碰撞。
最后,通过数学证明和仿真平台验证的方式对本文提出的基于频谱空洞分类的频谱分配方案进行验证。首先通过数学证明的方式验证了本文提出的基于频谱空洞分类思想对于隐蔽站问题和暴露站问题的解决能力；进一步通过系统级仿真对本方案和基于传统发射机检测的对比方案进行了仿真,从频谱检测结果、频谱检测漏检率、误检率以及从系统平均吞吐量多个角度进行了分析。仿真结果表明,本方案在频谱检测的准确性和从系统吞吐量等方面有了明显的性能提升
With the development of wireless communication technology and socio-economic, spectrum resource is an indispensable precious resource for wireless communication in modern society, especially mobile communications system. However, because of the existing fixed spectrum allocation policy constraints, the frequency utilization of many licensed spectrum is relatively low, making frequency resources much tenser. In order to improve the frequency utilization of licensed spectrum, cognitive radio technology is proposed.
Hidden terminal and exposed terminal problem is one of the most challenging issues of cognitive radio spectrum sensing and spectrum allocation, which will make a direct impact on the performance of cognitive radio. In this paper, hidden terminal and exposed terminal problem and the mutual interference between primary and secondary systems are analyzed. According to the different requirements of the available frequencies for secondary transmitters and receivers, the spectrum holes have been classified to transmit spectrum holes and receive spectrum holes. Further the differences of transmit/receive spectrum hole sensing are analyzed, as well as the impact to different duplex systems caused by spectrum hole classification.
Additionally, based on the spectrum holes classification, a novel spectrum allocation scheme in cognitive mobile communication system has been investigated. In this scheme the spectrum allocation for inter-cell and intra-cell is analysized detailed, as well as the spectrum hole concession and re-examination, to ensure the spectrum collision beween primary systems and secondary systems will not happen.
Finally, the performance of spectrum allocation scheme proposed in this paper based on spectrum holes classification is validated by the mathematical proof and system level simulation platform. Firstly, the ability to solve hidden terminal and exposed terminal problem of the proposed scheme is verified by mathematical proof. secondly, system level simulation platform is designed for the proposed scheme and the comparison scheme based on traditional transmitter sensing, and the pectrum sensing result, throughput of secondary system, unexpected rate and falsed rate of spectrum sensing are exported. The simulation results show that the proposed spectrum allocation scheme has significant performance gains in the spectrum detection accuracy and throughput of secondary systems.
隐蔽站问题和暴露站问题是认知无线电频谱检测和频谱分配中的具有挑战性的问题,直接影响了认知无线电能否正常、高效的发挥作用。本文针对认知无线电中的隐蔽站和暴露站问题,分析了主从系统之间的相互干扰。根据从用户发射机和接收机对可用频率的不同要求,将频谱空洞进行了分类,分为发射频谱空洞和接收频谱空洞。进一步分析了两种频谱空洞在频谱检测方面的差异性,以及这种频谱空洞分类对于不同双工系统所带来的影响。
进一步,基于频谱空洞分类的思想,在认知系统为移动通信系统的场景下,本文研究并提出了一种基于频谱空洞分类的频谱分配方案。其中对从系统小区内的频谱分配、小区间的频谱分配以及频谱空洞退让和复检进行了详细分析,保证从系统之间以及主从系统之间均不会产生频谱碰撞。
最后,通过数学证明和仿真平台验证的方式对本文提出的基于频谱空洞分类的频谱分配方案进行验证。首先通过数学证明的方式验证了本文提出的基于频谱空洞分类思想对于隐蔽站问题和暴露站问题的解决能力；进一步通过系统级仿真对本方案和基于传统发射机检测的对比方案进行了仿真,从频谱检测结果、频谱检测漏检率、误检率以及从系统平均吞吐量多个角度进行了分析。仿真结果表明,本方案在频谱检测的准确性和从系统吞吐量等方面有了明显的性能提升
With the development of wireless communication technology and socio-economic, spectrum resource is an indispensable precious resource for wireless communication in modern society, especially mobile communications system. However, because of the existing fixed spectrum allocation policy constraints, the frequency utilization of many licensed spectrum is relatively low, making frequency resources much tenser. In order to improve the frequency utilization of licensed spectrum, cognitive radio technology is proposed.
Hidden terminal and exposed terminal problem is one of the most challenging issues of cognitive radio spectrum sensing and spectrum allocation, which will make a direct impact on the performance of cognitive radio. In this paper, hidden terminal and exposed terminal problem and the mutual interference between primary and secondary systems are analyzed. According to the different requirements of the available frequencies for secondary transmitters and receivers, the spectrum holes have been classified to transmit spectrum holes and receive spectrum holes. Further the differences of transmit/receive spectrum hole sensing are analyzed, as well as the impact to different duplex systems caused by spectrum hole classification.
Additionally, based on the spectrum holes classification, a novel spectrum allocation scheme in cognitive mobile communication system has been investigated. In this scheme the spectrum allocation for inter-cell and intra-cell is analysized detailed, as well as the spectrum hole concession and re-examination, to ensure the spectrum collision beween primary systems and secondary systems will not happen.
Finally, the performance of spectrum allocation scheme proposed in this paper based on spectrum holes classification is validated by the mathematical proof and system level simulation platform. Firstly, the ability to solve hidden terminal and exposed terminal problem of the proposed scheme is verified by mathematical proof. secondly, system level simulation platform is designed for the proposed scheme and the comparison scheme based on traditional transmitter sensing, and the pectrum sensing result, throughput of secondary system, unexpected rate and falsed rate of spectrum sensing are exported. The simulation results show that the proposed spectrum allocation scheme has significant performance gains in the spectrum detection accuracy and throughput of secondary systems.
引文
[1]J. Mitola et al. Cognitive Radios:Making Software Radios more Personal. IEEE Personal Communications, vol.6, no.4, August 1999.
[2]J. Mitola. Cognitive radio:An integrated agent architecture for software defined radio, PhD Dissertation, Royal Inst. Technol.(KTH), Stockholm, Sweden,2000.
[3]S. Haykin, Cognitive Radio:Brain-Empowered Wireless Communications, IEEE JSAC, Feb.2005, Vol.23:201-220.
[4]F. Capar, T. Wsiss, I. Martoyo and F. Jondral. Analysis of Coexistence Strategies for Cellular and Wireless Local Area Networks, VTC Fall, October 2003.
[5]I. F. Akyilidiz, W. Y. Lee, M. C. Vuran, and et al, Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks:A survey, Computer Networks, Sept.15,2006, Vol.50:2127-2159.
[6]P. Kolodzy, Spectrum Policy Task Force:Findings and Recommendations, International Symposium on Advanced Radio Technologies (ISART), March 2003.
[7]G. Staple, K. Werbach. The End of Spectrum Scarcity, IEEE Spectrum. 2004,41(3):48-52.
[8]Federal Communication Commission. Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies, NPRM & Order, ET Docket no.03-108, FCC 03-322,2003.
[9]M. Mc Henry, Report on Spectrum Occupancy Measurements, Shared Spectrum Company, www.sharedspectrum.com/?section=nsf_summary.
[10]Tuttlebee W. Software Defined Radio:Origins, Drivers and International Perspectives New York, Wiley,2002.
[11]Reed J H. Software Radio, Prentice Hall PTR,2002.
[12]J. Mitola. Cognitive Radio for Flexible Mobile Multimedia Communications. Jounal Mobile Networks and Applications,2001,6(5):435-441.
[13]Federal Communication Commission. Notice of Proposed Rule Making and Order, December 2003.
[14]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,2008.5.
[15]C. J. Rieser, Biologically Inspired Cognitive Radio Engine Model Utilizing Distributed Genetic Algorithms for Secure and Robust Wireless Communications and Networking, Virginia Tech, Blacksburg, VA, August 2004.
[16]沈嘉.3GPP长期演进(LTE)技术原理与系统设计,人民邮电出版社,2008.11.
[17]谢希仁.计算机网络教程(第二版),人们邮电出版社,2007.2.
[18]Guo Caili, Zhang Tiankui, Zeng Zhimin, Investigation on Spectrum Sharing Technology Based on Cognitive Radio, CHINACOM,2006, pp:1-5.
[19]T. A. Weiss, F. K. Jondral. Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency, IEEE Radio Communications Magazine, 2004,42(3):8-12.
[20]Ghasemi, E. S. Sousa. Collaborative Spectrum Sensing for opportunistic Access in Fading Environment, in Proc. IEEE DySPAN 2005,2005:131-136.
[21]A. Sahai, N. Hoven, R. Tandra. Some Fundamental Limits in Cognitive Radio, Allerton Conf. on Commun. Control and Computing 2004,2004.
[22]D. Cabric. Implementation Issues in Spectrum Sensing for Cognitive Radios, Signals systems and Computers,2004. Conference Record of the Thirty-Eighth Asilomar Conference,2004.1:772-776.
[23]P. J. Kolodzy. Interference Temperature:a Metric for Dynamic Spectrum Utilization, International Journal of Network Management,2006.16:103-113.
[24]W. A. Gardner. Signal Interception:A Unifying Theoretical Framework for Feature Detection, IEEE Trans. On Communications, August 1988.36(8): 897-906.
[25]A. Fehske, J. D. Gaeddert, J. H. Reed. A New Approach to Signal Classification Using Spectral Correlation and Neural Networks, in Proc. IEEE DySPAN 2005,2005:144-150.
[26]H. Tang. Some Physical Layer Issues of Wide-band Cognitive Radio System, in Proc. IEEE DySPAN 2005,2005:151-159.
[27]G. Ganesan, Y. G. Li. Cooperative Spectrum Sensing in Cognitive Radio Networks, in Proc. IEEE DySPAN 2005,2005:137-143.
[28]J. Zhao, H. Zheng, G. H. Yang. Distributed Coordination in Dynamic Spectrum Allocation networks, in Proc. IEEE DySPAN 2005,2005:259-268.
[29]S. Shankar. Spectrum Agile Radios:Utilization and Sensing Architecture, in Proc. IEEE DySPAN 2005,2005:160-169.
[30]FCC ET Docket No.03-237 Notice of Inquiry and Notice of Proposed Rulemaking,2003.
[31]S. Haykin. Communication Systems,4th ed. New York:Wiley,2001:61.
[32]D. J. Thomson. Spectrum Estimation and Harmonic Analysis, in Proc. IEEE, 1982,70(9):1055-1096.
[33]B. Wild, K. Ramchandran. Detecting Primary Receivers for Cognitive Radio Applications, in Proc. IEEE DySPAN 2005, November 2005, pp:124-130.
[34]H. Zheng, L. Cao. Device-centric Spectrum Management, in Proc. IEEE DySPAN 2005,2005:56-65.
[35]V. Kanadia, A. Sabharwal, E. Knightly. MOAR:a Multichannel Opportunistic Autorate Media Access Protocol for ad hoc Networks, in Porc. IEEE BROADNETS 2004,2004:600-610.
[36]S. Krishnamurthy, M. Thoppian, S. Venkatesan, R. Prakash. Control Channel Based MAC-layer Configuration, Routing and Situation Awareness for Cognitive Radio Networks, in Proc. IEEE MILCOM 2005,2005.
[37]洪波.认知无线电系统中频谱分配综述,电信快报,2009(3)：29-32.
[38]T. Clancy, W. Arbaugh. Measuring Interference Tmeperature, Virginia Tech Wireless Personal Communications Symposium, June 2006.
[39]Wei Wang, Xin Liu. List-Coloring Based Channel Allocation for Open-Spectrum Wireless Networks. The 62nd IEEE Vehicular Technology Conference(VTC),1, 2005. pp:690-694.
[40]N. Clemens, C. Rose. Intelligent Power Allocation Strategies in an Unlicensed Spectrum, IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, November 2005.
[41]Raul Etkin, Abhay Parekh. Spectrum Sharing for Unlicensed Bands, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol.25, No.3, April 2007. pp:517-528.
[42]J. Neel, J. H. Reed, R. P. Gilles. The Role of Game Theory in the Analysis of Software Radio Networks, SDR Forum Technical Conference November,2002.
[43]J. Neel, J. H. Reed, R. P. Gilles. Convergence of Cognitive Radio Networks, Wireless Communications and Networking Conference 2004.
[44]C. Kloeck, H. Jaekel, F. K. Jondral. Dynamic and Local Combined Pricing, Allocation and Billing System with Cognitive Radios, The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks,2005:73-81.
[45]L. Cao, H. Zheng. Distributed Spectrum Allocation via Local Bargaining, In IEEE SECON,2005.9.
[46]T. Clancy, W. Arbaugh. Measuring Interference Temperature, Virginia Tech Wireless Personal Communications Symposium, June 2006.
[47]Q. Zhao. Spectrum Opportunity and Interference Constraint in Opportunistic Spectrum Access, in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, April 2007.
[48]ITU-R. Guidelines for Evaluation of Radio Interface Technologies for IMT-Advanced, IMT. EVAL. Report ITU-R M.2135, October 2008.
[49]H. Huining, H. Yanikomeroglu, D. D. Falconer, et al. Range Extension without Capacity Penalty in Cellular Networks with Digital Fixed Relays, In: GLOBECOM'04. IEEE,2004.3053-3057.
[2]J. Mitola. Cognitive radio:An integrated agent architecture for software defined radio, PhD Dissertation, Royal Inst. Technol.(KTH), Stockholm, Sweden,2000.
[3]S. Haykin, Cognitive Radio:Brain-Empowered Wireless Communications, IEEE JSAC, Feb.2005, Vol.23:201-220.
[4]F. Capar, T. Wsiss, I. Martoyo and F. Jondral. Analysis of Coexistence Strategies for Cellular and Wireless Local Area Networks, VTC Fall, October 2003.
[5]I. F. Akyilidiz, W. Y. Lee, M. C. Vuran, and et al, Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks:A survey, Computer Networks, Sept.15,2006, Vol.50:2127-2159.
[6]P. Kolodzy, Spectrum Policy Task Force:Findings and Recommendations, International Symposium on Advanced Radio Technologies (ISART), March 2003.
[7]G. Staple, K. Werbach. The End of Spectrum Scarcity, IEEE Spectrum. 2004,41(3):48-52.
[8]Federal Communication Commission. Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies, NPRM & Order, ET Docket no.03-108, FCC 03-322,2003.
[9]M. Mc Henry, Report on Spectrum Occupancy Measurements, Shared Spectrum Company, www.sharedspectrum.com/?section=nsf_summary.
[10]Tuttlebee W. Software Defined Radio:Origins, Drivers and International Perspectives New York, Wiley,2002.
[11]Reed J H. Software Radio, Prentice Hall PTR,2002.
[12]J. Mitola. Cognitive Radio for Flexible Mobile Multimedia Communications. Jounal Mobile Networks and Applications,2001,6(5):435-441.
[13]Federal Communication Commission. Notice of Proposed Rule Making and Order, December 2003.
[14]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,2008.5.
[15]C. J. Rieser, Biologically Inspired Cognitive Radio Engine Model Utilizing Distributed Genetic Algorithms for Secure and Robust Wireless Communications and Networking, Virginia Tech, Blacksburg, VA, August 2004.
[16]沈嘉.3GPP长期演进(LTE)技术原理与系统设计,人民邮电出版社,2008.11.
[17]谢希仁.计算机网络教程(第二版),人们邮电出版社,2007.2.
[18]Guo Caili, Zhang Tiankui, Zeng Zhimin, Investigation on Spectrum Sharing Technology Based on Cognitive Radio, CHINACOM,2006, pp:1-5.
[19]T. A. Weiss, F. K. Jondral. Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency, IEEE Radio Communications Magazine, 2004,42(3):8-12.
[20]Ghasemi, E. S. Sousa. Collaborative Spectrum Sensing for opportunistic Access in Fading Environment, in Proc. IEEE DySPAN 2005,2005:131-136.
[21]A. Sahai, N. Hoven, R. Tandra. Some Fundamental Limits in Cognitive Radio, Allerton Conf. on Commun. Control and Computing 2004,2004.
[22]D. Cabric. Implementation Issues in Spectrum Sensing for Cognitive Radios, Signals systems and Computers,2004. Conference Record of the Thirty-Eighth Asilomar Conference,2004.1:772-776.
[23]P. J. Kolodzy. Interference Temperature:a Metric for Dynamic Spectrum Utilization, International Journal of Network Management,2006.16:103-113.
[24]W. A. Gardner. Signal Interception:A Unifying Theoretical Framework for Feature Detection, IEEE Trans. On Communications, August 1988.36(8): 897-906.
[25]A. Fehske, J. D. Gaeddert, J. H. Reed. A New Approach to Signal Classification Using Spectral Correlation and Neural Networks, in Proc. IEEE DySPAN 2005,2005:144-150.
[26]H. Tang. Some Physical Layer Issues of Wide-band Cognitive Radio System, in Proc. IEEE DySPAN 2005,2005:151-159.
[27]G. Ganesan, Y. G. Li. Cooperative Spectrum Sensing in Cognitive Radio Networks, in Proc. IEEE DySPAN 2005,2005:137-143.
[28]J. Zhao, H. Zheng, G. H. Yang. Distributed Coordination in Dynamic Spectrum Allocation networks, in Proc. IEEE DySPAN 2005,2005:259-268.
[29]S. Shankar. Spectrum Agile Radios:Utilization and Sensing Architecture, in Proc. IEEE DySPAN 2005,2005:160-169.
[30]FCC ET Docket No.03-237 Notice of Inquiry and Notice of Proposed Rulemaking,2003.
[31]S. Haykin. Communication Systems,4th ed. New York:Wiley,2001:61.
[32]D. J. Thomson. Spectrum Estimation and Harmonic Analysis, in Proc. IEEE, 1982,70(9):1055-1096.
[33]B. Wild, K. Ramchandran. Detecting Primary Receivers for Cognitive Radio Applications, in Proc. IEEE DySPAN 2005, November 2005, pp:124-130.
[34]H. Zheng, L. Cao. Device-centric Spectrum Management, in Proc. IEEE DySPAN 2005,2005:56-65.
[35]V. Kanadia, A. Sabharwal, E. Knightly. MOAR:a Multichannel Opportunistic Autorate Media Access Protocol for ad hoc Networks, in Porc. IEEE BROADNETS 2004,2004:600-610.
[36]S. Krishnamurthy, M. Thoppian, S. Venkatesan, R. Prakash. Control Channel Based MAC-layer Configuration, Routing and Situation Awareness for Cognitive Radio Networks, in Proc. IEEE MILCOM 2005,2005.
[37]洪波.认知无线电系统中频谱分配综述,电信快报,2009(3)：29-32.
[38]T. Clancy, W. Arbaugh. Measuring Interference Tmeperature, Virginia Tech Wireless Personal Communications Symposium, June 2006.
[39]Wei Wang, Xin Liu. List-Coloring Based Channel Allocation for Open-Spectrum Wireless Networks. The 62nd IEEE Vehicular Technology Conference(VTC),1, 2005. pp:690-694.
[40]N. Clemens, C. Rose. Intelligent Power Allocation Strategies in an Unlicensed Spectrum, IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, November 2005.
[41]Raul Etkin, Abhay Parekh. Spectrum Sharing for Unlicensed Bands, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol.25, No.3, April 2007. pp:517-528.
[42]J. Neel, J. H. Reed, R. P. Gilles. The Role of Game Theory in the Analysis of Software Radio Networks, SDR Forum Technical Conference November,2002.
[43]J. Neel, J. H. Reed, R. P. Gilles. Convergence of Cognitive Radio Networks, Wireless Communications and Networking Conference 2004.
[44]C. Kloeck, H. Jaekel, F. K. Jondral. Dynamic and Local Combined Pricing, Allocation and Billing System with Cognitive Radios, The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks,2005:73-81.
[45]L. Cao, H. Zheng. Distributed Spectrum Allocation via Local Bargaining, In IEEE SECON,2005.9.
[46]T. Clancy, W. Arbaugh. Measuring Interference Temperature, Virginia Tech Wireless Personal Communications Symposium, June 2006.
[47]Q. Zhao. Spectrum Opportunity and Interference Constraint in Opportunistic Spectrum Access, in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, April 2007.
[48]ITU-R. Guidelines for Evaluation of Radio Interface Technologies for IMT-Advanced, IMT. EVAL. Report ITU-R M.2135, October 2008.
[49]H. Huining, H. Yanikomeroglu, D. D. Falconer, et al. Range Extension without Capacity Penalty in Cellular Networks with Digital Fixed Relays, In: GLOBECOM'04. IEEE,2004.3053-3057.