WRAN中无线电环境地图的生成技术研究
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摘要
认知无线电依靠人工智能技术感知无线通信环境,根据学习和决策算法自适应调整自身工作参数,从而动态检测和有效利用空闲频谱,是缓解频谱需求与资源有限之间矛盾的一种有效途径。IEEE802.22是一种无线区域网(WRAN)的技术规范,允许未授权用户在不影响授权用户(电视广播)业务的前提下,动态接入电视广播频段中的空闲信道。无线电环境地图(REM)是一种综合各种无线电环境信息的数据库,将REM引入无线区域网可以增强认知无线电的环境感知能力,通过从过去经验和当前结果的学习和推理,辅助认知无线电识别无线电场景的可靠性和准确性。论文重点研究无线电环境地图的生成技术,主要内容有:
1)深入分析认知无线电涉及到的环境参数的构成、各参数的影响特性,将无线电环境地图引入无线区域网的作用和意义。
2)针对认知无线电中位置感知和环境感知方法开展研究。在位置感知方法研究中,针对传统的基于无线电波传播的定位方法,设计了WRAN中CPE的定位模型和方法,并设计了一种定位精度自适应算法;在环境感知方法研究中,阐述了地理环境信息包括的内容和获得这些信息的方法。
3)针对认知无线电中频谱感知方法开展研究,阐述了研究无线电环境地图中频谱感知技术的背景;对感知技术与无线环境地图的相互关系进行分析并对现有频谱感知技术进行分析和总结;并提出一种WRAN-REM信息辅助的协作频谱感知算法。
4)设计了一种生成无线电环境地图的数据格式及数据库结构,阐述了无线电环境地图的生成步骤,建立了全局无线电环境地图的E-R模型和无线电环境信息数字化表,为认知无线电调用无线电环境地图信息提供支撑。
Cognitive radio relies on the support of artificial intelligence to sense the wireless communication environment. Based on certain learning and decision-making algorithms, the working parameters of system are changed adaptively in real-time, and then the idle spectrum is tested dynamically and can be used effectively. Cognitive radio technology is considered as an effective way to alleviate the contradiction between the current increasing spectrum demand and the limited spectrum resources.IEEE802.22 is a Wireless Regional Area Network (WRAN) specification to support the unlicensed operation in TV bands (54-862 MHz), which is to coexist with incumbent users and provide wideband internet access to rural and suburban areas. Radio Environment Map (REM) is a comprehensive database which contains radio environment information. Knowledge derived from querying a local or global REM can contribute to the radio’s environment awareness. Reasoning and learning help the CR to identify the specific radio scenario. The paper focus on the generation technique of REM and the main contents are:
(1)It analyses the composition of environmental parameters and the influence of various parameters of cognitive radio and the role and significance of REM in WRAN.
(2) Location and environment awareness which are two prominent features of cognitive radios are researched. Based on the traditional position method of radio wave propagation, WRAN CPE location model and method are designed. And a positioning accuracy adaptive algorithm is mentioned; Entities of geographical environmental information are mentioned and we have introduced some methods of how to acquire this information, too.
(3) Spectrum sensing methods for cognitive radio are researched. The technical background of researching spectrum sensing methods in REM is introduced. Then, we have analyzed the relationship between REM and spectrum sensing methods and compared strengths and weaknesses of the existing various spectrum sensing methods. At last, we present a WRAN-REM information assisted Collaborative spectrum sensing algorithm.
(4) Data format and database structure of REM are designed. The generation steps of REM are described. The E-R model of a global REM and the retrieving REM information are found for supporting calling REM information in cognitive radios.
1)深入分析认知无线电涉及到的环境参数的构成、各参数的影响特性,将无线电环境地图引入无线区域网的作用和意义。
2)针对认知无线电中位置感知和环境感知方法开展研究。在位置感知方法研究中,针对传统的基于无线电波传播的定位方法,设计了WRAN中CPE的定位模型和方法,并设计了一种定位精度自适应算法;在环境感知方法研究中,阐述了地理环境信息包括的内容和获得这些信息的方法。
3)针对认知无线电中频谱感知方法开展研究,阐述了研究无线电环境地图中频谱感知技术的背景;对感知技术与无线环境地图的相互关系进行分析并对现有频谱感知技术进行分析和总结;并提出一种WRAN-REM信息辅助的协作频谱感知算法。
4)设计了一种生成无线电环境地图的数据格式及数据库结构,阐述了无线电环境地图的生成步骤,建立了全局无线电环境地图的E-R模型和无线电环境信息数字化表,为认知无线电调用无线电环境地图信息提供支撑。
Cognitive radio relies on the support of artificial intelligence to sense the wireless communication environment. Based on certain learning and decision-making algorithms, the working parameters of system are changed adaptively in real-time, and then the idle spectrum is tested dynamically and can be used effectively. Cognitive radio technology is considered as an effective way to alleviate the contradiction between the current increasing spectrum demand and the limited spectrum resources.IEEE802.22 is a Wireless Regional Area Network (WRAN) specification to support the unlicensed operation in TV bands (54-862 MHz), which is to coexist with incumbent users and provide wideband internet access to rural and suburban areas. Radio Environment Map (REM) is a comprehensive database which contains radio environment information. Knowledge derived from querying a local or global REM can contribute to the radio’s environment awareness. Reasoning and learning help the CR to identify the specific radio scenario. The paper focus on the generation technique of REM and the main contents are:
(1)It analyses the composition of environmental parameters and the influence of various parameters of cognitive radio and the role and significance of REM in WRAN.
(2) Location and environment awareness which are two prominent features of cognitive radios are researched. Based on the traditional position method of radio wave propagation, WRAN CPE location model and method are designed. And a positioning accuracy adaptive algorithm is mentioned; Entities of geographical environmental information are mentioned and we have introduced some methods of how to acquire this information, too.
(3) Spectrum sensing methods for cognitive radio are researched. The technical background of researching spectrum sensing methods in REM is introduced. Then, we have analyzed the relationship between REM and spectrum sensing methods and compared strengths and weaknesses of the existing various spectrum sensing methods. At last, we present a WRAN-REM information assisted Collaborative spectrum sensing algorithm.
(4) Data format and database structure of REM are designed. The generation steps of REM are described. The E-R model of a global REM and the retrieving REM information are found for supporting calling REM information in cognitive radios.
引文
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[3] Y.P.Zhao, B.Le , J.Reed.“Network Support: The Radio Environment map[N]”, Cognitive Radio Technology– Bruce Fette (Ed.1), Ch 11, Elsevier 2006.
[4] Y.P.Zhao,David Raymond, Claudio da Silva,et al.Performance Evaluation of Radio Environment Map-Enabled Cognitive Spectrum-Sharing Networks[J].IEEE 2009.
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[7] Y.P.Zhao,J.H.Reed,S.Mao,et al.Overhead analysis for REM-enabled CR Networks[J].Proceedings of the first IEEE Workshop on Networking Technologies for Software Defined Radio Networks, Sept, 2006.
[8] Y.P.Zhao,J.Gaeddert, K. K. Bae,et al. Radio Environment Map-enabled Situation-aware Cognitive Radio Learning Algorithms[J].Software Defined Radio (SDR) Technical Conference,Nov.13-17,2006,Orlando,FL.
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[10] T.W.Wu,Y.E.Lin,H.Y.Hsieh.Modeling and Comparison of Primary User Detection Techniques in Cognitive Radio Networks[C] . Proceedings of the IEEE Global Telecommunications Conference, New Orleans, LA,November 2008: 1-5.
[11] Z.Quan, S.Cui,A.H.Sayed, Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio[C],Proceedings of the IEEE Military Communications Conference,San Diego,CA, October 2007: 1-6.
[12] X.Chen, W.Xu, Z.He et al. Spectral Correlation Based Multi-Antenna Spectrum Sensing Technique[C] , Proceedings of the IEEE wireless Communications and NetworkingConference, Las Vegas, NV, April 2008, pp.735-740.
[13] S.Haykin.Cognitive Radar Networks[C].Fourth IEEE Workshop on Sensor Array and Multichannel Processing, Boston, MA, July 2006, pp.1-24.
[14] H.Celebi,H.Arslan.Enabling Location and Environment Awareness in Cognitive Radios[J]. Elsevier Computer Communications ,31,6,April 2008: 1114-1125.
[15] J.Caffery.Wireless Location in CDMA Cellular Radio Systems[M], Kluwer Academic Publishers, 2001.
[16] P.C.Chen,D.Goodman.Location Estimation in CDMA Systems: Enhanced Measurement on Pilot Channels[J],WINLAB TR-170,September 1998.
[17] M.P.Wylie,J.Holtzman. The Non-Line-of-Sight Problems in Mobile Location Estimation[J], WINLAB TR-121,June 1996.
[18] W.Krenik,A.Batra.Cognitive Radio Techniques for Wide Area Networks[C].42 Design Automation Conference,Anaheim,CA,June13-17,2005.
[19] B.Krenik,C.Ranasik.The Potential for Unlicensed Wide Area Networks[S].Wireless Advanced Architectures Group,Texas Instruments White Paper,October 2004.
[20] H . Arslan . Cognitive Radio , Software Defined Radio and Adaptive Wireless Systems[M]. Springer, 2007.
[21] S.Geziei.A survey on wireless Position estimation[J].Wireless Personal Communications Towards Global&Seamless Personal Navigation,vol.44(3): 263-282,Feb.2008.
[22] S.M.Kay.Fundamentals of statistical Signal Processing: Estimation Theory Englewood Cliffs[J],NJ:Prentiee-Hall,1993.
[23] W.H.Foy.Position1ocation solutions by Taylor series estimation[J]. IEEE Transactions on Aerospace and Electronic Systems vol.AES-12(2): 187194,Mar.1976.
[24] J.Yan,G.Bellusci,C.Tiberius,et al.Analyzing non-linearity effect for indoor Positioning using an acoustic ultra-wideband system[C].Proc.5th Workshop on Positioning,Navigation and Communication(WPNC’08): 95-101, Mar.2008.
[25] Y.Huang,J.Benesty,G.Elko,et al.Real-time Passive source localization: a practical linear-correction least-squares approach[C]. IEEE Transactions on Speech and Audio Processing,vol.9(8), Nov.2001.
[26] Y.T.Chan,H.Y.C.Hang,P.C.Ching.Exact and approximate maximum likelihood localization algorithms[C].IEEE Transactions on Vehicular Technology,vol.55(1): 10-16.Jan.2006.
[27] J.J.Caffery,G.L.Stuber.Subscriber location in CDMA cellular networks[C].IEEE Transactions on Vehicular Technology,vol.47(2): 406-416,May 1998.
[28] A.Beck,P.Stoica,J.Li.Exact and approximate solutions of source localization problems[C].IEEE Transactions on Signal Processing,vol.56(5),pp,1770-1778,May 2008.
[29] K.W.Cheung,H.C.So,W.K.Ma,et al.A constrained least squares approach to mobile positioning[J].EURASIP Journal on Applied Signal Processing,vol,2006: 1-23,2006.
[30]熊瑾煜,王巍,朱中梁.基于泰勒级数展开的蜂窝TDOA定位算法[J].通信学报,2004,25(4):144-150.
[31] H.C.Schau,A.Z.Robinson. Passive source location employing intersecting spherical surfaces from time-of-arrival difference[J].IEEE Trans on Acoustics,Speech,and Signal Processing,1987,ASSP-35(1):1223-1225.
[32] H.Celebi.Location awareness in cognitive radio networks[D].Ph.D.dissertation,University of South Florida, FL,Aug.2008.
[33] K.L.Bell,Y.Steinberg,et al.Extended Ziv-Zakai Lower Bound for Vector Parameter Estimation[J]. IEEE Trans.on Information Theory,43(2):624-637,1997.
[34] Z.Sahinoglu,S.Gezici.Ranging in the IEEE 802.15.4a Standard[C].IEEE Wireless and Microwave Technology Conference,Clearwater,FL,USA,1-5,2006.
[35] S.H.Celebi , H.Arslan.Enabling Location and Environment Awareness in Cognitive Radios[J].Elsevier Computer Communications,31,6,Apr.2008, pp.1114-1125.
[36] P.Luley,A.Almer,C.Seifert,et al.A multi-sensor system for mobile services with vision enhanced object and location awareness[J]. IEEE International Mobile Commerce and Services,Munich,Germany,July,2005: 52–59.
[37] S.Haykin.Cognitive Radar: A Way of the Future[J]. IEEE Signal Processing Magazine,23,1,January 2006: 30-40.
[38] S.Yarkan,H.Arslan.Exploiting location awareness toward improved wireless system design in cognitive radio[J].IEEE Communication .Mag.46 (1) (2008) 128–136.
[39] A.V.D,awate,G.B.Giannakis.Statistical tests for presence ofcyclostationarity[J].IEEE Trans.Signal Process.vol.42: 2355–2369,Sep.1994.
[40] W.A.Gardner.Exploitation of spectralredundancy in cyclostationary signals[J].IEEE Signal Process.Mag.vol.8: 14–36,Apr..1991.
[41] Y . G . Li , Z . Ding . ARMA system identification based on second-ordercyclostationarity[J].IEEE Trans.Signal Process.vol.42: 3483–3494,Dec.1994.
[42] D.Cabric,S.M.Mishra , R.W.Brodersen.Implementation issues in spectrum sensingfor cognitive radios[C].Proc.Asilomar Conf.Signals,Syst.Comput.Nov.2004,vol.1: 772–776.
[43] C.R.C.M.da Silva,B.Choi et al.Distributed spectrum sensing for cognitiveradiosystems[C].Proc.ITA Workshop 2007.
[44] FCC.Spectrum Policy Task Force report[R].ET Docket 02-155,Nov.2002.
[45] Y . Zeng , Y . C . Liang . Maximum-minimum eigenvalue detection for cognitive radio[C].Proc.IEEE PIMRC,Athens,Greece,Sep.2007.
[46] Y.Zeng,Y.C.Liang.Covariance based signal detections for cognitive radio[C]. Proc.IEEE DYSPAN,Dublin,Ireland,Apr.2007: 202–207.
[47] Y.Zeng,C.L.Koh, Y.C.Liang.Maximum eigenvalue detection: Theoryand application[C].Proc.IEEE ICC,Beijing,China,May 2008: 4160–4164.
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