认知无线电网络中的频谱接入算法研究
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摘要
随着无线通信技术的不断发展,作为不可再生资源的无线频谱越来越稀缺。然而,FCC的统计数据表明,虽然某些无线频段已非常拥挤,但仍有大量的无线频谱资源处于空闲状态。受限于无线频谱资源分配所实行的固定分配方式,人们无法灵活的利用这些空闲的频谱资源,这是当前无线电频谱管理中的主要问题。
机会频谱接入是当今无线电技术发展的趋势,是对固定频谱管理规则的突破。认知无线电(Cognitive Radio, CR)是一种机会频谱接入技术,通过对周围无线通信参数的历史值和当前观测值进行检测、分析、学习和推理,并在观测的基础上调整自己的传输参数,从而能在合适的频率以适当的调制方式和发射功率完成用户的无线传输任务。认知无线电技术能够有效地提高频谱利用效率。
本文首先对认知无线电技术的起源、发展以及研究现状进行了简单的介绍,给出认知无线电的定义和功能；其次,阐述了认知无线电的频谱感知和频谱接入技术,分别给出了频谱感知和频谱接入的主要方法和研究模型；另外,根据认知无线电频谱感知和接入问题的特点,建立了部分可观测马尔科夫决策过程(POMDP)模型。在该模型框架下,提出了基于最大似然准则的信道转移概率估计算法。对基于POMDP的最优接入策略算法进行了分析,利用马尔科夫信道的收敛特性,提出了一种基于POMDP的有限迭代计算的频谱接入算法,在保持性能的同时大大减少了计算复杂度；最后,建立了分布式合作的POMDP模型,在该框架下,相邻认知用户相互交换信息和本地策略,通过策略梯度方法渐进的逼近全局最优接入,减少了单个认知用户独立决策时的冲突,提高了频谱利用率。
Along with the wireless communication demand's unceasing development, the wireless frequency spectrum is getting scarcer, which is non-renewable resource. However, the FCC statistical data indicates that although certain wireless frequency bands have been crowded, still have the massive wireless frequency spectrum resources to be at the idling condition. Subject to static allocation regulations now, the people are unable to use those idle frequency spectrum resources flexibly. This is the primary problem under the current spectrum management system.
Opportunity spectrum access is the tendency of radio technological development now, is breakthrough to the fixed spectrum management rule. The cognition radio (CR) is one kind of opportunity spectrum access, through carrying on the examination, analysis, learn, inference to the periphery wireless communication parameter's history and the current observation, adjusts its own transmission parameter based on the observation foundation, can thus complete the user's transmission duty in the appropriate frequency by the suitable modulation system and the emissive power. The cognition radio technology can raise the spectrum use efficiency effectively.
Firstly, this paper gives the origin of cognitive radio technology, development, research status, and the definition and function of cognitive radio. The second chapter describes in detail the spectrum sensing and spectrum access technology and gives the major methods and models individually. In the third chapters according to specialty of cognitive radio, partially observable Markov decision-making process (POMDP) model is developed. Under the POMDP model, based on ML criterion estimation of channel transformation probability is designed; by analyzing the optimal access policy of POMDP, based on limited iterative method, the modified algorithm is proposed which keeps the performance and reduce the computable complexity. The fourth chapter includes a distributed POMDP model of cooperation. By interaction in the neighboring user information based on policy gradient methods to approach gradually global optimal access, the method can reduce the collision between cognitive users and improve the efficiency of spectrum.
机会频谱接入是当今无线电技术发展的趋势,是对固定频谱管理规则的突破。认知无线电(Cognitive Radio, CR)是一种机会频谱接入技术,通过对周围无线通信参数的历史值和当前观测值进行检测、分析、学习和推理,并在观测的基础上调整自己的传输参数,从而能在合适的频率以适当的调制方式和发射功率完成用户的无线传输任务。认知无线电技术能够有效地提高频谱利用效率。
本文首先对认知无线电技术的起源、发展以及研究现状进行了简单的介绍,给出认知无线电的定义和功能；其次,阐述了认知无线电的频谱感知和频谱接入技术,分别给出了频谱感知和频谱接入的主要方法和研究模型；另外,根据认知无线电频谱感知和接入问题的特点,建立了部分可观测马尔科夫决策过程(POMDP)模型。在该模型框架下,提出了基于最大似然准则的信道转移概率估计算法。对基于POMDP的最优接入策略算法进行了分析,利用马尔科夫信道的收敛特性,提出了一种基于POMDP的有限迭代计算的频谱接入算法,在保持性能的同时大大减少了计算复杂度；最后,建立了分布式合作的POMDP模型,在该框架下,相邻认知用户相互交换信息和本地策略,通过策略梯度方法渐进的逼近全局最优接入,减少了单个认知用户独立决策时的冲突,提高了频谱利用率。
Along with the wireless communication demand's unceasing development, the wireless frequency spectrum is getting scarcer, which is non-renewable resource. However, the FCC statistical data indicates that although certain wireless frequency bands have been crowded, still have the massive wireless frequency spectrum resources to be at the idling condition. Subject to static allocation regulations now, the people are unable to use those idle frequency spectrum resources flexibly. This is the primary problem under the current spectrum management system.
Opportunity spectrum access is the tendency of radio technological development now, is breakthrough to the fixed spectrum management rule. The cognition radio (CR) is one kind of opportunity spectrum access, through carrying on the examination, analysis, learn, inference to the periphery wireless communication parameter's history and the current observation, adjusts its own transmission parameter based on the observation foundation, can thus complete the user's transmission duty in the appropriate frequency by the suitable modulation system and the emissive power. The cognition radio technology can raise the spectrum use efficiency effectively.
Firstly, this paper gives the origin of cognitive radio technology, development, research status, and the definition and function of cognitive radio. The second chapter describes in detail the spectrum sensing and spectrum access technology and gives the major methods and models individually. In the third chapters according to specialty of cognitive radio, partially observable Markov decision-making process (POMDP) model is developed. Under the POMDP model, based on ML criterion estimation of channel transformation probability is designed; by analyzing the optimal access policy of POMDP, based on limited iterative method, the modified algorithm is proposed which keeps the performance and reduce the computable complexity. The fourth chapter includes a distributed POMDP model of cooperation. By interaction in the neighboring user information based on policy gradient methods to approach gradually global optimal access, the method can reduce the collision between cognitive users and improve the efficiency of spectrum.
引文
[1]J. Mitola, Cognitive radio for flexible mobile multimedia communications. [J]. in Proc. of IEEE International Workshop on Mobile Multimedia Communications (MoMuC), pp.3-10, Nov.1999.
[2]McHenry M. Spectrum Occupancy Measurements. [EB/OL]. http://www.sharedspectrum.com/?section=nsf summary.2005.8
[3]FCC, Spectrum policy task force report, FCC 02-155. [R].Nov.2002.
[4]J. Mitola Ill and GQ. Maguire. Cognitive radio:Making Software Radios More Personal. [J]. IEEE Personal Communications,1999,6(4):13-18.
[5]J. Mitola III. Cognitive Radio:An Integrated Agent Architecture for Software Defined Radio. [D]. Ph.D. Dissertation, Royal Institute of Technology,2000.
[6]W. Tuttlebee. Software Defined Radio:Origins, Drivers and International Perspectives. New York:Wiley,2002.
[7]Simon Haykin. Cognitive Radio:Brain. Empowered Wireless Communications. [J]. IEEE Journal on Selected Areas in Communications,2005,23(2):201-220.
[8]FCC. Notice of Proposed Rule Making and Order. [R].ET Docket No.03.322, December 2003.
[9]IEEE 802LAN/MAN Standards Committee 802.22. WG [EB/OL]. Available:http: //grouper.ieee.org/
[10]IEEE 802.16's License. Exempt (LE) Task Group. [EB/OL]. Available: http://www.ieee802.org/16/le/.
[11]IEEE P1900 Working Group[EB/OL].Available:http //grouper.ieee.org/groups/sce41/index.html.
[12]T. A. Weiss and EK, Jondral. Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency.[J].IEEE Radio Communications,2004,42(3):8-14
[13]Q. Zhao and A. Swami, A decision-theoretic framework for opportunistic spectrum access. [J].IEEE Wireless Comm. Magazine:Special Issue on Cognitive Wireless Networks, vol.14, no.4, pp.14-20, Aug.2007.
[14]Q. Zhao, L. Tong, A. Swami, and Y. Chen, Decentralized cognitive mac for opportunistic spectrum access in ad hoc networks:A pomdp framework. [J]. IEEE JSAC:Special Issue on Adaptive, Spectrum Agile and Cognitive Wireless Networks, vol.25, no.3, pp.589-600, Apr. 2007.
[15]Y. Chen, Q. Zhao, and A. Swami, Distributed cognitive mac for energy constrained opportunistic spectrum access. [C]. Proc. of IEEE Military Communication Conference (MILCOM), Las Vegas, USA, Oct.2006.
[16]A. T. Hoang and Y.C. Liang. Adaptive scheduling of spectrum sensing periods in cognitive radio networks. [C]. Proc. of 50th IEEE Global Telecommunications Conference, Washington DC, USA, Nov.2007.
[17]Y. C. Liang, Y. H. Zeng, E. Peh, and A. T. Hoang. Sensing-throughput tradeoff for cognitive radio networks. [J]. Proc. of IEEE ICC'07, Glasgow, Jun.2007.
[18]A. Ghasemi and E. Sousa. Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks. [C]. Proc. of 4th IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, USA, Jan.2007.
[19]Y. Pei, A. T. Hoang, and Y.-C. Liang. Sensing-throughput tradeoff in cognitive radio networks:How frequently should spectrum sensing be carried out? [J].Proc. of 18th IEEE PIMRC, Athens, Greece, Sep.2007.
[20]M. Mushkin and I. Bar-David. Capacity and coding for the gilbert-elliott channels. [J].IEEE Trans on Inform. Theory, vol.35, no.6, pp.1277-1290, Nov.1989.
[21]Ganesan, GYeLi. Cooperative spectrum sensing in cognitive radio networks. [C].The First IEEE International Symposium on New Frontiers in Dynamic Spectrum ACCCSS Networks (DySPAN), PP.137—143. (2005).
[22]M. M. Buddhikot, K. Ryan. Spectrum management i171 coordinated dynamic spectrum access based cellular networks. [C]. IEEE DySPAN 2005. Nov.2005. PP.299-397.
[23]A. Ghasemi, E. S. Sousa. Collaborative spectrum sensing for opportunistic access in fading environment. [J]. Proc. IEEE DySPAN 2005,12:131-136.
[24]F. Digham, M. Alouini, M. Simon. On the energy detection of unknown signals over fading channels. [J]. Proc. IEEE ICC 2003,2003,5(4):3575-3579.
[25]Eric Rasmusen. Game and Information:An Introduction to Game Theory. [M]. Chapter 9-10, Cambridge, Blackwell Publisher,1994.
[26]张维迎.博奕论与信息经济学.[M].上海：上海人民出版社,1996.
[27]J. Jia Q Z. A Non-Cooperative Power Control Game for Secondary Spectrum Sharing. [C]. Proceedings of IEEE International Conference on Communications, IEEE ICC, Jun.,2007.
[28]L J. Huang, R. A. Berry, M. L. Honig. Spectrum sharing with distributed interference compensation. [C]. Proc. IEEE DySPAN 2005, November 2005,pp:88—93.
[29]Y. Chen, Q. Zhao, and A. Swami, Joint Design and Separation Principle for Opportunistic Spectrum Access. [C]. IEEE Asilomar Conference on Signals, Systems, and Computers, 2006.
[30]Q. Zhao, L. Tong, A. Swami, and Y. Chen. Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks:A POMDP framework. [J]. Journal of Selected Areas in Communications, vol 25, Issue 3, April 2007 Page(s):589-600
[31]Y. Chen, Q. Zhao, and A. Swami. Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Error. [J]. IEEE Transactions on Information Theory, Feb.,2007.
[32]Fuqaha. A. A, Khan. B, Rayes. A. Opportunistic Channel Selection Strategy for Better QoS in Cooperative Networks with Cognitive Radio Capabilities. [J]. IEEE Journal on Selected Areas in Communications, Vol.26, No.1, pp:156-167, Jan.2008
[33]Kim. H and Shin. K. G, Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. [J]. IEEE Transactions on Mobile Computing, vol.7, no.5, pp:533-545, May 2008.
[34]Q. Zhao, L. Tong, and A. Swami. Decentralized cognitive MAC for dynamic spectrum access. [C]. Proc. of IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Nov.2005.
[35]David A. Levin, Yuval Peres, Elizabeth L. Wilmer. Markov Chains and Mixing Times:47-60.
[36]R. Smallwood and E. Sondik. The optimal control of partially observable Markov processes over a finite horizon. [M]. Operations Research, pp.1071-1088,1971..
[37]Bonet, B. An e-optimal grid-based algorithm for partially observable Markov decision processes. [C].19th International Conference on Machine Learning, Sydney, Australia (June 2002)
[38]M.Zorzi and R.Rao, "Error control and energy consumption in communications for nomadic computing. [J]. IEEE Transactions on Computers, vol.46, pp.279-289, March 1997.
[39]D. Pynadath and M. Tambe. The communicative multi-agent team decision problem: Analyzing teamwork theories and models. [J]. Journal of Artificial Intelligence Research, vol.16, pp.389-423,2002.
[40]D. Bernstein, R. Givan, N. Immerman, and S. Zilberstein. The Complexity of Decentralized Control of Markov Decision Process. [M]. Mathematics of Operations Research, vol.27, no. 4, pp.819-840,2002.
[41]D. Aberdeen. Policy-Gradient Algorithms for Partially Observable Markov Decision Process. [D]. Australian National University, Australia, Apr.2003.
[42]T. Jaakkola, S. Singh, and M. Jordan. Reinforcement Learning Algorithm for Partially Observable Markov Decision Process. [J]. Neural Information Processing Systems, vol.7, 1995, pp.345-352.
[43]Leonid Peshkin. Policy Search for Reinforcement Learning. [D]. PhD thesis, Brown University,2002. Draft.
[44]D. Aberdeen and J. Baxter. Scaling Internal-State Policy-Gradient Methods for POMDPs. [C]. 19th International Conf. on Machine Learning, Sydney, Australia, July 2002, pp.1-12.
[2]McHenry M. Spectrum Occupancy Measurements. [EB/OL]. http://www.sharedspectrum.com/?section=nsf summary.2005.8
[3]FCC, Spectrum policy task force report, FCC 02-155. [R].Nov.2002.
[4]J. Mitola Ill and GQ. Maguire. Cognitive radio:Making Software Radios More Personal. [J]. IEEE Personal Communications,1999,6(4):13-18.
[5]J. Mitola III. Cognitive Radio:An Integrated Agent Architecture for Software Defined Radio. [D]. Ph.D. Dissertation, Royal Institute of Technology,2000.
[6]W. Tuttlebee. Software Defined Radio:Origins, Drivers and International Perspectives. New York:Wiley,2002.
[7]Simon Haykin. Cognitive Radio:Brain. Empowered Wireless Communications. [J]. IEEE Journal on Selected Areas in Communications,2005,23(2):201-220.
[8]FCC. Notice of Proposed Rule Making and Order. [R].ET Docket No.03.322, December 2003.
[9]IEEE 802LAN/MAN Standards Committee 802.22. WG [EB/OL]. Available:http: //grouper.ieee.org/
[10]IEEE 802.16's License. Exempt (LE) Task Group. [EB/OL]. Available: http://www.ieee802.org/16/le/.
[11]IEEE P1900 Working Group[EB/OL].Available:http //grouper.ieee.org/groups/sce41/index.html.
[12]T. A. Weiss and EK, Jondral. Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency.[J].IEEE Radio Communications,2004,42(3):8-14
[13]Q. Zhao and A. Swami, A decision-theoretic framework for opportunistic spectrum access. [J].IEEE Wireless Comm. Magazine:Special Issue on Cognitive Wireless Networks, vol.14, no.4, pp.14-20, Aug.2007.
[14]Q. Zhao, L. Tong, A. Swami, and Y. Chen, Decentralized cognitive mac for opportunistic spectrum access in ad hoc networks:A pomdp framework. [J]. IEEE JSAC:Special Issue on Adaptive, Spectrum Agile and Cognitive Wireless Networks, vol.25, no.3, pp.589-600, Apr. 2007.
[15]Y. Chen, Q. Zhao, and A. Swami, Distributed cognitive mac for energy constrained opportunistic spectrum access. [C]. Proc. of IEEE Military Communication Conference (MILCOM), Las Vegas, USA, Oct.2006.
[16]A. T. Hoang and Y.C. Liang. Adaptive scheduling of spectrum sensing periods in cognitive radio networks. [C]. Proc. of 50th IEEE Global Telecommunications Conference, Washington DC, USA, Nov.2007.
[17]Y. C. Liang, Y. H. Zeng, E. Peh, and A. T. Hoang. Sensing-throughput tradeoff for cognitive radio networks. [J]. Proc. of IEEE ICC'07, Glasgow, Jun.2007.
[18]A. Ghasemi and E. Sousa. Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks. [C]. Proc. of 4th IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, USA, Jan.2007.
[19]Y. Pei, A. T. Hoang, and Y.-C. Liang. Sensing-throughput tradeoff in cognitive radio networks:How frequently should spectrum sensing be carried out? [J].Proc. of 18th IEEE PIMRC, Athens, Greece, Sep.2007.
[20]M. Mushkin and I. Bar-David. Capacity and coding for the gilbert-elliott channels. [J].IEEE Trans on Inform. Theory, vol.35, no.6, pp.1277-1290, Nov.1989.
[21]Ganesan, GYeLi. Cooperative spectrum sensing in cognitive radio networks. [C].The First IEEE International Symposium on New Frontiers in Dynamic Spectrum ACCCSS Networks (DySPAN), PP.137—143. (2005).
[22]M. M. Buddhikot, K. Ryan. Spectrum management i171 coordinated dynamic spectrum access based cellular networks. [C]. IEEE DySPAN 2005. Nov.2005. PP.299-397.
[23]A. Ghasemi, E. S. Sousa. Collaborative spectrum sensing for opportunistic access in fading environment. [J]. Proc. IEEE DySPAN 2005,12:131-136.
[24]F. Digham, M. Alouini, M. Simon. On the energy detection of unknown signals over fading channels. [J]. Proc. IEEE ICC 2003,2003,5(4):3575-3579.
[25]Eric Rasmusen. Game and Information:An Introduction to Game Theory. [M]. Chapter 9-10, Cambridge, Blackwell Publisher,1994.
[26]张维迎.博奕论与信息经济学.[M].上海：上海人民出版社,1996.
[27]J. Jia Q Z. A Non-Cooperative Power Control Game for Secondary Spectrum Sharing. [C]. Proceedings of IEEE International Conference on Communications, IEEE ICC, Jun.,2007.
[28]L J. Huang, R. A. Berry, M. L. Honig. Spectrum sharing with distributed interference compensation. [C]. Proc. IEEE DySPAN 2005, November 2005,pp:88—93.
[29]Y. Chen, Q. Zhao, and A. Swami, Joint Design and Separation Principle for Opportunistic Spectrum Access. [C]. IEEE Asilomar Conference on Signals, Systems, and Computers, 2006.
[30]Q. Zhao, L. Tong, A. Swami, and Y. Chen. Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks:A POMDP framework. [J]. Journal of Selected Areas in Communications, vol 25, Issue 3, April 2007 Page(s):589-600
[31]Y. Chen, Q. Zhao, and A. Swami. Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Error. [J]. IEEE Transactions on Information Theory, Feb.,2007.
[32]Fuqaha. A. A, Khan. B, Rayes. A. Opportunistic Channel Selection Strategy for Better QoS in Cooperative Networks with Cognitive Radio Capabilities. [J]. IEEE Journal on Selected Areas in Communications, Vol.26, No.1, pp:156-167, Jan.2008
[33]Kim. H and Shin. K. G, Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. [J]. IEEE Transactions on Mobile Computing, vol.7, no.5, pp:533-545, May 2008.
[34]Q. Zhao, L. Tong, and A. Swami. Decentralized cognitive MAC for dynamic spectrum access. [C]. Proc. of IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Nov.2005.
[35]David A. Levin, Yuval Peres, Elizabeth L. Wilmer. Markov Chains and Mixing Times:47-60.
[36]R. Smallwood and E. Sondik. The optimal control of partially observable Markov processes over a finite horizon. [M]. Operations Research, pp.1071-1088,1971..
[37]Bonet, B. An e-optimal grid-based algorithm for partially observable Markov decision processes. [C].19th International Conference on Machine Learning, Sydney, Australia (June 2002)
[38]M.Zorzi and R.Rao, "Error control and energy consumption in communications for nomadic computing. [J]. IEEE Transactions on Computers, vol.46, pp.279-289, March 1997.
[39]D. Pynadath and M. Tambe. The communicative multi-agent team decision problem: Analyzing teamwork theories and models. [J]. Journal of Artificial Intelligence Research, vol.16, pp.389-423,2002.
[40]D. Bernstein, R. Givan, N. Immerman, and S. Zilberstein. The Complexity of Decentralized Control of Markov Decision Process. [M]. Mathematics of Operations Research, vol.27, no. 4, pp.819-840,2002.
[41]D. Aberdeen. Policy-Gradient Algorithms for Partially Observable Markov Decision Process. [D]. Australian National University, Australia, Apr.2003.
[42]T. Jaakkola, S. Singh, and M. Jordan. Reinforcement Learning Algorithm for Partially Observable Markov Decision Process. [J]. Neural Information Processing Systems, vol.7, 1995, pp.345-352.
[43]Leonid Peshkin. Policy Search for Reinforcement Learning. [D]. PhD thesis, Brown University,2002. Draft.
[44]D. Aberdeen and J. Baxter. Scaling Internal-State Policy-Gradient Methods for POMDPs. [C]. 19th International Conf. on Machine Learning, Sydney, Australia, July 2002, pp.1-12.
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