基于认知MIMO系统的资源分配算法研究
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摘要
随着无线通信研究的持续深入,越来越多的先进技术都对频谱资源有着更高的需求,由此造成的非授权频谱资源不足和授权频谱资源利用率低下之间的矛盾已成为无线通信发展过程中的桎梏。在此背景下,认知MIMO技术作为解决这一矛盾的关键技术已经得到了极大的重视和广泛的研究。它利用频谱重用的概念,可以同时提高频谱利用的效率和灵活性,并能够有效平衡空间分集,降低干扰,提高系统总速率。在认知MIMO网络中满足频谱共享(SS)机制的模型被称为“干扰温度模型”,本论文就是基于这一模型展开的,研究重点是选取合适的干扰约束条件,将从用户(CR)传输对主用户(PR)通信造成的影响控制在一定范围内,确保主从用户能够在同一个授权频段上同时进行传输,并获得合适的资源分配方案来最优化系统性能。
本论文所进行的具体工作和创新可以归纳为如下两个方面：
第一,研究了干扰功率约束下的认知¨MIMO系统资源分配,并首次提出了一种新的资源分配方法。即先利用瑞利衰落信道统计特性推导PR均值干扰功率约束条件,再联合CR的发送功率约束等要素进行认知MIMO系统的资源分配设计。这样做不但可以消除传统方案中感知时间不确定度的影响,易于工程实践的应用,而且通过仿真验证可以看出,它还能够同时提高PR系统以及CR系统的遍历容量,使其获得更好的特性。
第二,考虑到PR通信过程中可能出现的复杂情况,本论文不仅分析了其他对PR通信进行保护的方案,还给出了一种新的设计方案。即在利用信道估计确定认知MIMO系统发送波束成形机制的基础上,推导出PR的容量损耗约束条件,并以最大化CR系统遍历容量为目标获得最优的认知MIMO系统资源分配方案,实现了环境感知和数据传输的折中。MATLAB仿真结果证明实际中当PR收发端之间为深衰落信道或者PR发送功率极低的时候,采用这种方法不仅不会影响PR的性能,还能够使相应CR系统的遍历容量得到提升。
With the continuous development of wireless communication, more and more advanced technologies have a higher demand of the spectrum resources which causes the contradiction between low utilization rate of authorized frequency spectrum and shortage of unauthorized spectrum resources. Therefore, it has hindered the speed of development greatly. As the key to solve this problem, CR MIMO which intends to reuse spectrum resources have got a great attention and widely studied. It not only improves the spectrum utilization but also increases the capacity. Meanwhile, it can remove the interference to the authorized users. In this thesis we consider SS (spectrum sharing) mode and find the suitable IT (interference temperature) constraints in CR MIMO systems.
The main contributions of this thesis can be summarized as two points:
First, we study the resource allocation strategy in CR MIMO systems under the interference power constraints. Considering the statistical properties of Rayleigh fading channels, this thesis first proposes a new method to gain the average interference power (AIP) constraints for PR in CR MIMO systems. Then we design the optimal transmit strategy for a CR link to maximize the ergodic capacity subject to the AIP constraints and CR transmit power constraints. Simulation results show that the proposed scheme is not only more feasible but also improving the ergodic capacity of both PR and CR.
Second, in consideration of some special cases, this thesis introduces some other constraints to protect PR's communications. Especially, we work hard to acquire the PR capacity loss constraints based on the channel learning in CR MIMO systems to provide protection to PR. Then, we get the optimal transmit strategy for a CR link to maximize the ergodic capacity subject to the PR capacity loss constraints and the CR transmit power constraints. In other words, we can achieve the tradeoff between environment learning and data transmission by this way. Simulation results show that the proposed method which protects the QoS of PR can improve the ergodic capacity of CR when the PR link experienced a deep fading channel or PR transmit power is extremely low.
本论文所进行的具体工作和创新可以归纳为如下两个方面：
第一,研究了干扰功率约束下的认知¨MIMO系统资源分配,并首次提出了一种新的资源分配方法。即先利用瑞利衰落信道统计特性推导PR均值干扰功率约束条件,再联合CR的发送功率约束等要素进行认知MIMO系统的资源分配设计。这样做不但可以消除传统方案中感知时间不确定度的影响,易于工程实践的应用,而且通过仿真验证可以看出,它还能够同时提高PR系统以及CR系统的遍历容量,使其获得更好的特性。
第二,考虑到PR通信过程中可能出现的复杂情况,本论文不仅分析了其他对PR通信进行保护的方案,还给出了一种新的设计方案。即在利用信道估计确定认知MIMO系统发送波束成形机制的基础上,推导出PR的容量损耗约束条件,并以最大化CR系统遍历容量为目标获得最优的认知MIMO系统资源分配方案,实现了环境感知和数据传输的折中。MATLAB仿真结果证明实际中当PR收发端之间为深衰落信道或者PR发送功率极低的时候,采用这种方法不仅不会影响PR的性能,还能够使相应CR系统的遍历容量得到提升。
With the continuous development of wireless communication, more and more advanced technologies have a higher demand of the spectrum resources which causes the contradiction between low utilization rate of authorized frequency spectrum and shortage of unauthorized spectrum resources. Therefore, it has hindered the speed of development greatly. As the key to solve this problem, CR MIMO which intends to reuse spectrum resources have got a great attention and widely studied. It not only improves the spectrum utilization but also increases the capacity. Meanwhile, it can remove the interference to the authorized users. In this thesis we consider SS (spectrum sharing) mode and find the suitable IT (interference temperature) constraints in CR MIMO systems.
The main contributions of this thesis can be summarized as two points:
First, we study the resource allocation strategy in CR MIMO systems under the interference power constraints. Considering the statistical properties of Rayleigh fading channels, this thesis first proposes a new method to gain the average interference power (AIP) constraints for PR in CR MIMO systems. Then we design the optimal transmit strategy for a CR link to maximize the ergodic capacity subject to the AIP constraints and CR transmit power constraints. Simulation results show that the proposed scheme is not only more feasible but also improving the ergodic capacity of both PR and CR.
Second, in consideration of some special cases, this thesis introduces some other constraints to protect PR's communications. Especially, we work hard to acquire the PR capacity loss constraints based on the channel learning in CR MIMO systems to provide protection to PR. Then, we get the optimal transmit strategy for a CR link to maximize the ergodic capacity subject to the PR capacity loss constraints and the CR transmit power constraints. In other words, we can achieve the tradeoff between environment learning and data transmission by this way. Simulation results show that the proposed method which protects the QoS of PR can improve the ergodic capacity of CR when the PR link experienced a deep fading channel or PR transmit power is extremely low.
引文
[1]ForceFSPT. Report of Spectrum Efficiency Working Group.[Online]. Available: gov/sptf/files/SEWGFinal Reportl.pdf.2002.
[2]Federal Communications Commission. Spectrum policy task force report.[Online] Available:hraunfoss.fcc.gov/edocs_public/attachmatch/DOC-228542Al.pdf. 2002.
[3]J. Mitola Ⅲ. Cognitive radio:An integrated agent architecture for software defined radio. Ph.D. dissertation. Stockholm, Sweden:Royal Inst. Technol., Dec.2000.
[4]Group D X W.The XG Architectural Framework.2003,1(0).
[5]赵梦欣.认知无线电网络路由协议研究与仿真[D].华中科技大学,2007.
[6]L. Zhang, Y. C. Liang and Y. Xin. Joint beamforming and power control for multiple access channels in cognitive radio networks. IEEE J. Select. Areas Commun.,2008,26(1):38-51.
[7]Rui Z, Ying C L. Dynamic resource allocation in cognitive radio networks. IEEE Signal Processing Magzine.2010,27(3):101-114.
[8]周潘.基于博弈论的认知无线电功率控制与速率分配研究[D].华中科技大学,2008.
[9]Cordeiro C, Challapali K, Birru D, et al. IEEE 802.22:The first worldwide wireless standard based on cognitive radios//Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore, USA,2005,328-337.
[10]S. Haykin. Cognitive radio:Brain-empowered wireless communications. IEEE J. Sel. Areas Commun.,2005,23(2):201-220.
[11]A. Sahai, D. Cabric. Spectrum sensing:Fundamental limits and practical challenges, in Proc. IEEE DySPAN (presented tutorial), Baltimore, MD, Nov. 2005.
[12]Ghasemi,E. S. Sousa. Spectrum sensing in cognitive radio networks:Requirements. challenges and design trade-offs. IEEE Commun. Mag..2008.46(4):32-39.
[13]A. J. Goldsmith. Wireless Communications. New York:Cambridge University Press.2005.
[14]Y.-C. Liang. Y. Zeng, E. C. Y. Peh and A. T. Hoang. Sensing-Throughput tradeoff for cognitive radio networks. IEEE Trans. Wireless Commun.,2008.7(4): 1326-1337.
[15]Yi yang Pei. Sensing-Throughput Tradeoff for Cognitive Radio Networks:A Multiple-Channel Scenario. IEEE Personal Indoor and Mobile Radio Commun., 2009,1257-1261
[16]Xing Y, Chetan N, Haleem M A. et al. Dynamic spectrum access with Qos and interference temperature constraints. IEEE Transactions on Mobile Computing, 2007,6(4):4420-4425.
[17]R. Zhang, Y.-C. Liang. Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks. IEEE J. Sel. Topics Sig. Process.,2008,2(1): 88-102.
[18]R. Zhang, F. Gao and Y.-C. Liang. Cognitive beamforming made practical: Effective interference channel and learning-throughput tradeoff. IEEE Trans. Commun.,2010,8(2):706-718.
[19]Feifei Gao, Rui Zhang, Ying-Chang Liang and Xiaodong Wang. Design of Learning-Based MIMO Cognitive Radio Systems. IEEE Trans,2010,59(4): 1707-1720.
[20]Telatar I.E. Capacity of Multi-antenna Gaussian Channels. AT&T-Bell Labs Technical Report. June 1995.
[21]Foschini G.J, Gans M.J. On Limits of Wireless Communications in a Fading Environment When Using Multiple Antennas. Wireless Personal Commun.,1998, 311-335.
[22]Biguesh M, Gershman A B. MIMO Channel Estimation:Optimal Training and Tradeoff s Between Estimation Techniques Communications [C].2004 IEEE International Conference,2004 (5):2658-2662.
[23]Wolniansky P.W., Foschini, G.J., Golden, G.D., etc. V-BLAST:an architecture for realizing very high data rates over the rich-scattering wireless channel. International Symposium on Signal, Systems, and Electronics.1998, pp.295-300.
[24]Jankiraman M. Space-time codes and MIMO systems, Artech House.,2004.
[25]Vucetic B. Yuan J.H. Space-Time Coding. Chichester U.K.:John Wiley & Sons., 2004.
[26]Carvalho E De, Slock D T M. Cramer-Rao bounds for semi-blind, blind and training sequence based channel estimation[C]. Signal Processing Advances in Wireless Communications, First IEEE Signal Processing Workshop on.1997, 129-132.
[27]Aldo G, Orozco-Lugo, M.Mauricio Lara, and Des C. McLernon. Channel Esimation Using Implicit Training. IEEE Transactions on Signal Processing,2004, 52(1),329-332.
[28]Liu H, Xu G, Tong L. et al. Recent developments in blind channel equalization: From cyclostationarity to subspaces[J]. Signal Processing,1996, vol.50,83-99.
[29]Carvalho E de, Slock D. T M. Semi-blind maximum-likelihood multichannel estimation with Gaussian pior for the symbols using soft decisions [C]. IEEE Vehicular Technology Conference,1998, Vol.2,1563-1567.
[30]Goldsmith A, Jafar S A, Mari'c I. et al. Breaking spectrum gridlock with cognitive radios:an information theoretic perspective. IEEE Proceeding,2009,7(5): 894-914.
[31]Xu Z. Perturbation analysis for subspace decomposition with application in sub space-based algorithms, IEEE Transactions on Signal Processing, Nov.2002, 51(11):2820-2830.
[32]Xin Kang. Optimal Power Allocation for Cognitive Radio under Primary User's Outage Loss Constraint. IEEE ICC 2009 proceedings,1-5.
[33]Kanapathippillai Cumanan, Rui Zhang, and Sangarapillai Lambotharan. A New Design Paradigm for MIMO Cognitive Radio with Primary User Rate Constraint. IEEE COMMUNICATIONS LETTERS,2012,16(5):706-709.
[34]R. Zhang. Optimal power control over fading cognitive radio channels by exploiting primary user CSI, in Proc. IEEE GLOBECOM,2008.
[35]Z. Xu. On the second-order statistics of the weighted sample covariance matrix. IEEE Trans. Signal Process.,2003,51(2):527-534.
[36]Rui. Z. On peak versus average interference power constraints for protecting primary users in cognitive radio networks. IEEE Trans. Wireless Commun.,2009, 8(4):2112-2120.
[37]D A. Gore. Transmit Selection in Spatial Multiplexing Systems. IEEE Commun. Lett.,2002,6(11):491-493.
[38]T. Cover and J. Thomas. Elements of Information Theory. New York:Wiley-Interscience,1991.
[39]S. Boyd and L. Vandenberghe. Cambridge UK:Convex Optimization. Cambridge University Press,2004.
[40]Rappaport Theodore S. Wireless Communications:Principles And Practice 2/E. Pearson Education,2010.
[2]Federal Communications Commission. Spectrum policy task force report.[Online] Available:hraunfoss.fcc.gov/edocs_public/attachmatch/DOC-228542Al.pdf. 2002.
[3]J. Mitola Ⅲ. Cognitive radio:An integrated agent architecture for software defined radio. Ph.D. dissertation. Stockholm, Sweden:Royal Inst. Technol., Dec.2000.
[4]Group D X W.The XG Architectural Framework.2003,1(0).
[5]赵梦欣.认知无线电网络路由协议研究与仿真[D].华中科技大学,2007.
[6]L. Zhang, Y. C. Liang and Y. Xin. Joint beamforming and power control for multiple access channels in cognitive radio networks. IEEE J. Select. Areas Commun.,2008,26(1):38-51.
[7]Rui Z, Ying C L. Dynamic resource allocation in cognitive radio networks. IEEE Signal Processing Magzine.2010,27(3):101-114.
[8]周潘.基于博弈论的认知无线电功率控制与速率分配研究[D].华中科技大学,2008.
[9]Cordeiro C, Challapali K, Birru D, et al. IEEE 802.22:The first worldwide wireless standard based on cognitive radios//Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore, USA,2005,328-337.
[10]S. Haykin. Cognitive radio:Brain-empowered wireless communications. IEEE J. Sel. Areas Commun.,2005,23(2):201-220.
[11]A. Sahai, D. Cabric. Spectrum sensing:Fundamental limits and practical challenges, in Proc. IEEE DySPAN (presented tutorial), Baltimore, MD, Nov. 2005.
[12]Ghasemi,E. S. Sousa. Spectrum sensing in cognitive radio networks:Requirements. challenges and design trade-offs. IEEE Commun. Mag..2008.46(4):32-39.
[13]A. J. Goldsmith. Wireless Communications. New York:Cambridge University Press.2005.
[14]Y.-C. Liang. Y. Zeng, E. C. Y. Peh and A. T. Hoang. Sensing-Throughput tradeoff for cognitive radio networks. IEEE Trans. Wireless Commun.,2008.7(4): 1326-1337.
[15]Yi yang Pei. Sensing-Throughput Tradeoff for Cognitive Radio Networks:A Multiple-Channel Scenario. IEEE Personal Indoor and Mobile Radio Commun., 2009,1257-1261
[16]Xing Y, Chetan N, Haleem M A. et al. Dynamic spectrum access with Qos and interference temperature constraints. IEEE Transactions on Mobile Computing, 2007,6(4):4420-4425.
[17]R. Zhang, Y.-C. Liang. Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks. IEEE J. Sel. Topics Sig. Process.,2008,2(1): 88-102.
[18]R. Zhang, F. Gao and Y.-C. Liang. Cognitive beamforming made practical: Effective interference channel and learning-throughput tradeoff. IEEE Trans. Commun.,2010,8(2):706-718.
[19]Feifei Gao, Rui Zhang, Ying-Chang Liang and Xiaodong Wang. Design of Learning-Based MIMO Cognitive Radio Systems. IEEE Trans,2010,59(4): 1707-1720.
[20]Telatar I.E. Capacity of Multi-antenna Gaussian Channels. AT&T-Bell Labs Technical Report. June 1995.
[21]Foschini G.J, Gans M.J. On Limits of Wireless Communications in a Fading Environment When Using Multiple Antennas. Wireless Personal Commun.,1998, 311-335.
[22]Biguesh M, Gershman A B. MIMO Channel Estimation:Optimal Training and Tradeoff s Between Estimation Techniques Communications [C].2004 IEEE International Conference,2004 (5):2658-2662.
[23]Wolniansky P.W., Foschini, G.J., Golden, G.D., etc. V-BLAST:an architecture for realizing very high data rates over the rich-scattering wireless channel. International Symposium on Signal, Systems, and Electronics.1998, pp.295-300.
[24]Jankiraman M. Space-time codes and MIMO systems, Artech House.,2004.
[25]Vucetic B. Yuan J.H. Space-Time Coding. Chichester U.K.:John Wiley & Sons., 2004.
[26]Carvalho E De, Slock D T M. Cramer-Rao bounds for semi-blind, blind and training sequence based channel estimation[C]. Signal Processing Advances in Wireless Communications, First IEEE Signal Processing Workshop on.1997, 129-132.
[27]Aldo G, Orozco-Lugo, M.Mauricio Lara, and Des C. McLernon. Channel Esimation Using Implicit Training. IEEE Transactions on Signal Processing,2004, 52(1),329-332.
[28]Liu H, Xu G, Tong L. et al. Recent developments in blind channel equalization: From cyclostationarity to subspaces[J]. Signal Processing,1996, vol.50,83-99.
[29]Carvalho E de, Slock D. T M. Semi-blind maximum-likelihood multichannel estimation with Gaussian pior for the symbols using soft decisions [C]. IEEE Vehicular Technology Conference,1998, Vol.2,1563-1567.
[30]Goldsmith A, Jafar S A, Mari'c I. et al. Breaking spectrum gridlock with cognitive radios:an information theoretic perspective. IEEE Proceeding,2009,7(5): 894-914.
[31]Xu Z. Perturbation analysis for subspace decomposition with application in sub space-based algorithms, IEEE Transactions on Signal Processing, Nov.2002, 51(11):2820-2830.
[32]Xin Kang. Optimal Power Allocation for Cognitive Radio under Primary User's Outage Loss Constraint. IEEE ICC 2009 proceedings,1-5.
[33]Kanapathippillai Cumanan, Rui Zhang, and Sangarapillai Lambotharan. A New Design Paradigm for MIMO Cognitive Radio with Primary User Rate Constraint. IEEE COMMUNICATIONS LETTERS,2012,16(5):706-709.
[34]R. Zhang. Optimal power control over fading cognitive radio channels by exploiting primary user CSI, in Proc. IEEE GLOBECOM,2008.
[35]Z. Xu. On the second-order statistics of the weighted sample covariance matrix. IEEE Trans. Signal Process.,2003,51(2):527-534.
[36]Rui. Z. On peak versus average interference power constraints for protecting primary users in cognitive radio networks. IEEE Trans. Wireless Commun.,2009, 8(4):2112-2120.
[37]D A. Gore. Transmit Selection in Spatial Multiplexing Systems. IEEE Commun. Lett.,2002,6(11):491-493.
[38]T. Cover and J. Thomas. Elements of Information Theory. New York:Wiley-Interscience,1991.
[39]S. Boyd and L. Vandenberghe. Cambridge UK:Convex Optimization. Cambridge University Press,2004.
[40]Rappaport Theodore S. Wireless Communications:Principles And Practice 2/E. Pearson Education,2010.