MIMO系统中检测算法的研究
|
摘要
随着无线通信技术的飞速发展,以及互联网与移动通信的融合,高速数据通信服务正在不断增长。多输入多输出(MIMO)技术能够在不损失带宽和发射功率的情况下,成倍地提高系统的容量,并使系统性能得到极大的提高,这些优点使其在频谱资源日益匮乏和系统容量需求急剧增加的今天倍受青睐。信号检测问题是MIMO系统中的一项关键技术问题。
本文首先论述了MIMO技术的研究背景、意义以及国内外研究现状,接着介绍了MIMO系统的基本概念,对MIMO系统的信道容量进行分析,同时给出了信道容量的计算公式及不同系统信道容量的仿真结果。V-BLAST系统能够有效利用MIMO系统容量,所以详细介绍了V-BLAST系统的结构、基本原理及其各种传统检测算法,其中包括最大似然检测(ML)、最小均方误差检测算法(MMSE)、迫零检测算法(ZF)、排序的串行干扰抵消算法(OSIC)、QR分解的检测算法与次优的球形译码算法(SD),并运用matlab软件对各种算法的性能进行仿真。
由于线性算法的性能较最优检测的性能差距太大,本文重点研究了一种次优的树搜索检测算法,即QRD-M检测算法。着重分析了该算法的原理与实现方法,讨论了基于扩展信道排序的QR分解(MMSE-SQRD),一种减少路径搜索过程中排序复杂度的Sort-free算法。与传统的QRD-M检测算法相比,本文的算法不仅使算法计算复杂度明显减少,还使算法的性能得到了改善。
High-speed data communication service is constantly growing with the rapid development of wireless communication technologies, as well as the integration of the Internet and mobile communications. Multiple-input multiple-output (MIMO) technology can improve the system capacity significantly and achieve better system performance without increasing the transmitting power and losing the bandwidth. These advantages make MIMO technology favored in today’s spectrum resources shortage problem when the need of system capacity is still dramatically increasing. Signal detection is a key technical issue of the MIMO system.
First, the background, significance and research status of MIMO technology in domestic and overseas are introduced. The basic concepts of MIMO system and channel capacity are also discussed. Furthermore, the formulations of channel capacity and simulation results under different systems are derived. Since the V-BLAST system can sufficiently utilize the capacity of MIMO, the structure of V-BLAST system, basis principles are discussed as well as several traditional detection algorithms such as including ML, MMSE, ZF, OSIC, QR, SQRD, and suboptimal SD. Finally, the performance of various algorithms is provided through Matlab.
Compared with the optimal algorithm, the performance of the linear algorithm is poor. The article focuses on a sub-optimal tree search detection algorithm-QRD-M detection algorithm, and analyzes the principle and realization of algorithm. Based on the extended channel ranking QR decomposition, a new algorithm is discussed. The new sort-free algorithm can reduce the complexity of sorting in path searching process. Compared with traditional QRD-M algorithm, the proposed algorithm not only reduces the computational complexity significantly, but also improves the algorithm performance.
本文首先论述了MIMO技术的研究背景、意义以及国内外研究现状,接着介绍了MIMO系统的基本概念,对MIMO系统的信道容量进行分析,同时给出了信道容量的计算公式及不同系统信道容量的仿真结果。V-BLAST系统能够有效利用MIMO系统容量,所以详细介绍了V-BLAST系统的结构、基本原理及其各种传统检测算法,其中包括最大似然检测(ML)、最小均方误差检测算法(MMSE)、迫零检测算法(ZF)、排序的串行干扰抵消算法(OSIC)、QR分解的检测算法与次优的球形译码算法(SD),并运用matlab软件对各种算法的性能进行仿真。
由于线性算法的性能较最优检测的性能差距太大,本文重点研究了一种次优的树搜索检测算法,即QRD-M检测算法。着重分析了该算法的原理与实现方法,讨论了基于扩展信道排序的QR分解(MMSE-SQRD),一种减少路径搜索过程中排序复杂度的Sort-free算法。与传统的QRD-M检测算法相比,本文的算法不仅使算法计算复杂度明显减少,还使算法的性能得到了改善。
High-speed data communication service is constantly growing with the rapid development of wireless communication technologies, as well as the integration of the Internet and mobile communications. Multiple-input multiple-output (MIMO) technology can improve the system capacity significantly and achieve better system performance without increasing the transmitting power and losing the bandwidth. These advantages make MIMO technology favored in today’s spectrum resources shortage problem when the need of system capacity is still dramatically increasing. Signal detection is a key technical issue of the MIMO system.
First, the background, significance and research status of MIMO technology in domestic and overseas are introduced. The basic concepts of MIMO system and channel capacity are also discussed. Furthermore, the formulations of channel capacity and simulation results under different systems are derived. Since the V-BLAST system can sufficiently utilize the capacity of MIMO, the structure of V-BLAST system, basis principles are discussed as well as several traditional detection algorithms such as including ML, MMSE, ZF, OSIC, QR, SQRD, and suboptimal SD. Finally, the performance of various algorithms is provided through Matlab.
Compared with the optimal algorithm, the performance of the linear algorithm is poor. The article focuses on a sub-optimal tree search detection algorithm-QRD-M detection algorithm, and analyzes the principle and realization of algorithm. Based on the extended channel ranking QR decomposition, a new algorithm is discussed. The new sort-free algorithm can reduce the complexity of sorting in path searching process. Compared with traditional QRD-M algorithm, the proposed algorithm not only reduces the computational complexity significantly, but also improves the algorithm performance.
引文
[1]彭小平,移动无线通信的过去和未来发展趋势[J].信息通信.2009(4):69-72.
[2]林云,何丰. MIMO技术原理及应用[M].北京:人民邮电出版社.2010.
[3]马晓慧,LTE下行链路关键技术的研究与实现[D].西安:西安电子科技大学.2009.
[4]谢显中,雷维嘉,移动通信中的空时信号处理[M],北京:电子工业出版社.2008.
[5]高洋,MIMO系统的均衡技术研究[D].西安:西北工业大学.2006.
[6]朱婧,应用于MIMO接收系统中的检测算法研究[D].西安:西安电子科技大学.2008.
[7] I. Emre Telatar. Capacity of Multi-antenna Gaussian Channels [J]. AT&T-Bell Labs Internal Tech. MIMO, June, 1995:1-28.
[8] G. J. FOSCHINI, M. J. GANS. On Limits of Wireless Communication in a Fading Environment When Using Multiple Antennas [J]. Wireless Personal Communication, March, 1998, 6(3): 311-335.
[9] A. J. PAULRAJ, D. Gore, R. U. NABAR, et al. An overview of MIMO communications-a key to gigabit wireless [J]. Proceedings of the IEEE, 2004, 92(2).
[10]黄嵘,MIMO系统中联合收发波束形成算法研究[D].成都:电子科技大学.2006.
[11]熊春林, MIMO系统的信号检测及迭代接收机技术研究[D],湖南:国防科学技术大学.2009.
[12]蒋欣, MIMO系统中空时检测算法研究[D].湖南:湖南大学.2006.
[13]黄滔,袁超伟等,MIMO相关技术与应用[M].北京:机械工业出版社.2007.
[14]肖扬,MIMO多天线无线通信系统[M].北京:人民邮电出版社.2009.
[15] D. Wubben, R. Bohnke and V. Kuhn , et al. Efficient algorithm for decoding layered space-time codes [J], Electronics Letters, 2001, 37(22):1348-1350.
[16] D.Wubben, R. Bohnke and V. Kuhn, MMSE extension of V-BLAST based in sorted QR decomposition [J], IEEE 58th Vehicular Technology Conference, 2003:508-512.
[17] W. L. Zhao, G.B. Giannakis. Sphere decoding algorithms with improved radius search. Wireless communications and networking conference[C], 2004, (4): 2290-2294.
[18] M.Albert,chan,Inkyu Lee. A new reduced-complexity sphere decoder for multiple antenna systems [J]. IEEE International conference on communications, 2002, (1):460-464.
[19] Q.W. Li, Z.Z. Wang. Improved k-best sphere decoding algorithms for MIMO systems [J].IEEE international symposium, 2006:1159-1162.
[20]熊春林,刘伟等,VBLAST系统中的裁减QRD-M树搜索检测算法[J].系统仿真学报.2009, 21(11):3378-3415.
[21] X. M. Wang, W. S. Lu, A. Antoniou. A near-optimal multi-user detector for CDMA systemsusing semi definite programming relaxation [J]. IEEE Trans. Signal Processing, 2003, (51):2246-2250.
[22]孙艳华,吴伟陵,分枝定界MIMO检测算法的推广及改进[J],高技术通信, 2006, 16(6): 556-559.
[23] C. Schlegel, L. Wei. A simple way to compute the minimum distance in multi-user CDMA systems [J]. IEEE Trans. Communication, 1997(45):532-535.
[24]梅中辉,殷玮玮,吴乐南, MIMO系统中一种低复杂度联合检测算法[J],信号处理, 2007,23(1): 112- 115.
[25]赵慧,MIMO系统中信号检测技术的研究[D].北京.北京邮电大学.2006.
[26]杨倩倩,基于量子神经网络的MIMO信号检测技术[J],计算机工程应用技术.2008: 1342-1345.
[27] D. Bin, W. Xiao dong, D. Arnaud. A new class of soft MIMO demodulation algorithms [J].IEEE Signal Processing Letters, 2003, 51(11):2752-2763.
[28] H. D. Zhu, Z. N. Shi, F. B. Behrouz. MIMO detection using Marko chain Monte Carlo techniques for near-capacity performance [J]. IEEE ICASSP, 2005:1017-1020.
[29]黄成,收发多天线系统抗烦扰技术研究[D].成都:电子科技大学.2006.
[30]黄丘林,MIMO无线通信技术研究[D],西安:西安电子科技大学.2007.
[31] L.Z. Zheng, D. Tse. Diversity and multiplexing a fundamental tradeoff in multiple antenna channels [J].IEEE Transaction on Information Theory, 2003, 49(5):1073-1096.
[32] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space time block coding for wireless communications: performance results [J]. IEEE JSAC, 1999, 17(3): 451-460.
[33] V. Tarokh, N. Seshadri and A. R. Calderbank. Space time codes for high data rate wireless communication: performance criterion and codes construction [J]. IEEE Trans IT, 1998, 44(2): 744-765.
[34] B. M. Hochwald and T. L. Marzetta. Unitary space time modulation for multiple antenna communication in Rayleigh flat fading [J]. IEEE Trans. Inform. Theory, 2000 46(2), 543-563.
[35] B. L. Hughes. Differential space time modulation [J].IEEE Trans. Inform .Theory, 2000, 46(7): 2567-2578.
[36]岳希亮,多用户MIMO系统空时分组编码联合波束成形技术研究[D],西安:西安电子科技大学.2008.
[37]杜晨光,MIMO无线通信系统资源管理算法研究及仿真[D],西安:西安电子科技大学.2008.
[38]王鹏,MIMO系统中若干关键技术的研究[D],北京:北京邮电大学.2006.
[39] G. J. Foschini. Layered space-time Architecture for wireless communication in fading environment when using multiple antennas [J]. Bell Labs Technical Journal, 1996: 41-59.
[40]艾渤,唐世刚译,MIMO通信系统编码[M],北京,电子工业出版社,2008.
[41] H. EI. Gamal and A. R. Hammons. A New Approach to Layered Space–Time Coding and signal processing [J]. IEEE Transactions on Information Theory, 2001, 47(6):2321-2334.
[42]林佳健, MIMO系统中的迭代检测技术[D],西安,西安电子科技大学,2008.
[43] D. Wubben, R. Bohnke, V. Kuhn et al. Reduced complexity MMSE detection for BLAST architectures [C]. IEEE, Global Telecommunications Conference, 2003, (4): 2258- 2262.
[44]张贤达,矩阵分析与应用[M],北京,清华大学出版社,2004.
[45] P. W. Wolniansky, G. J. Foschini, G. D. Golden, et al. VBLAST An architecture for realizing very high data rates over the rich scattering wireless channel[J].IEEE Signals Systems and Electronics,1998:295-300.
[46] S. Baro, G. Bauch, A. Pavlic, et al. Improving BLAST performance using space time block and Turbo decoding[C]. IEEE Global Telecommunication Conference, 2000, (2):1067-1071.
[47]贺中堂,张力军,陈自力等,MIMO-OFDM系统基于QR分解的信号检测算法[J],计算机工程与应用,2006(14):137-140.
[48] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space time block codes from orthogonal design [J]. IEEE Transactions on Information Theory.1999, 45(5):1456-1467.
[49] C. Windpassinger, R. F. H. Fischer. Low complexity near maximum likelihood detection and precoding for MIMO systems using lattice reduction [J]. IEEE Proc. Information Theory Workshop, 2003:345-348.
[50] B.Hassibi, H.Vikalo. On the sphere decoding algorithm I expected complexity [J]. IEEE Transaction on Signal Processing.2005, 53(8):2806-2818.
[51] J. Jalden, B. Ottersten. On the complexity of sphere decoding in digital communications [J]. IEEE Trans Signal Processing, 2005, 53(4):1474-1484.
[52] A. D. Murugan, H. EI. Gamal, M. O. Damen, et al. A unified framework for tree search decoding rediscovering the sequential decoder [J].IEEE Transactions on Inform Theory, 2006, 52(3):933-953.
[53] K. Su. Efficient maximum likelihood detection for communication over MIMO channels[R]. Technical report, University of Cambridge, 2005.
[54] J. Pons, P. Duvaut. New Approaches for Lowering Path Expansion Complexity of K-Best MIMO Detection Algorithms. IEEE International Conference on Communications, 2009:1-6.
[55] K. Amiri, C. Dick, R. Rao, et al. Novel sort-free detector with modified real valued decomposition ordering in MIMO systems. IEEE Global Telecommunications Conference, 2008:1-5.
[2]林云,何丰. MIMO技术原理及应用[M].北京:人民邮电出版社.2010.
[3]马晓慧,LTE下行链路关键技术的研究与实现[D].西安:西安电子科技大学.2009.
[4]谢显中,雷维嘉,移动通信中的空时信号处理[M],北京:电子工业出版社.2008.
[5]高洋,MIMO系统的均衡技术研究[D].西安:西北工业大学.2006.
[6]朱婧,应用于MIMO接收系统中的检测算法研究[D].西安:西安电子科技大学.2008.
[7] I. Emre Telatar. Capacity of Multi-antenna Gaussian Channels [J]. AT&T-Bell Labs Internal Tech. MIMO, June, 1995:1-28.
[8] G. J. FOSCHINI, M. J. GANS. On Limits of Wireless Communication in a Fading Environment When Using Multiple Antennas [J]. Wireless Personal Communication, March, 1998, 6(3): 311-335.
[9] A. J. PAULRAJ, D. Gore, R. U. NABAR, et al. An overview of MIMO communications-a key to gigabit wireless [J]. Proceedings of the IEEE, 2004, 92(2).
[10]黄嵘,MIMO系统中联合收发波束形成算法研究[D].成都:电子科技大学.2006.
[11]熊春林, MIMO系统的信号检测及迭代接收机技术研究[D],湖南:国防科学技术大学.2009.
[12]蒋欣, MIMO系统中空时检测算法研究[D].湖南:湖南大学.2006.
[13]黄滔,袁超伟等,MIMO相关技术与应用[M].北京:机械工业出版社.2007.
[14]肖扬,MIMO多天线无线通信系统[M].北京:人民邮电出版社.2009.
[15] D. Wubben, R. Bohnke and V. Kuhn , et al. Efficient algorithm for decoding layered space-time codes [J], Electronics Letters, 2001, 37(22):1348-1350.
[16] D.Wubben, R. Bohnke and V. Kuhn, MMSE extension of V-BLAST based in sorted QR decomposition [J], IEEE 58th Vehicular Technology Conference, 2003:508-512.
[17] W. L. Zhao, G.B. Giannakis. Sphere decoding algorithms with improved radius search. Wireless communications and networking conference[C], 2004, (4): 2290-2294.
[18] M.Albert,chan,Inkyu Lee. A new reduced-complexity sphere decoder for multiple antenna systems [J]. IEEE International conference on communications, 2002, (1):460-464.
[19] Q.W. Li, Z.Z. Wang. Improved k-best sphere decoding algorithms for MIMO systems [J].IEEE international symposium, 2006:1159-1162.
[20]熊春林,刘伟等,VBLAST系统中的裁减QRD-M树搜索检测算法[J].系统仿真学报.2009, 21(11):3378-3415.
[21] X. M. Wang, W. S. Lu, A. Antoniou. A near-optimal multi-user detector for CDMA systemsusing semi definite programming relaxation [J]. IEEE Trans. Signal Processing, 2003, (51):2246-2250.
[22]孙艳华,吴伟陵,分枝定界MIMO检测算法的推广及改进[J],高技术通信, 2006, 16(6): 556-559.
[23] C. Schlegel, L. Wei. A simple way to compute the minimum distance in multi-user CDMA systems [J]. IEEE Trans. Communication, 1997(45):532-535.
[24]梅中辉,殷玮玮,吴乐南, MIMO系统中一种低复杂度联合检测算法[J],信号处理, 2007,23(1): 112- 115.
[25]赵慧,MIMO系统中信号检测技术的研究[D].北京.北京邮电大学.2006.
[26]杨倩倩,基于量子神经网络的MIMO信号检测技术[J],计算机工程应用技术.2008: 1342-1345.
[27] D. Bin, W. Xiao dong, D. Arnaud. A new class of soft MIMO demodulation algorithms [J].IEEE Signal Processing Letters, 2003, 51(11):2752-2763.
[28] H. D. Zhu, Z. N. Shi, F. B. Behrouz. MIMO detection using Marko chain Monte Carlo techniques for near-capacity performance [J]. IEEE ICASSP, 2005:1017-1020.
[29]黄成,收发多天线系统抗烦扰技术研究[D].成都:电子科技大学.2006.
[30]黄丘林,MIMO无线通信技术研究[D],西安:西安电子科技大学.2007.
[31] L.Z. Zheng, D. Tse. Diversity and multiplexing a fundamental tradeoff in multiple antenna channels [J].IEEE Transaction on Information Theory, 2003, 49(5):1073-1096.
[32] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space time block coding for wireless communications: performance results [J]. IEEE JSAC, 1999, 17(3): 451-460.
[33] V. Tarokh, N. Seshadri and A. R. Calderbank. Space time codes for high data rate wireless communication: performance criterion and codes construction [J]. IEEE Trans IT, 1998, 44(2): 744-765.
[34] B. M. Hochwald and T. L. Marzetta. Unitary space time modulation for multiple antenna communication in Rayleigh flat fading [J]. IEEE Trans. Inform. Theory, 2000 46(2), 543-563.
[35] B. L. Hughes. Differential space time modulation [J].IEEE Trans. Inform .Theory, 2000, 46(7): 2567-2578.
[36]岳希亮,多用户MIMO系统空时分组编码联合波束成形技术研究[D],西安:西安电子科技大学.2008.
[37]杜晨光,MIMO无线通信系统资源管理算法研究及仿真[D],西安:西安电子科技大学.2008.
[38]王鹏,MIMO系统中若干关键技术的研究[D],北京:北京邮电大学.2006.
[39] G. J. Foschini. Layered space-time Architecture for wireless communication in fading environment when using multiple antennas [J]. Bell Labs Technical Journal, 1996: 41-59.
[40]艾渤,唐世刚译,MIMO通信系统编码[M],北京,电子工业出版社,2008.
[41] H. EI. Gamal and A. R. Hammons. A New Approach to Layered Space–Time Coding and signal processing [J]. IEEE Transactions on Information Theory, 2001, 47(6):2321-2334.
[42]林佳健, MIMO系统中的迭代检测技术[D],西安,西安电子科技大学,2008.
[43] D. Wubben, R. Bohnke, V. Kuhn et al. Reduced complexity MMSE detection for BLAST architectures [C]. IEEE, Global Telecommunications Conference, 2003, (4): 2258- 2262.
[44]张贤达,矩阵分析与应用[M],北京,清华大学出版社,2004.
[45] P. W. Wolniansky, G. J. Foschini, G. D. Golden, et al. VBLAST An architecture for realizing very high data rates over the rich scattering wireless channel[J].IEEE Signals Systems and Electronics,1998:295-300.
[46] S. Baro, G. Bauch, A. Pavlic, et al. Improving BLAST performance using space time block and Turbo decoding[C]. IEEE Global Telecommunication Conference, 2000, (2):1067-1071.
[47]贺中堂,张力军,陈自力等,MIMO-OFDM系统基于QR分解的信号检测算法[J],计算机工程与应用,2006(14):137-140.
[48] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space time block codes from orthogonal design [J]. IEEE Transactions on Information Theory.1999, 45(5):1456-1467.
[49] C. Windpassinger, R. F. H. Fischer. Low complexity near maximum likelihood detection and precoding for MIMO systems using lattice reduction [J]. IEEE Proc. Information Theory Workshop, 2003:345-348.
[50] B.Hassibi, H.Vikalo. On the sphere decoding algorithm I expected complexity [J]. IEEE Transaction on Signal Processing.2005, 53(8):2806-2818.
[51] J. Jalden, B. Ottersten. On the complexity of sphere decoding in digital communications [J]. IEEE Trans Signal Processing, 2005, 53(4):1474-1484.
[52] A. D. Murugan, H. EI. Gamal, M. O. Damen, et al. A unified framework for tree search decoding rediscovering the sequential decoder [J].IEEE Transactions on Inform Theory, 2006, 52(3):933-953.
[53] K. Su. Efficient maximum likelihood detection for communication over MIMO channels[R]. Technical report, University of Cambridge, 2005.
[54] J. Pons, P. Duvaut. New Approaches for Lowering Path Expansion Complexity of K-Best MIMO Detection Algorithms. IEEE International Conference on Communications, 2009:1-6.
[55] K. Amiri, C. Dick, R. Rao, et al. Novel sort-free detector with modified real valued decomposition ordering in MIMO systems. IEEE Global Telecommunications Conference, 2008:1-5.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |