OFDMA-MIMO系统区分用户移动速度的无线资源自适应分配算法研究
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摘要
随着我国新一轮大规模基础建设的兴起,以高速铁路为代表的高速移动环境下无线宽带业务需求也随之不断的增长,高速移动环境下的通信系统引起了人们越来越多的关注。在高速移动环境下,无线通信具有频率偏移较大、信道条件变化较快等特点,现有的无线资源分配方式,已不能完全满足高速移动用户的需求。
当用户高速移动时,其信道质量变化较快,短时间内可能经历深度衰落,若此时仍采用和低速移动用户相同的资源分配方式,将导致一段时间内高速移动用户误码性能的急剧恶化。因此,需要在充分考虑高速、低速移动用户信道变化的特点的基础上结合其服务质量(Quality of Service, QoS)需求进行资源分配。本文主要研究OFDMA-MIMO系统中在保证用户QOS需求的前提下充分利用用户的无线信道变化特性,合理的分配无线资源,优化系统性能。
本文在首先介绍了高速移动环境和相关技术背景及原理,并简单分析了快速变化的信道条件对自适应调制编码技术的影响。其次,介绍了正交振幅调制(Quadrature Amplitude Modulation, QAM)技术的相关原理,并分析了QAM信号星座图的最小欧氏距离与误码率以及QAM信号能量间的关系。然后分析给出了高速、低速移动用户的信号预处理方式。同时,结合高速、低速移动用户的信号预处理方式,详细介绍了高速、低速移动用户资源分配的依据——满足用户误码要求的最小欧氏距离门限值的计算方式,并据此提出了一种以贪婪算法为基础的区分用户移动速度的无线资源分配算法。最后,介绍了OFDMA-MIMO系统相关仿真参数,搭建了系统仿真平台,并结合了二维时频资源分配特性对所提算法进行了仿真分析与评价。仿真分析表明,该算法不仅能够很好的满足低速用户的QoS需求,并能在保证高速移动用户的QoS需求的情况下,为高速、低速移动用户合理的分配无线资源,优化了系统性能。
With the coming of the new round of large-scale infrastructure investment, we demand for wireless broadband services in the high-speed mobile environment which is represented by the continuous development of high-speed railway. Wireless communication system in the high-speed mobile environment has attracted more and more attention. Due to the constraints of larger frequency offset, fast channel variation and other factors, the existed wireless resources allocation schemes cannot satisfy the QoS requirements of users when they move fast.
When a user moves fast, he/she may experience deep fading in a short time because of the fast channel variation, which will result in the sharp deterioration of BER performance. Therefore, resources allocation needs to consider the QoS requirements of users while the characteristics of the high-speed mobility are taken into account. This thesis focuses on how to allocate radio resource reasonably, in order to optimize system performance on the basis of ensuring the QoS requirements.
We firstly introduce the characteristics of high-speed mobile environment and the related technical background. On this basis, the impact to AMC technology due to the fast channel variation is analyzed. Then we introduce the theory of QAM technology, and discusse the relationship between the minimum Euclidean distance of the QAM signal constellation and the BER as well as the energy of the QAM signal. Futhermore, we give the signal preprocessing method of high-speed user and low-speed user. Based on this, we detail the principles of resource allocation for high-speed and low-speed users, which calculates the threshold of minimum Euclidean distance that satisfies the requirements of user BER. After that, based on greedy algorithm, a resources allocation algorithm which distinguishes high-speed and low-speed users is proposed. Finally, we introduce the related simulation parameters of OFDMA-MIMO system and build up system simulation platform. Combined with the characteristic of the two-dimensional time-frequency, we give the simulation and evaluation results. The simulation results show that this algorithm can not only meet the QoS requirements of low-speed users, but also ensure the QoS demand of high-speed users through allocating radio resource reasonably which aims to optimize system performance.
当用户高速移动时,其信道质量变化较快,短时间内可能经历深度衰落,若此时仍采用和低速移动用户相同的资源分配方式,将导致一段时间内高速移动用户误码性能的急剧恶化。因此,需要在充分考虑高速、低速移动用户信道变化的特点的基础上结合其服务质量(Quality of Service, QoS)需求进行资源分配。本文主要研究OFDMA-MIMO系统中在保证用户QOS需求的前提下充分利用用户的无线信道变化特性,合理的分配无线资源,优化系统性能。
本文在首先介绍了高速移动环境和相关技术背景及原理,并简单分析了快速变化的信道条件对自适应调制编码技术的影响。其次,介绍了正交振幅调制(Quadrature Amplitude Modulation, QAM)技术的相关原理,并分析了QAM信号星座图的最小欧氏距离与误码率以及QAM信号能量间的关系。然后分析给出了高速、低速移动用户的信号预处理方式。同时,结合高速、低速移动用户的信号预处理方式,详细介绍了高速、低速移动用户资源分配的依据——满足用户误码要求的最小欧氏距离门限值的计算方式,并据此提出了一种以贪婪算法为基础的区分用户移动速度的无线资源分配算法。最后,介绍了OFDMA-MIMO系统相关仿真参数,搭建了系统仿真平台,并结合了二维时频资源分配特性对所提算法进行了仿真分析与评价。仿真分析表明,该算法不仅能够很好的满足低速用户的QoS需求,并能在保证高速移动用户的QoS需求的情况下,为高速、低速移动用户合理的分配无线资源,优化了系统性能。
With the coming of the new round of large-scale infrastructure investment, we demand for wireless broadband services in the high-speed mobile environment which is represented by the continuous development of high-speed railway. Wireless communication system in the high-speed mobile environment has attracted more and more attention. Due to the constraints of larger frequency offset, fast channel variation and other factors, the existed wireless resources allocation schemes cannot satisfy the QoS requirements of users when they move fast.
When a user moves fast, he/she may experience deep fading in a short time because of the fast channel variation, which will result in the sharp deterioration of BER performance. Therefore, resources allocation needs to consider the QoS requirements of users while the characteristics of the high-speed mobility are taken into account. This thesis focuses on how to allocate radio resource reasonably, in order to optimize system performance on the basis of ensuring the QoS requirements.
We firstly introduce the characteristics of high-speed mobile environment and the related technical background. On this basis, the impact to AMC technology due to the fast channel variation is analyzed. Then we introduce the theory of QAM technology, and discusse the relationship between the minimum Euclidean distance of the QAM signal constellation and the BER as well as the energy of the QAM signal. Futhermore, we give the signal preprocessing method of high-speed user and low-speed user. Based on this, we detail the principles of resource allocation for high-speed and low-speed users, which calculates the threshold of minimum Euclidean distance that satisfies the requirements of user BER. After that, based on greedy algorithm, a resources allocation algorithm which distinguishes high-speed and low-speed users is proposed. Finally, we introduce the related simulation parameters of OFDMA-MIMO system and build up system simulation platform. Combined with the characteristic of the two-dimensional time-frequency, we give the simulation and evaluation results. The simulation results show that this algorithm can not only meet the QoS requirements of low-speed users, but also ensure the QoS demand of high-speed users through allocating radio resource reasonably which aims to optimize system performance.
引文
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[37]黄海,MIMO-OFDM系统中编码技术的研究,北京邮电大学硕士论文,2006年3月.
[38]秦艳,OFDM系统MIMO技术应用研究,西安电子科技大学硕士论文,2007年1月.
[39]程楠,周猛,孟德香,张玉胜,移动WiMAX系统AMC研究,电信科学,2007年第7期.P51-55.
{40] Shenli Zhou, Georgios B.Giannakis, Optimal Transmitter Eigen-Beamforming and Space-Time Block Coding Based on Channel Mean Feedback, IEEE Transactions on Signal Processing,2002 Vol.50 pages:2599-2613.
[41]T. Pollet, M. Van Bladel, and M. Moeneclaey, BER sensitivity of OFDM systems to carrier frequency offset and Wiener phase noise, IEEE Trans. Commun,,1995,43(2/3/4):191-193.
[42]马月槐,蔡跃明,徐友云,一种多用户MIMO-OFDM系统中的天线与子载波分配算法,信号 处理,2008年2月第24卷第1期,p40-44.
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[53]Ya Han Pan, Aissa S, Dynamic resource allocation for broadband MIMO-OFDM systems, International Conference on Wireless Networks, Communications and Mobile Computing,2005 vol.2 Page(s):863-867.
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[2]Y Long, L Kuang, J Lu, An ICI mitigation method for high speed mobile OFDM, IEICE Transactions on,2008 Volume 31. Pages:4130-4135.
[3]Min Gong, Chao Zhang, Jianhua Lu, Xiaoang Lin, Dynamic Resource Allocation in High Speed Mobile OFDMA System, ICC,2008 Pages:3335-3339.
[4]Han Jung Su,Do Joo Hyun, Kwak Kyung Sup, Choi Hyung Jin, Robust SIR Measurement Algorithm with Closed Loop Power Control in High Speed Mobile Channel Environment, IEICE Trans Commun,2006 Pages:2819-2827.
[5]KW Park, YS Cho, An MIMO-OFDM technique for high-speed mobile channels, IEEE Communications Letters,2005 Volume.9 Page(s):604-606.
[6]芮鹤龄,高速环境对TD_SCDMA系统物理层影响研究,电信工程技术与标准化,2008年10月,pages：79-81.
[7]田龙,高速移动环境下OFDM系统的频偏估计方法,上海交通大学硕士论文,2008年1月.
[8]高爱勤,高速铁路移动信道条件下的OFDM技术的研究,北京交通大学硕士论文,2007年12月.
[9]秦升平,邱岩,尹长川,李剑锋,乐光新,高速移动Rayleigh信道下的相位调制OFDM系统,电路与系统学报,2004年6月,第9卷,第3期.
[10]GJ.Foschini, M.J.Gans.On Limits of Wireless Communications in a Fading Environment when using Multiple Antennas, Wireless personal communications.1998 Pages:311-335.
[11]Chiurtu N, Rimoldi B, Telatar E, On the capacity of multi-antenna Gaussian channels, IEEE International Symposium on Information Theory,2001.
[12]Li-Chun Wang and Chu-Jung Yeh. Adaptive joint subchannel and power allocation for multi-user MIMO-OFDM systems, Digital Object Identifier 10.1109/PIMRC.2008.4699623.
[13]Xiaofeng Lu; Zan li; Jueping Cai; Xiaojun Chen. An Adaptive Resource Allocation Algorithm Based on Spatial Subchannel in Multiuser MIMO/OFDM Systems, ICC.2008 Page(s):4532-4536.
[14]Pascual-Iserte, A.; Perez-Neira, A.I.; Lagunas M.A, On power allocation strategies for maximum signal to noise and interference ratio in an OFDM-MIMO system, Digital Object Identifier 10.1109/TWC.2004 Volume 3,Page(s):808-820.
[15]Guangyi Liu; Xiantao Liu; Ping Zhang. QoS oriented dynamical resource allocation for eigen beamforming MIMO OFDM, Digital Object Identifier 10.1109/VETECF.2005.1558180,2005 Volume 3 Page(s):1450-1454.
[16]Chemaly R, Letaief K.B, Zeghlache D, Adaptive resource allocation for multiuser MIMO-OFDM networks based on partial channel state information, IEEE Global Telecommunications Conference, 2005.
[17]Ya-Han Pan, Letaief K.B, Zhigang Cao, Dynamic resource allocation with adaptive beamforming for MIMO OFDM systems under perfect and imperfect CSI, IEEE Wireless Communications and Networking Conference,2004.
[18]Rey F, Lamarca M, Vazquez G, Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI, IEEE Transactions on Signal Processing,2005:Volume:53.
[19]Ya-Han Pan, Aissa S, Performance analysis of selective space-time coding and selection diversity under perfect and imperfect CSI, IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications,2005.
[20]Yuanyuan Ma, Patzold M, Performance Comparison of Space-Time Coded MIMO-OFDM Systems Using Different Wideband MIMO Channel Models, ISWCS 2007., Page(s):762-766.
[21]I 梅德弗德夫,J.R沃尔顿,J.W凯淳,部分信道状态信息(CSI)多输入、多输出(MIMO)系统的功率控制,中国专利局,申请号：CN200710142201.5,2008：2.
[22]Pengfei Xia, Shengli Zhou, Georgios B.Giannakis. Adaptive MIMO-OFDM based on partial channel state information, IEEE transactions on signal processing, Jan.2004 vol.52, NO 1.
[23]S. Zhou and G B. Giannakis, Adaptive modulation for multiantenna transmissions with channel mean feedback, IEEE Trans. Wireless Commun on Wireless Communications,2004 vol.3 Page(s): 1626-1636.
[24]鞠建波,陈建勇,江帆,一种高速移动衰落信道的特性与分析,电信技术,2002年第5期,pages：124-127.
[25]王欣,高速移动环境下OFDM系统关键技术的研究,北京交通大学博士论文,2006年10月.
[26]樊昌信,詹道庸,徐炳祥,通信原理,北京：国防工业出版社,2001.
[27]宋铁成,正交频分复用(OFDM)移动通信系统关键技术的研究,东南大学博士学位论文,2006年3月.
[28]舒治安,OFDM系统关键技术研究,哈尔滨工程大学博士论文,2006年8月.
[29]吕浚哲,高效OFDM传输中的关键技术及其空时处理,西安电子科技大学博士论文，2004年4月.
[30]苏听,MIMO无线通信系统中若干问题的研究,西安电子科技大学博士论文,2006年6月.
[31]董伟,MIMO系统中关键技术研究,西安电子科技大学博士论文,2008年12月.
[32]黄邱林,MIMO无线通信技术研究,西安电子科技大学博士论文,2007年1月.
[33]S M Alamouti. A Simple Transmit Diversity Technique for Wireless Communications, IEEE Journal on Selected Areas in Communications,1998,16(8):145-1458.
[34]Yuheng Huang, Ritcey J.A, The capacity of bit-interleaved space-time coded modulation with imperfect channel state information, Signals, Systems and Computers,2003 Vol.1 Page(s): 1042-1046.
[35]Athaudage C.R.N, Sathananthan K, Probability of error of space-time coded OFDM systems with frequency offset in frequency-selective Rayleigh fading channels, IEEE International Conference on Communications,2005. Vol.4 Page(s):2593-2599.
[36]钱轶群,MIMO无线通信系统中的空时编码与预编码研究,东南大学博士论文,2006年11月.
[37]黄海,MIMO-OFDM系统中编码技术的研究,北京邮电大学硕士论文,2006年3月.
[38]秦艳,OFDM系统MIMO技术应用研究,西安电子科技大学硕士论文,2007年1月.
[39]程楠,周猛,孟德香,张玉胜,移动WiMAX系统AMC研究,电信科学,2007年第7期.P51-55.
{40] Shenli Zhou, Georgios B.Giannakis, Optimal Transmitter Eigen-Beamforming and Space-Time Block Coding Based on Channel Mean Feedback, IEEE Transactions on Signal Processing,2002 Vol.50 pages:2599-2613.
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