The COST 273 MIMO Channel Model Three Kinds of Clusters

Full-Dimension MIMO (FD-MIMO) for Next Generation Cellular Technology AbstractThis article considers a practical implementation of massive MIMO system.Although the best performance can be achieved when a large number of active antennas are placed only in horizontal domain, BS form factor(形状系数) limitation often makes horizontal array placement infeasible. To cope with this limitation, this article introduces full-dimension MIMO (FD-MIMO) cellular wireless communication system, where active antennas are placed in a 2D grid at BSs.动机：大量有源天线水平放置可以达到最好性能，但基站的形状系统限制使水平阵列放置不灵活。

为此引入full-dimension MIMO 蜂窝无线通信系统，其中基站端的有源天线以2维网络状分布。

For analysis of the FD-MIMO systems, a 3D spatial channel model is introduced, on which system-level simulations are conducted.内容：为了分析FD-MIMO系统，介绍了一个3维空间信道模型，并在该信道模型的基础上进行了系统级的仿真。

The simulation results show that the proposed FD-MIMO system with 32 antenna ports achieves 2–3.6 times cell average throughput gain and 1.5–5 times cell edge throughput gain compared to the 4G LTE system of two antenna ports at the BS.结论：仿真结果显示，相比于具有2天线端口的4G LTE系统，所提FD-MIMO系统在拥有32天线端口天线时，小区平均吞吐量增加2-3.6倍，小区边缘吞吐量增加1.5-5倍。

基于相关矩阵的MIMO建模与仿真杨楠;冯国良【摘要】随着信息技术的不断发展,频谱资源变的日益匮乏,而MIMO技术以其巨大的优势引起了学术界的广泛关注.通过研究MIMO信道的衰落特性与信道建模方法,进一步的分析影响MIMO无线信道性能的主要因素.本文对MIMO无线信道的基本原理、特性及应用进行了深入探究,并对MIMO信道建模理论和方法进行了分析,采用相关矩阵法建立合理的信道模型,并对角度功率谱、发射机和接收机的结构、多普勒功率谱密度、功率延迟等参数进行了合理化设置,最后对MIMO信道和高斯信道进行对比分析.%With the development of technology,the spectrum resource becomes more and more scarce,and MIMO technology has attracted wide attention from various research institutions with its enormous advantages.In depth research on MIMO wireless channel transmission theory,characteristics and key technologies,we must establish the channel model which can reasonably reflect the characteristics of MIMO transmission environment and channel fading,By studying the fading characteristics of MIMO channel and the channel modeling method,the main factors that affect the performance of MIMO wireless channel are analyzed.In this paper,the basic principles,characteristics and applications of MIMO wireless channel are deeply explored,and the theory and method of MIMO channel modeling are analyzed,The correlation matrix method to establish the reasonable channel model,and the angular power spectrum,transmitter and receiver structure,Doppler power spectraldensity,power delay and other parameters are set,finally carries on the contrast analysis to the MIMO channel and the Gaussian channel.【期刊名称】《电子设计工程》【年(卷),期】2017(025)024【总页数】4页(P74-77)【关键词】MIMO;相关矩阵;信道模型;传输环境【作者】杨楠;冯国良【作者单位】西安铁路职业技术学院陕西西安710014;西安铁路职业技术学院陕西西安710014【正文语种】中文【中图分类】TN915.9LTE系统采用多输入多输出（MIMO）技术用以应对系统对高容量、高速率和高移动性的需求。

TELE 9754 Coding and InformationTheoryResearch Workshop ReportAbstract—Mobile wireless communication has become one of the most important aspects of our daily life. The continuously increasing usage has imposed great pressure upon telecommunication system where the availability of channel capacity and spectral resources are limited. Multiple Input Multiple Output (MIMO) is considered as one of the possible solutions to the above problem and has attracted considerable attention among researchers and engineers in the field of mobile communication due to the great advantages it exhibits. In recent years, MIMO technology has been developed into more sophisticated forms and utilized in some common communication devices around us. This report is intended to provide readers with a brief review of the historical and technological developments of MIMO, and its applications.I. INTRODUCTIONOur wireless communication systems have undergone remarkable developments and progresses in the past 20 years, from 1G to 4G and the upcoming 5G. Such systems have provided our life with significant conveniences which were otherwise impossible and unachievable before the 1980s. However, under the condition of limited bandwidth resources and channel capacity, the developing communication scheme is unable to meet the fast growing demand from users of mobile devices. In other words, our communication system has somewhat attained its bottleneck and needs some new technology to enhance its performance. On the other hand, MIMO equipped with modern efficient signal processing techniques and processing hardware demonstrates prominent characteristics that could be taken to mitigate the above problems. MIMO can be defined, in simple terms, as a system which consists of multiple antennas at both the transmitter and receiver sides [6]. A systematic diagram of MIMO is illustrated by Figure 1.Figure 1. Systematic diagram of a MIMO systemThe underlying fact which enables MIMO to attract intense attention is that it could exploit the advantages of beamforming gain, spatial diversity and spatial multiplexing to enhance the performance of a communication system without extra consumption of spectral resources.The content of this report is organized in six separate sections. Section II offers readers a set of abbreviations used throughout the report. Section III illustrates the historical developments and milestones of MIMO from theory to implementations. Section IV introduces, in general sense, how MIMO functions and achieves the aforementioned advantages. Section V categorizes MIMO into various classes based on the properties it composes and some comparisons among them would be made. Section VI provides some examples of application of MIMO in modern communication scheme. Finally, a brief conclusion will be drawn in Section VI. Additional information can be found by referring to the Appendix section.II. TABLE OF ABBREVIATIONSThe following table (Table 1) lists a set of commonlyA BRIEF REVIEW ON MIMO TECHNOLOGY AND ITS APPLICATIONSLikai Ma z3326280used abbreviations to which will be referred in the following sections of this report. Table 1. Table of abbreviations III. HISTORICAL DEVELOPMENT OF MIMO [1] The history of MIMO can be dated back several decades ago. Although the idea of MIMO was not proposed until the 1970s, antenna arrays, also known as smart antennas (illustrated in Figure 2) had been developed to take the advantage of diversity and enhance wireless transmission and reception in analogue communications. CLASSIFICATION OF MIMO Figure 2. An example of antenna array. The idea of MIMO was first conceived in the 1970s in Bell Laboratory, which was inspired by the desire to overcome the problem of bandwidth limitation and interference in transmission cables. Such idea was too difficult to be realized and had remained in the form of theory for a long period of time, due to the limitation that the processing hardware and signal processing algorithms available at that stage was unable to support MIMO signal processing. Nevertheless, the theory of MIMO had continued to be enriched by some of the early researchers ’, including A.R Kaye, D.A George, Branderburg, Wyner and W. Van. Etten. In the late 1980s, MIMO theory had further been developed by Jack Salz and Jack Winters whose work centralizedaround the idea of beamforming.The concept of SM was proposed in 1993 by Arogyaswami Paulraj and Thomas. In 1996, Greg Raleigh and Gerard J. Foschini further developed the approaches towards MIMO using co-located antennas at the transmitter. Significant breakthrough in practical application of MIMO did not take place until the late 1990s. In 1998, SM was first demonstrated in the formFigure 2.Timeline of development of MIMO.of prototype in Bell Lab. Since then, the development of MIMO had been accelerated and some products with such technology integrated started to be available commercially. In 2002, Iospan Wireless Inc. launch the first commercial product with MIMO embedded, which was a milestone in the real application of this technology. Later, in 2005, the first standard of WLAN (IEEE 802.11n), also commonly known as Wi-Fi, with MIMO-OFDM was produced by Airgo Networks and has become more and more popular since then. The more detailed historical development of MIMO is depicted as a timeline and can be found in Figure 2.IV. HOW DOES MIMO WORKThe underlying principle of MIMO is that signals transmitted and received at both the transmitter and receiver sides combine together so that either parallel data sub-streams are formed or SNR is improved [3]. The benefits that MIMO exploits are known as beamforming, spatial diversity and spatial multiplexing.Figure 3.Smith chart showing the technique of beamformingBeamforming is achieved by focusing energy in some desired angular direction through appropriate choice of antenna parameters [1, 2]. The Smith chart in Figure 3 illustrates the idea of beamforming where the main lob is pointing at a particular angular direction while the side lobes are significantly suppressed. When the channel between the transmitter and receiver are located within the range of LOS, MIMO can be configured to exploit the advantages of beamforming so that the antenna gains combine constructively and thereby an enhanced receiving power and SNR are attained in the link.When multiple copies of a signal are transmitted from the transmitter, they may subject to non-idealities in the communication channel, for example fading, reflection and refraction, to different extents. Multiple replicas of the signal incoming from different directions can be analyzed by employing some sophisticated DSP algorithms to recover the original transmitted signal if those signals are highly uncorrelated. Such technique is referred as spatial diversity [2]. In general, the more the extent of uncorrelation, the better the effect of spatial diversity. MIMO could also take the advantages of spatial diversity to improve the quality of the received signal (ie, increased SNR) and hence to provide a more reliable communication link.Figure 4. The MIMO channel capacity increases almost linearly with the number of transmitting or receiving antennas [5]In a fading channel, particularly Rayleigh fading with CSI known to the receiver, MIMO could form a number of parallel and independent sub-channels through which a code word can be divided into a number of pieces and transmitted separately [4, 5]. In other words, a higher transmission rate (channel capacity) could be achieved. In theoretical sense, the channel capacity increases approximately with the number of transmitting or receiving antennas, as depicted in Figure 4. This discovery has a tremendous implicationuponcommunication system, that higher information exchange rate can be achieved without consuming extra bandwidth, by introducing additional antennas at the transmitter and receiver sides. The benefits exploited by MIMO are summarized in the following table (Table 2).Table 2. Summary table of MIMO techniquesIn general, beamforming, spatial diversity and spatial multiplexing are three rivaling techniques that engineers should make appropriate decisions on what could be sacrificed in order to gain more advantages from the others. The inter-relations among these techniques are depicted in Figure 5 [2].Figure 5. Inter-relations among three MIMO techniquesAlthough they are rivaling factors, they are not necessarily mutually exclusive, meaning that by making appropriate decisions on to what extent those are used, one can design a communication scheme which employs a combination of those techniques such that certain degrees of advantages of them can be involved. Such decision should be based solely upon the specific engineering problem to be solved. V. V ARIOUS TYPES OF MIMOA MIMO system can be divided into different classes according to some specific criterion. A MIMO system is commonly classified according to the criterions that whether multiple users are able to be served simultaneously. The classifications is shown as in Figure 6.Figure 6. Classification of MIMOIn the case of multiple users, a MIMO system is referred as SU-MIMO if only a single user among them is served at a time. In contrary, the term MU-MIMO is defined for the case where multiple users can be served in parallel. The following figure (Figure 7) depicts a comparison between SU-MIMO and MU-MIMO.Figure 7. Comparison between SU-MIMO and MU-MIMO [7](A) SU-MIMO SYSTEM [7, 8]In SU-MIMO, the time-frequency resources are allocated entirely to a single user in a given communication session. If SM is employed, multiple sub-streams can then be created to scale up the channel capacity by the order dictated by the minimum of transmitting or receiving antennas. Different users can be served through the use of TDMA or FDMA.One can see that since the order of increases in channel capacity in SU-MIMO is limited by the transmitter or receiver side which consists of the smallest number of antennas, the improvements in channel throughput may be very limited, particularly for cellular communication networks. In other words, the user end would likely be the constraint on the enhancements of channel capacity. The number of antennas that can be integrated to the users’ mobile devices, such as mobile phones, is very limited, mainly due to limitations like portability and space availability.(B)MU-MIMO SYSTEM [7, 8]MU-MIMO can be considered as an extension to the theory of SU-MIMO. In a MU-MIMO system, multiple users can be served in parallel with the same time-frequency resources available. By exploiting the advantages of SM, the channel throughput for MU-MIMO can then be enhanced by the number of transmit antennas with sufficient number of users, namely a similar scaling principle carried by the case of SU-MIMO.As oppose to SU-MIMO, MU-MIMO better exploits the multiplexing gain provided by SM, which is achieved by allocating different users to different sub-channels. Different users can not only be served by employing TDMA or FDMA (in SU-MIMO), but also by means of SDMA. Therefore, MU-MIMO has more advantages over SU-MIMO in terms of time, frequency and spatial allocations.VI. APPLICATION OF MIMO IN MODERNCOMMUNICATION SCHEME [3]As the developments in both powerful signal processing hardware and more sophisticated MIMO models have become available in recent years, the application of MIMO in our modern communication systems have been made possible as oppose to the past, mainly by the ITU and 3GPP.Some of the common communication systems, including the 3G/4G network, Wi-Fi (IEEE 802.11n) and WiMAX have already integrated some MIMO technologies to a certain extent where various forms of MIMO have been deployed and different advantages are exploited. The use of MIMO technology in modern communication systems can be depicted by the following figure (Figure 8).Figure 8.Application of MIMO in modern communication systems.The current CDMA2000 standard, one of the 3G standards (WCDMA, CDMA2000 and TD-SCDMA) has adopted transmit diversity, while the WCDMA-based UMTS has also enabled implementation of transmit diversity and beamforming at base stations. Furthermore, the 3GPP LTE employs SU-MIMO with SM and STC. The more advanced version, so called 3GPP LTE-Advanced further extends from what has been designed in LTE and has involved MU-MIMO and multi-cell MIMO.In IEEE802.16 standard (also commonly known as WiMAX), MIMO-OFDMA, a technique that utilizes OFDM modulation scheme in combination with multiple antennas, has been deployed.IEEE802.11n or Wi-Fi is another commonly used communication standard and has implemented several MIMO technologies to enhance its data through put, channel capacity and overall performance. The techniques employed by Wi-Fi are mainly antenna selection, STC and beam forming. The following table (Table 3) provides a summary for the different MIMO technologies used in those communication schemes and their performance (data rate).Table 3. Summary of MIMO technologies in modern communication systems and their overall performances.VII. CONCLUSIONIn conclusion, the historical developments, classification and current applications associated with MIMO technologies have been outlined and reviewed in this report. It can be seen that MIMO has a great deal of advantages over other traditional communication technologies. MIMO can also be used in conjunction with other existing techniques including digital modulation (OFDM in particular), coding (STC, DPC and etc) and multiple access (TDMA and FDMA) in order to derive more powerful and efficient communication schemes and provide users with better communication quality. Although there still exits some compelling problems regarding the wide application of MIMO, one can see that such technology will be more extensively integrated in our future generation wireless communication systems.REFERENCE[1] Raut, Pravin W., and S. L. Badjate. "MIMO-Future Wireless Communication."[2] Sibille, Alain, Claude Oestges, and Alberto Zanella. MIMO: from theory to implementation. Academic Press, 2010.[3] Clerckx, Bruno, and Claude Oestges. MIMO Wireless Networks: Channels, Techniques and Standards for Multi-antenna, Multi-user and Multi-cell Systems. Academic Press, 2013.[4] Holter, Bengt. "On the capacity of the MIMO channel: A tutorial introduction."Proc. IEEE Norwegian Symposium on Signal Processing. 2001.[5] Liang, Yang Wen. "Ergodic and Outage Capacity of Narrowband MIMO Gaussian Channels." Dept. of Electrical and computer Engg. University of British Columbia, V ancouver, British Columbia (2005).[6] Telatar, Emre. "Capacity of Multi‐antenna Gaussian Channels." European transactions on telecommunications 10.6 (1999): 585-595.[7] Bauch, Gerhard, and Guido Dietl. "Multi-user MIMO for achieving IMT-Advanced requirements." Telecommunications, 2008. ICT 2008. International Conference on. IEEE, 2008.[8] Li, Qinghua, et al. "MIMO techniques in WiMAX andLTE: a feature overview."Communications Magazine, IEEE 48.5 (2010): 86-92.。

专题：移动通信（5G ）测试面向汽车的MIMO OTA 测试技术李雷1，魏贵明1，姜国凯2，冯家煦2，吴翔1（1.中国信息通信研究院，北京 100191；2.中国汽车技术研究中心有限公司，天津 300300）摘 要：汽车智能网联是当前交通产业发展的主要方向之一。

通过汽车与周围车辆、行人、交通设施、蜂窝网络进行信息交互，提升交通网决策管理智能化水平，改善道路安全与效率。

如何定量精确地评估整车通信性能是业界关注的热点问题，然而目前尚缺少成熟的解决方案，特别是面向整车产品。

主要研究了基于多探头吸波暗室（multi-probe anechoic chamber ，MPAC ）的汽车空口测量系统的搭建方法，并针对整车空口测试提出了一种低成本解决方案，可以在信道模拟器数字通路受限的情况下，通过数字变换，成倍拓展测试区域。

数字仿真结果表明，基于此方案所构造的测试区域，其空间相关性、时间相关性均满足相关标准要求。

关键词：汽车空口测试；多探头吸波暗室；空间相关性；时间相关性 中图分类号：TN929.5 文献标识码：Adoi : 10.11959/j.issn.1000−0801.2021040MIMO OTA performance testing of vehiclesLI Lei 1, WEI Guiming 1, JIANG Guokai 2, FENG Jiaxu 2, WU Xiang 11. China Academy of Information and Communications Technology, Beijing 100191, China2. China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, ChinaAbstract: Intelligent traffic system is one of promising technologies for traffic industry. Based on communication among cars, pedestrian, traffic infrastructure and cellular networks, it’s able to significantly promote on road safety and efficiency by making proper traffic instructions. How to precisely evaluate the communication performance of a whole vehicle become a hot-topic in the field. Unfortunately, there is still no solution about the setup and procedures for the OTA testing. A low-cost solution was proposed by reconstructing channel models and optimizing the existing testing system framework that referred to as multi-probe anechoic chamber. The proposed method was able to mul-tiple the test area with limited channel emulator RF channels by introducing a low-cost digital converter. Numerical simulations indicate that the proposed method has the ability to reproduce spatial and temporal correlation features of the target channel model, which is coherent with related standards’ requirements.Key words: OTA testing for vehicles, multi-probe anechoic chamber, spatial correlation, temporal correlation1 引言多输入多输出（multi-input multi-output ，MIMO ）通过在收发两端配置多路天线，基于合适的空时编码技术，可以在相同的时频资源上传输多流数据，从而成倍地提升系统的频谱效率，收稿日期：2020−07−29；修回日期：2021−02−18 通信作者：魏贵明，*******************.cn·33·电信科学 2021年第2期是长期演进（long term evolution，LTE）和新空口（new radio，NR）接入网络的核心物理层技术之一。

Design of High-Diversity Gain MIMO Antenna Arrays through Surface Current OptimizationSebastien Clauzier, Said M. Mikki and Yahia M. M. AntarElectrical and Computer EngineeringRoyal Military College of CanadaStation Forces, Kingston, ON K7K 7B4, CanadaE-mail: sebastien.clauzier@rmc.ca; said.mikki@rmc.ca; yahia-y@rmc.caAbstract—In this paper, we present a general method forobtaining MIMO antenna current distribution with optimumcross-correlation diversity gain. The method is illustrated with anexample of 3-element arrays of wire antenna operating at 60GHz.I.I NTRODUCTIONThe recent demands for the development of the 5G wirelesssystem requires, among other things, new design methods toimprove MIMO systems performances [1]. For MIMOsystems, the characterization of these performances goesthrough several parameters [2], [3], [4], for example thechannel capacity, the TARC (Total Active ReflectionCoefficient) and the cross-correlation coefficient. In this paper,we use a new expression of the cross-correlation coefficient [1]to propose a method to optimize the MIMO performancethrough searching for proper surface current.The basic idea in [1] is to transfer the problem ofcomputing cross correlation from the far zone to the radiatingcurrents themselves via the cross-correlation Green’s function.Using this quantity, it is possible to directly manipulate currentelements on the MIMO antennas in order to improve thecorrelation performance at the far zone. To illustrate this newmethodology, we consider an array of three wire antennas at60GHz. We show that a direct surface current optimization canbe applied to minimize the cross-correlation coefficient.II.T HEORYA.The new expression of the cross-correlation coefficientThe original definition of this coefficient uses the far-fieldradiated by antennas [2] is:ρ=|∫dΩE1(θ,φ).E∗(θ,φ)4π|√∫dΩ|E1(θ,φ)|24π√∫dΩ|E2(θ,φ)|24π(1)This expression gives an exact value of the cross-correlation coefficient but due to the necessity of a complete 3D measurement of the radiation pattern, it is computationally difficult to evaluate. An alternative method was proposed in[1]. If we consider two antennas (antenna 1 and antenna 2) ofa receiving MIMO system, we can directly express the cross-correlation coefficient in terms of the current expression J1(r) and J2(r). Indeed, the numerator of (1) can be replaced by [1]:∫dΩE1(r̂).E2∗(r̂)4π=∫d3r′∫d3r′′V2V1J1(r′).C̅(r′,r′′). J2*(r′′),(2) while the denominator of (1) involves∫dΩ|E1(r̂)|24π=∫d3r′∫d3r′′V2V1J1(r′).C̅(r′,r′′). J1*(r′′) (3)∫dΩ|E2(r̂)|24π=∫d3r′∫d3r′′V2V1J2(r′).C̅(r′,r′′). J2*(r′′)(4)Here, C̅(r′,r′′) is called the cross-correlation Green’s function and is defined as [1]C̅(r′,r′′)=∫dΩ[I−r̂r]4πe ik(r′−r′′).r̂(5)where r′and r′′ vary on the surfaces of the two antennas, r̂(θ,φ)=x̂sinθcosφ+ŷsinθsinφ+ẑcosθ is the unit vector. I is the unit dyad and k is the wave number. The derivation of the expressions above is straightforward but lengthy and so will not be presented here. Details and verifications can be found in [1]. The results presented below were verified by comparison with a full-wave simulation conducted using CST.Equations (2) to (4) allow us to determine the cross-correlation coefficient in terms of the current distribution on both antennas. As a result, a current optimization scheme can be used to minimize this cross-correlation coefficient. In this paper, we will consider a continuous current optimization for an array of 3 wire antennas.B.Description of the optimization processConsider a case with two wire antennas, a schematic representation of the process is given in Fig. 1. Each dipole is segmented by an array of infinitesimal dipoles (in our case 5 infinitesimal dipoles). This segmentation allows us to model a continuous current distribution supported by each wire through a discrete approximation comprised of few infinitesimal dipoles. In general, arbitrary continuous currents can always be approximated with sufficiently large number of point sources. In this paper, we choose small number of infinitesimal dipoles to reduce the optimization cost.9978-1-4799-7815-1/15/$31.00 ©2015 IEEE AP-S 2015Fig. 1. Schematic representation of the segmentation of thewire antennas by an array of infinitesimal dipoles. Each infinitesimal dipole will be associated with a complex moment corresponding to the amplitude and the phase of the continuous current on the wire. Then, an optimization program (genetic algorithm) will update these two parameters on each dipole in order to minimize the cross-correlation coefficient calculated with the formula belowρIJ =∑∑ρInJm Nn=1M m=1(6)where M and N are, respectively, the number of infinitesimal dipoles considered for the dipole I and the dipole J (Here chosen both to be 5). ρInJm is the cross-correlation between the n tℎ infinitesimal dipole of the wire I and the m tℎ infinitesimal dipole of the wire J.III. E XAMPLE OF AN ARRAY OF 3 WIRE ANTENNAS AT60GH Z We apply the design process described above to an array of three half-wave dipole antennas at 60GHz (Figure 2). The genetic algorithm is used to optimize the amplitude and the phase of each infinitesimal dipole (segment on the three wire antennas).Fig. 3 shows the variation of the cross-correlation during the optimization process. We notice that after 100 iterations, we have reached a cross-correlation coefficient equal to 0.35. Fig. 4 shows the optimized amplitude and phase of the current on each dipole. If we compare the diversity gains, calculated through the formulation exposed in [1], before and after the optimization, we obtain with a non-optimized current a diversity gain of 0.41, while after optimization with the current represented in Fig. 4, a diversity gain of 0.8 is achieved. Therefore, proper choice of the current allows us to multiply by almost 2 the diversity gain.The numerical examples tried by the authors suggest that phase variations on radiating currents are more important for diversity gain performance than the amplitude distribution. Therefore, linear wires with special currents can significantly boast up the performance of MIMO systems. Realizations of these currents can be attained by engineering the surface impedance boundary condition on specially coated wires or by direct implementation of arrays of electrically-small antennas to mimic the continuous current obtained by our method.Fig. 2. Schematic representation of an array of three wireantennas.Fig. 3. Schematic representation of an array of three wireantennas.(a) (b)Fig. 4. Optimized amplitude (a) and phase (b) distribution onthe 3 half-wave dipoles.IV. C ONCLUSIONIn this paper, it was shown that by careful manipulation of the amplitude and phase of the radiating current, it is possible to minimize the cross-correlation coefficient (and therefore maximizing the diversity gain) in arbitrary MIMO system antenna arrays. The method was outlined and applied successfully to three half-wavelength dipole antennas.R EFERENCES[1] S. Mikki and Y. Antar, ‘On cross correlation in antenna arrays withapplications to spatial diversity and MIMO systems,’ submitted to IEEE Transactions on Antennas and Propagation,, 2014.[2] M.S. Sharawi, ‘Printed MIMO antenna system: Performance Metrics,Implementations and Challenges,’ Forum for Electromagnetic Research Methods and Application Technologies (FERMAT), 2014.[3] W. C. Jakes et al, Microwave Mobile Communications , John Wiley &Sons, 1974.[4] Nelson Costa and Simon Haykin, Multiple-Input Multiple OutputChannel Models , John Wiley & Sons, 2010.10。

文章编号:1001-2486(2006)06-0063-05MIMO 信道的三维互相关模型及其相关特性分析Ξ高　凯,张尔扬(国防科技大学电子科学与工程学院,湖南长沙　410073)摘　要:针对频率选择性衰落MI MO 移动信道,建立三维(3D )纯随机模型,并推导出3D MIM O 信道模型的联合空时频相关函数。

新的3D 模型统一了现有的多种信道模型,新的联合空时相关函数综合考虑了信道收发两端的多普勒扩展、信号到达方向与离开方向的3D 非均匀扩展与两端天线阵的配置。

最后基于新的相关函数,分析了非均匀角度扩展参数以及阵元配置参数的变化对MIM O 信道相关特性的影响。

关键词:MI MO 信道;三维模型;空时频相关函数中图分类号:T N91112 文献标识码:AA 3D Cr oss 2cor relation Model for MIMO Fading Channel an d ItsCr oss 2cor relation Perfor mance A nalysisG A O K ai ,ZH A NG Er 2yang(C ollege of Electronic Science and Engineering ,National Univ.of De fens e T echnology ,C hangsha 410073,China)Abstract :A 3D statis tical channels m odel for m ob ile frequen cy selective fading MIM O channels w as prop osed and a new joint space 2time 2frequency correlation function w as derived.T he 3D channels model in tegrates many kn own channel m odels and the new space 2time 2frequency correlation function tak es into account such parameters o f MIMO sys tem as the D oppler s pread ,the 3D ang le spread ,and antenna array con figuration.Furth erm ore ,the impact of n onuni form angle s pread and an tenna array con figuration on the Cross 2correlation Performance o f channels w ere analy zed bas ed on the new correlation function.K ey w or ds :MI MO channel ;32D model ;space 2time 2frequency correlation function近年来,MI MO 系统得到了广泛的关注。

通信网络技术无线通信工程中的MIMO系统应用与性能分析马远航（日海恒联通信技术有限公司，河南郑州文章深入分析多输入多输出（Multiple InputMultiple Output，MIMO）系统在无线通信工程中的应用及其性能，重点探讨其关键技术和应用场景。

MIMO系统通过空间复用和阵列增益提升通信系统的容量和可靠性，尤其在空间复用方面，通过向量偏转传输技术实现在同一时频资源上传输多个独立数据流，从而大幅提高频谱效率。

此MIMO系统可靠性和抗衰落能力上的重要作用，分析了基于最小均方误差（Minimum Mean Square Error，MMSE）算法的信道估计与均衡技术在保证系统性能上的关键应用。

仿真结果显示，系统在信噪比较高时实现了显著的吞吐量提升，验证了其在无线通信领域的优越性。

多输入多输出（MIMO）系统；空间复用；信道编码；信道估计；无线通信Application and Pperformance Analysis of MIMO System in Wireless CommunicationEngineeringMA Yuanhang(Rihai Henglian Communication Technology Co., Ltd., Zhengzhou维度资源，扩大了通信系统容量，提升了通信系统可靠性，成为现代无线通信技术进步的重要支撑力之一。

系统关键技术分析实验室提出的向量偏转传输技术，系统的空间复用，从而获得多径增益[2]。

个天线看作一个发射向量空间，个天线看作一个接收向量空间，通过个正交基矢量，并根据信的奇异值进行分解，得到发射端和接。

经过预编码矩阵V变换个正交的个不同的数据流且不发生的严格要求。

2.3 信道估计与均衡为跟踪间的快速时变信道，需要进行准确可靠的信道估计。

本设计采用基于训练序列的据传输之前，发送已知的训练序列，接收端获得经信道冲激响应的序列。

接收序列为式中：N为提高估计准确性，训练序列之间采用循环移位设计，接收端收集多个传输块的训练序列进行联合信道估计。