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Tài liệu tham khảo chuyên ngành viễn thông Simulation of a Multiple Input Multiple Output (MIMO) wireless system
DUBLIN CITY UNIVERSITY SCHOOL OF ELECTRONIC ENGINEERING Simulation of a Multiple Input Multiple Output (MIMO) wireless system John Fitzpatrick TC4 52140938 April 2004 B.Eng IN Telecommunications Engineering Supervised by Dr Conor Brennan Simulation of a MIMO wireless system – John Fitzpatrick Acknowledgements I would like to thank my supervisor Dr Conor Brennan for his guidance, assistance and approachability throughout this project I would also like to thank John Diskin for his work on the ray tracing program Finally I would like to thank my parents and Laura for their support throughout my project Declaration I hereby declare that, except where otherwise indicated, this document is entirely my own work and has not been submitted in whole or in part to any other university Signed: ii Date: Simulation of a MIMO wireless system – John Fitzpatrick Abstract This project explores the development of a multiple input multiple output (MIMO) simulator using ray tracing techniques This project gives an overview of ray tracing techniques, beamforming, MIMO channel models and MIMO systems It explains the ability of MIMO systems to offer significant capacity increases over traditional wireless systems, by exploiting the phenomenon of multipath By modelling high frequency radio waves as travelling along localized linear trajectory paths, they can be approximated as rays, just as in optics The radio environment is then represented using a ray tracing C++ program I highlight some of the different approaches used to realize a MIMO system, the most important being the Singular Value Decomposition (SVD) I illustrate the development of the MIMO simulator, through explanations of the techniques and algorithms I developed and used These algorithms model the system under ideal conditions with no noise distortions I show the use of the MIMO simulator created, and investigate the MIMO channel The results obtained show the affects of changing the different parameters of the system on the MIMO channel and the radio environment Finally, in the conclusion, I discuss the future of MIMO systems and recommend further modifications, which could be made to the MIMO simulator, to create a more accurate and efficient system iii Simulation of a MIMO wireless system – John Fitzpatrick Table Of Contents CHAPTER - INTRODUCTION CHAPTER - TECHNICAL BACKGROUND 2.1 MULTIPATH 2.2 RAY TRACING 2.3 BEAMFORMING 2.4 LINEAR ARRAYS 2.5 MIMO 2.5.1 MIMO Transmission 2.5.2 The MIMO Channel H 2.6 GAUSSIAN ELIMINATION 10 2.7 SINGULAR VALUE DECOMPOSITION (SVD) 12 CHAPTER – IMPLEMENTATION OF RAY TRACING 13 3.1 RAY TRACING 14 3.1.2 The ray tracing program 14 3.2 CONVERGENCE OF ORDER 26 CHAPTER - IMPLEMENTATION OF MIMO SIMULATOR 30 4.1 GAUSSIAN ELIMINATION 30 4.2 SVD 33 4.2.1 Operation of the SVD algorithm 33 4.2.2 Matlab SVD 35 4.3 FURTHER MODIFICATIONS TO THE RAY TRACING PROGRAM 39 4.4 PLOTTING THE RESULTS 40 4.5 THE MIMO SIMLATOR 41 4.5.1 MIMO simulator users guide 43 CHAPTER – RESULTS 46 5.1 SVD IN FREESPACE 46 5.2 NUMBER OF ELEMENTS IN AN ARRAY 49 iv Simulation of a MIMO wireless system – John Fitzpatrick 5.3 DIELECTRIC PARAMETERS AND CORRIDOR MODEL 51 CHAPTER - CONCLUSIONS AND FURTHER RESEARCH 55 Matlab code for Beamforming 58 C++ Gaussian Elimination Code 60 Matlab Singular Value Decomposition (SVD) Code 64 Matlab ‘mimo’ Code 66 v Simulation of a MIMO wireless system – John Fitzpatrick Table of Figures FIGURE 2-1 MULTIPATH ENVIRONMENT FIGURE 2-2 SIMO SYSTEM FIGURE 2-3 LINEAR BEAMFORMING ARRAY FIGURE 2-4 BEAMFORMING FIGURE 2-5 THREE ELEMENT MIMO SYSTEM FIGURE 2-6 DATA TRANSMISSION IN MIMO SYSTEMS FIGURE 3-1 BUILDING STRUCTURE 15 FIGURE 3-2 OBLONG (WALL) 16 FIGURE 3-3 FACE 17 FIGURE 3-4 RAY NODES 19 FIGURE 3-5 DIRECT RAY 20 FIGURE 3-6 FIRST ORDER IMAGE 21 FIGURE 3-7 FINDING REFLECTION POINTS 22 FIGURE 3-8 FINDING THE REFLECTION POINT 25 FIGURE 3-9 SAMPLE POINTS FOR CONVERGENCE 27 FIGURE 3-10 CONVERGENCE GRAPH, BLUE =1ST, RED =2ND, GREEN 3RD ORDER 27 FIGURE 3-11 2D PLOT OF 4TH ORDER ROOM WITH WALLS 28 FIGURE 3-12 3D PLOT OF 4TH ORDER ROOM WITH WALLS 29 FIGURE 4-1 SCREENSHOT OF GAUSSIAN ELIMINATION PROGRAM 32 FIGURE 4-2 SCREENSHOT OF C++ SVD PROGRAM 34 FIGURE 4-3 SCREENSHOT OF RAY TRACING PROGRAM 43 FIGURE 4-4 SCREENSHOT “PLEASE ENTER ORDER” 43 FIGURE 4-5 SCREENSHOT “PLEASE RUN ‘MYSVD’ ” 44 FIGURE 4-6 SCREENSHOT “PLEASE RUN ‘MIMO’“ 44 FIGURE 4-7 RESULT OF RAY TRACING PROGRAM, TX ANTENNA IN FREESPACE 45 FIGURE 4-8 RESULT OF RAY TRACING PROGRAM, RX ANTENNA IN FREESPACE 45 FIGURE 5-1 TX FREESPACE ANTENNA GAIN PLOT 46 FIGURE 5-2 RX FREESPACE ANTENNA GAIN PLOT 47 FIGURE 5-3 TX FREESPACE ANTENNA GAIN PLOT WITH ANTENNA SHIFTED UP 48 FIGURE 5-4 RX FREESPACE ANTENNA GAIN PLOT WITH ANTENNA SHIFTED UP 48 FIGURE 5-5 ELEMENT ANTENNA ARRAY 49 FIGURE 5-6 ELEMENT ANTENNA ARRAY 50 FIGURE 5-7 ELEMENT ANTENNA ARRAY 50 FIGURE 5-8 TX CORRIDOR MODEL 52 FIGURE 5-9 RX CORRIDOR MODEL 52 vi Simulation of a MIMO wireless system – John Fitzpatrick FIGURE 5-10 TX CORRIDOR MODEL, INCREASED DIELECTRIC PARAMETERS 53 FIGURE 5-11 RX CORRIDOR MODEL, INCREASED DIELECTRIC PARAMETERS 54 vii Simulation of a MIMO wireless system – John Fitzpatrick Chapter - Introduction In the modern era of communications, the ability to send large volumes of data is crucial With the increasing use of wireless LAN technology and third generation mobile telephony systems, the demand for data services has never been greater The bandwidth of wireless communication systems is often limited by the cost of the radio spectrum required Any increase in bit rate, which can be realised without increasing the bandwidth, makes the system more spectrally efficient and less costly Traditional wireless communication systems have been made more spectrally efficient through the use of clever coding techniques and algorithms However, the fundamental bandwidth limitation does not change Multiple Input Multiple Output (MIMO) communication systems have been an increasingly hot topic of research over the past eight years, due to their ability to greatly increase spectral efficiencies As opposed to traditional wireless systems, in which there is one transmitting and one receiving antenna, MIMO systems use arrays of multiple antennas at both ends of the communication link, all operating at the same frequency at the same time This introduces spatial diversity into the system, which can be used to tackle the problem of multipath In wireless communications system, such as point to point radio links, radio waves not simply propagate from the transmit antenna to the receive antenna Rather they bounce and scatter off objects, this effect is known as multipath This effect is regarded as an impediment to the accurate transmission of data in traditional wireless links MIMO systems exploit multipath by using the rich scattering environment to increase the spectral efficiency of the wireless system The modelling of radio waves on a large scale can be very complex There is however, a simplification At high frequencies radio waves can be approximated as travelling along localized paths This is similar to the geometrical treatment of light rays in optics Using ray tracing methods, complex radio environments can be modelled The use of numerical techniques is crucial to the operation of MIMO systems Algorithms and signal processing at both ends of a MIMO wireless link are crucial to encode and Simulation of a MIMO wireless system – John Fitzpatrick decode the data The most important numerical method in MIMO systems is Singular Value Decomposition (SVD) This allows the complex path, which exists between transmitter and receiver to be analysed and simplified By combining the above techniques it was the aim of this project to develop a fully operational MIMO simulator The simulator needed to model indoor radio environments and be easy to use Chapter - Technical Background Simulation of a MIMO wireless system – John Fitzpatrick In wireless communications system, such as point to point radio links, radio waves not simply propagate from the transmit antenna to the receive antenna Rather they bounce and scatter off objects This effect is known as multipath When the radio waves strike an object in the environment, they scatter randomly as can be seen in figure 2.1 This is also known as independent Rayleigh scattering The red line shows the direct propagation path, whereas the many blue lines show the multiple propagation paths produced by multipath Figure 2-1 MultiPath Environment 2.1 Multipath Multipath results in multiple copies of the same transmitted signal arriving at the receiver, at different times As they arrive at different times they have varying phase delays, which can result in scattered signals combining destructively at the receiver producing destructive interference and fading To carry out any simulation, the multipath environment needs to be modelled This is done using ray tracing 2.2 Ray tracing The radio environment was modelled using ray tracing Ray tracing was initially developed in the field of computer graphics to produce photorealistic computer generated images Ray tracing operates by calculating the path taken by a ray of light from a light source to the point of interest At frequencies greater than approximately 900MHz, radio waves can be described as travelling along localized ray paths (i.e approximately a straight line) The ... antennas used in a linear array Simulation of a MIMO wireless system – John Fitzpatrick 2.4 Linear arrays Beamforming can be accomplished by using many different types of arrays, such as linear,... point of interest At frequencies greater than approximately 900MHz, radio waves can be described as travelling along localized ray paths (i.e approximately a straight line) The Simulation of a MIMO... sonar systems Using multiple antennas introduces spatial diversity into the system These antennas are also known as ‘smart antennas’ Spatial diversity is based upon the fact that two signals detached