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Research on Statistical Modeling of MIMO Wireless Channel

Author: ZhaoYang
Tutor: LiZhaoXun
School: PLA Information Engineering University
Course: Communication and Information System
Keywords: MIMO Channel Model Complex Spatial Correlation Time Slot Dynamic Modeling
CLC: TN919.3
Type: Master's thesis
Year: 2010
Downloads: 114
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Abstract

As one of the major framework in future wireless communication systems, multiple input multiple output (MIMO) technology digs up spatial resource of wireless channel and increase the spectrum usage ratio of wireless communication system through spatial diversity and multiplexing. However, the profits gained from MIMO are highly dependent on the channel characteristics. So in order to evaluate/verify, and thus improve the performance of various MIMO wireless algorithms and even a whole system during R&D phases, it is obliged to set up MIMO channel models coinciding with the target channel environments rather than carrying out awkward campaigns under real environments. In this thesis, in-depth analyses of wireless channel characteristics are performed; a constructive amendment and a further expansion are applied to the existing MIMO channel models thereafter to make them more accurate when simulating the real channel environments. The contents of this thesis are abstracted as follows:For a better understanding of the channel’s fading nature, a thorough analysis is carried out in terms of‘large-scale fading’and‘small-scale fading’, from origin to treatment. The large-scale fading includes path loss and shadow fading; the small-scale fading is the result of multi-path transmission and Doppler Effect. The causes, characteristics and controlling parameters of these fading effects are analyzed. Single fading effect, as well as complicated combined ones is simulated.In order to go deep into the MIMO channel modeling, the existing modeling methods are first put into categories. An extra emphasis is then given to the two‘statistical channel modeling’methods:‘geometrically-based’and‘correlation-based’methods, in description and simulations. A comparison is attached at the end, which indicates that the two methods cannot share advantages of each other. These two modeling methods serve as the basis for further research.With the purpose to share the advantages of‘geometrically-based’and‘correlation-based’methods, i.e. to be able to represent channel geometrical setup as well as configure channel spatial correlations, a constructive amendment is made to the traditional‘correlation-based’methods, and a‘complex-correlation-based MIMO channel model’is given. The model introduces the complex spatial correlation coefficients as a function of channel geometrical parameters. With this functional relationship, the complex spatial correlation coefficients are configured by channel geometrical parameters generated by the‘geometrically-based’method. The simulation illustrates that the complex spatial correlation coefficients are reproduced, which is determined by the channel geometrical parameters. The modeling architecture leaves an interface for a further dynamic modeling of spatial correlations.For the sake of covering the time-varying nature of channel due to consecutive motions of mobile station (MS), a dynamic channel modeling scheme is given, which takes into account the dynamic behavior of channel geometrical setup, Doppler Effect, spatial correlation and large-scale fading. The simulation results show that the position, velocity and antenna array orientation of MS, the normalized power, AoD/AoA, propagation delay, Doppler spectrum and complex spatial correlation coefficients per directional path, and the received signal power change with MS motions. An extension description of the model is included finally.

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