Design and implementation of flexible 4M×4N MIMO channel emulator

Author(s):  
Nianzu Zhang ◽  
Guangqi Yang ◽  
Jianfeng Zhai ◽  
Wei Hong
2020 ◽  
Vol 10 (12) ◽  
pp. 4161
Author(s):  
Qiuming Zhu ◽  
Wei Huang ◽  
Kai Mao ◽  
Weizhi Zhong ◽  
Boyu Hua ◽  
...  

In this paper, a discrete non-stationary multiple-input multiple-output (MIMO) channel model suitable for the fixed-point realization on the field-programmable gate array (FPGA) hardware platform is proposed. On this basis, we develop a flexible hardware architecture with configurable channel parameters and implement it on a non-stationary MIMO channel emulator in a single FPGA chip. In addition, an improved non-stationary channel emulation method is employed to guarantee accurate channel fading and phase, and the schemes of other key modules are also illustrated and implemented in a single FPGA chip. Hardware tests demonstrate that the output statistical properties of proposed channel emulator, i.e., the probability density function (PDF), cross-correlation function (CCF), Doppler power spectrum density (DPSD), and the power delay profile (PDP) agree well with the corresponding theoretical ones.


Author(s):  
Tran Thi Thao Nguyen ◽  
Leonardo Lanante ◽  
Yuhei Nagao ◽  
Masayuki Kurosaki ◽  
Hiroshi Ochi

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
Ping-Heng Kuo ◽  
Pang-An Ting

Geometric mean decomposition (GMD) has been proposed as a method to realize multiple spatial links with identical gains that are intrinsic to a MIMO channel. In order to simplify system design and implementation based on knowledge regarding probability behavior of MIMO-GMD schemes, the main objective of this paper is to statistically characterize the link gains and channel capacities that can be provided via GMD. In particular, closed-form univariate and bivariate probability density functions (PDFs) for these metrics under Rayleigh fading are derived using Gamma approximations. By applying these analytical results, the fluctuations of MIMO-GMD schemes are examined by modeling both link gains and capacities using finite-state Markov chains (FSMCs).


Author(s):  
Tran Thi Thao NGUYEN ◽  
Leonardo LANANTE ◽  
Yuhei NAGAO ◽  
Hiroshi OCHI

Author(s):  
Andreas Schwind ◽  
Philipp Berlt ◽  
Mario Lorenz ◽  
Christian Schneider ◽  
Matthias A. Hein

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