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Channel Fading Effect Analysis on Diffusion Cooperation Strategies over Adaptive Networks
( Jie Yang ),( Ehsan Mostafapour ),( Amir Aminfar ),( Jie Wang ),( Hao Huang ),( Afsaneh Akhbari ),( C. Ghobadi ),( Guan Gui ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.1
In this paper, we investigate the performance of the diffusion adaptation strategies for parameter estimation in wireless adaptive networks, where the nodes exchange information over noisy and fading wireless channels. This paper shows the differences between the effect of Rayleigh and Rician fading over wireless adaptive networks and proves that the Rician fading is a more practical model in such kinds of networks. Simulation results imply that the effect of Rayleigh fading is more degrading for the estimation process than Rician fading. Also, the simulation results show the performance of adapt then combine (ATC) diffusion algorithm is better than the combine then adapt (CTA) algorithm by merely considering noise in wireless channels. While the performance of CTA prevails ATC over the wireless adaptive network in the presence of noise plus channel fading.
Non-stationary Sparse Fading Channel Estimation for Next Generation Mobile Systems
( Saadat Dehgan ),( Changiz Ghobadi ),( Javad Nourinia ),( Jie Yang ),( Guan Gui ),( Ehsan Mostafapour ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.3
In this paper the problem of massive multiple input multiple output (MIMO) channel estimation with sparsity aware adaptive algorithms for 5<sup>th</sup> generation mobile systems is investigated. These channels are shown to be non-stationary along with being sparse. Non-stationarity is a feature that implies channel taps change with time. Up until now most of the adaptive algorithms that have been presented for channel estimation, have only considered sparsity and very few of them have been tested in non-stationary conditions. Therefore we investigate the performance of several newly proposed sparsity aware algorithms in these conditions and finally propose an enhanced version of RZA-LMS/F algorithm with variable threshold namely VT-RZA-LMS/F. The results show that this algorithm has better performance than all other algorithms for the next generation channel estimation problems, especially when the non-stationarity gets high. Overall, in this paper for the first time, we estimate a non-stationary Rayleigh fading channel with sparsity aware algorithms and show that by increasing non-stationarity, the estimation performance declines.