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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.
Dadfarnia, S.,Haji Shabani, A.M.,Dehgan Shirie, H. Korean Chemical Society 2002 Bulletin of the Korean Chemical Society Vol.23 No.4
A simple and rapid technique for the separation and preconcentration of lead in water and biological samples has been devised. Preconcentrationis based on the depositionof analyte onto a column packed with dithizone immobilized on sodium dodecyl sulfate coated alumina at pH $\geq$ 3. The trapped lead is eluted with 5 mL of 4 M nitric acid and determined by flame atomic absorption spectroscopy. A sample of 1 L, results in a preconcentration factor of 200 and the precision at 20${\mu}g$ $L^{-1}$ is 1.3%(n=8). The procedure is applied to tap water, well water, river water, vegetable extract and milk samples, and accuracy is assessed through recovery experiments and by independent analysis by furnace atomic absorption.