Non-stationary Sparse Fading Channel Estimation for Next Generation Mobile Systems

• Saadat Dehgan,
• Changiz Ghobadi,
• Javad Nourinia,
• Jie Yang,
• Guan Gui,
• Ehsan Mostafapour,

Vol. 12, No. 3, March 30, 2018
10.3837/tiis.2018.03.004, Download Paper (Free):

• channel estimation
• Sparse
• Non-Stationary
• Adaptive algorithms
• Variable threshold.

Abstract

In this paper the problem of massive multiple input multiple output (MIMO) channel estimation with sparsity aware adaptive algorithms for 5th 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.

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