Sparse channel estimation using adaptive filtering and compressed sampling

Khalifa, Mohammed Osman; Abdelhafiz, Abubaker; Zerguine, Azzedine

doi:10.1109/icceee.2013.6633922

Cited by 7 publications

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References 6 publications

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“…Usually, this can be implemented using adaptive channel estimation (ACE) or adaptive filter algorithms which has been extensively studied in the literature such as the LMS algorithm [3]- [4], the NLMS algorithm [5], and the LMF algorithm [6]. However, most of the classical techniques ignored the fact that most channels in real life are sparse in nature, which means that most of the channel taps are zeros or almost zeros, while a few number of the channel taps are non-zeros [7]. Fig.1 depicts a typical sparse channel.…”

Section: Introductionmentioning

confidence: 99%

Sparse Channel Estimation with Gradient-Based Algorithms: A Comparative Study

EI-Moaty

Zerguine

2018

2018 15th International Multi-Conference on Systems, Signals &Amp; Devices (SSD)

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Channel state information (CSI) is very crucial for any wireless communication systems. Typically, CSI can be characterized at the receiver side using channel impulse response (CIR). Many observations have shown that the CIR of broadband multi path wireless channels are often sparse. To this point, the family of least mean square (LMS)-based algorithms have been widely used to estimate the CIR, unfortunately the performance of LMS family is not much accurate in terms of sparse channel estimation. The Least Mean Mixed Norm (LMMN) algorithm combines the advantages of both the Least Mean square (LMS) and the Least Mean Fourth (LMF)algorithm, which makes this algorithm stands in a very special position among the family members in terms of convergence and steady state error. In this paper, we held a fair comparative study between the LMMN and a number of the LMS-based algorithms, such as the LMS algorithm, the zero-attracting (ZA-LMS) algorithm, and the normalized (NLMS) algorithm. Simulation results are carried out to compare the performance of all these algorithms with the LMMN algorithm. The results show that the LMMN algorithm outperforms the rest of these algorithms in the identification of sparse systems in terms of both fast convergence and the steady state error.

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