A modified IPNLMS algorithm using system sparseness

Hirano, Genki; Shimamura, Tetsuya

doi:10.1109/ispacs.2012.6473615

Cited by 9 publications

(1 citation statement)

References 12 publications

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“…Similarly, β should take a relatively small value to obtain a faster convergence speed when the sparsity of the SI channel is small. According to the relevant research on the influence of the sparsity of the SI channel on the performance of IPNLMS algorithm in [29,30], we construct the adaptive control parameter β(k) with the sparsity measurement valuesψ(w(k)) to approximate the curve of the relationship, which is expressed as:…”

Section: Improved Arc-ipnsaf Algorithmmentioning

confidence: 99%

Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Zhang

Shi

et al. 2021

Electronics

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Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

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Section: Improved Arc-ipnsaf Algorithmmentioning

confidence: 99%