Mobile Communications Engineering
Mobile Communications Engineering
Random Iterative Models
Convergence and steady-state analysis of the normalized least mean fourth algorithm
Digital Signal Processing
An improved statistical analysis of the least mean fourth (LMF) adaptive algorithm
IEEE Transactions on Signal Processing
The least mean fourth (LMF) adaptive algorithm and its family
IEEE Transactions on Information Theory
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data
IEEE Transactions on Information Theory
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The learning speed of an adaptive algorithm can be improved by properly constraining the cost function of the adaptive algorithm. In this work, a noise-constrained least mean fourth (NCLMF) adaptive algorithm is proposed. The NCLMF algorithm is obtained by constraining the cost function of the standard LMF algorithm to the fourth-order moment of the additive noise. The NCLMF algorithm can be seen as a variable step-size LMF algorithm. The main aim of this work is to derive the NCLMF adaptive algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF algorithm is carried out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF algorithm.