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Random Fourier Feature Based Deep Learning for Wireless Communications
Deep-learning (DL) has emerged as a powerful machine-learning technique for several classic problems encountered in generic wireless communications.
Finite Dictionary Variants of the Diffusion KLMS Algorithm
This paper formulates the diffusion KLMS in a fixed budget setting such that the storage requirement is curtailed while maintaining appreciable performance in terms of convergence.
Diffusion-KLMS Algorithm and its Performance Analysis for Non-Linear Distributed Networks
It has also been observed that, the diffusion-LMS generally outperforms other distributed LMS algorithms like spatial LMS and incremental LMS.
A Lower Bound for the Variance of Estimators for Nakagami m Distribution
In this paper, we derive a theoretical expression for the variance of our proposed estimator.
Contraction Principle based Robust Iterative Algorithms for Machine Learning
Our contribution in this paper is an improvement upon this iterative algorithms in terms of their respective performance metrics and robustness.
