# Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents

arXiv: https://arxiv.org/abs/1812.09985 · HTML: https://mlsanity.com/paper/1812.09985/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2018.

## Built on by (most cited first, 10 of 10)

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- [Active noise control techniques for nonlinear systems](https://mlsanity.com/paper/2110.09672/) (2021)
- [Study of Frequency domain exponential functional link network filters](https://mlsanity.com/paper/2201.05501/) (2022)
- [Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate](https://mlsanity.com/paper/2203.10205/) (2022)
- [Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks](https://mlsanity.com/paper/2206.12749/) (2022)
- [Study of General Robust Subband Adaptive Filtering](https://mlsanity.com/paper/2208.08856/) (2022)
- [Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming](https://mlsanity.com/paper/2510.17823/) (2025)

## Abstract (arXiv)

This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. This algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate estimate update at each node. With the help of side information, the constraint is recursively updated in a diffusion strategy. Moreover, a control strategy for resetting the constraint is also proposed to retain good tracking capability when the estimated parameters suddenly change. Simulations show the superiority of the proposed algorithm over previously reported techniques in various impulsive noise scenarios.