Deep Neural Network-Driven Adaptive Filtering

2025-08-06

AI Summary

Addressing the long-standing challenge of poor generalization of adaptive filtering (AF) under non-Gaussian noise, this paper proposes a novel deep neural network (DNN)-driven AF framework. Methodologically, the DNN is formulated as a universal nonlinear operator that directly maps the residual error to the learning gradient—bypassing explicit cost function design—and implicitly optimizes maximum likelihood estimation for data-driven gradient generation. The framework tightly integrates DNNs with classical filter architectures through implicit cost modeling and direct gradient projection. We rigorously establish mean and mean-square stability of the algorithm. Experimental results demonstrate that the proposed method significantly improves convergence speed, steady-state accuracy, and robustness across diverse non-Gaussian environments, outperforming state-of-the-art AF algorithms in generalization capability.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Non-convex Optimization
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper proposes a deep neural network (DNN)-driven framework to address the longstanding generalization challenge in adaptive filtering (AF). In contrast to traditional AF frameworks that emphasize explicit cost function design, the proposed framework shifts the paradigm toward direct gradient acquisition. The DNN, functioning as a universal nonlinear operator, is structurally embedded into the core architecture of the AF system, establishing a direct mapping between filtering residuals and learning gradients. The maximum likelihood is adopted as the implicit cost function, rendering the derived algorithm inherently data-driven and thus endowed with exemplary generalization capability, which is validated by extensive numerical experiments across a spectrum of non-Gaussian scenarios. Corresponding mean value and mean square stability analyses are also conducted in detail.

Problem

• Address generalization challenge in adaptive filtering
• Shift paradigm to direct gradient acquisition
• Enhance data-driven filtering in non-Gaussian scenarios

Innovation

• DNN-driven framework for adaptive filtering
• Direct gradient acquisition replaces cost design
• Maximum likelihood as implicit cost function