Deep Neural Network-Driven Adaptive Filtering

TL;DR

This paper introduces a deep neural network-based adaptive filtering framework that directly learns gradients for improved generalization, validated through extensive experiments and stability analysis.

Contribution

It presents a novel DNN-driven adaptive filtering approach that replaces explicit cost functions with direct gradient learning, enhancing generalization.

Findings

Demonstrates superior generalization in non-Gaussian scenarios

Provides stability analysis for the proposed method

Validates effectiveness through extensive numerical experiments

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.