Achieving Robust Generalization for Wireless Channel Estimation Neural Networks by Designed Training Data

TL;DR

This paper introduces a training data design method that enhances the robustness and generalization of neural networks for wireless channel estimation, reducing the need for online retraining on unseen channels.

Contribution

The paper presents a novel data design approach that improves neural network generalization for wireless channels without online training, validated through simulations with attention-based and CNN models.

Findings

Neural networks maintain performance on unseen channels.

Designed training data enhances generalization without online retraining.

Simulation confirms robustness across different channel models.

Abstract

In this paper, we propose a method to design the training data that can support robust generalization of trained neural networks to unseen channels. The proposed design that improves the generalization is described and analysed. It avoids the requirement of online training for previously unseen channels, as this is a memory and processing intensive solution, especially for battery powered mobile terminals. To prove the validity of the proposed method, we use the channels modelled by different standards and fading modelling for simulation. We also use an attention-based structure and a convolutional neural network to evaluate the generalization results achieved. Simulation results show that the trained neural networks maintain almost identical performance on the unseen channels.