Achieving Robust Channel Estimation Neural Networks by Designed Training Data

TL;DR

This paper introduces a method for designing synthetic training data for neural networks to achieve robust wireless channel estimation without prior channel knowledge, ensuring good generalization to unseen channels.

Contribution

The paper proposes design criteria for synthetic training datasets that enable neural networks to generalize well across diverse wireless channels without online adaptation.

Findings

Neural networks trained with the proposed data design generalize effectively to unseen channels.

The approach is robust across different neural network architectures.

Simulations show consistent performance over fixed and variable channel conditions.

Abstract

Channel estimation is crucial in wireless communications. However, in many papers neural networks are frequently tested by training and testing on one example channel or similar channels. This is because data-driven methods often degrade on new data which they are not trained on, as they cannot extrapolate their training knowledge. This is despite the fact physical channels are often assumed to be time-variant. However, due to the low latency requirements and limited computing resources, neural networks may not have enough time and computing resources to execute online training to fine-tune the parameters. This motivates us to design offline-trained neural networks that can perform robustly over wireless channels, but without any actual channel information being known at design time. In this paper, we propose design criteria to generate synthetic training datasets for neural networks, which guarantee that after training the resulting networks achieve a certain mean squared error (MSE) on new and previously unseen channels. Therefore, trained neural networks require no prior channel information or parameters update for real-world implementations. Based on the proposed design criteria, we further propose a benchmark design which ensures intelligent operation for different channel profiles. To demonstrate general applicability, we use neural networks with different levels of complexity to show that the generalization achieved appears to be independent of neural network architecture. From simulations, neural networks achieve robust generalization to wireless channels with both fixed channel profiles and variable delay spreads.