Learning Approximate Neural Estimators for Wireless Channel State Information

TL;DR

This paper investigates neural network-based estimators for wireless channel state information, demonstrating their potential to outperform traditional analytical methods in complex, real-world conditions.

Contribution

It introduces a data-driven neural estimation approach for wireless channels, offering a viable alternative to classical analytic estimators with improved accuracy in challenging scenarios.

Findings

Neural estimators outperform traditional methods in fading channels.

They provide more accurate short-time estimations.

They handle non-linear hardware and propagation effects effectively.

Abstract

Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical characterization from analytic system models which are used pervasively today. We explore an alternative approach to building estimators which relies principally on approximate regression using large datasets and large computationally efficient artificial neural network models capable of learning non-linear function mappings which provide compact and accurate estimates. For single carrier PSK modulation, we explore the accuracy and computational complexity of such estimators compared with the current gold-standard analytically derived alternatives. We compare performance in various wireless operating conditions and consider the trade offs between the two different classes of systems. Our results show the learned estimators can provide improvements in areas such as short-time estimation and estimation under non-trivial real world channel conditions such as fading or other non-linear hardware or propagation effects.