Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory

TL;DR

This paper introduces a deep learning-based blind estimator for doubly selective OFDM channels that does not require pilot symbols and provides a theoretical analysis of its mean squared error performance.

Contribution

It presents the first blind OFDM channel estimator using deep learning without pilot symbols and offers a novel theoretical analysis of its testing MSE performance.

Findings

Achieves accurate channel estimation without pilot symbols.

Provides theoretical bounds on the estimator's MSE.

Demonstrates effectiveness through empirical results.

Abstract

We provide a new generation solution to the fundamental old problem of a doubly selective fading channel estimation for orthogonal frequency division multiplexing (OFDM) systems. For systems based on OFDM, we propose a deep learning (DL)-based blind doubly selective channel estimator. This estimator does require no pilot symbols, unlike the corresponding state-of-the-art estimators, even during the estimation of a deep fading doubly selective channel. We also provide the first of its kind theory on the testing mean squared error (MSE) performance of our investigated blind OFDM channel estimator based on over-parameterized ReLU FNNs.