Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory
This addresses a fundamental challenge in wireless communication for OFDM systems, offering a novel approach that could reduce overhead and improve efficiency.
The paper tackles the problem of doubly selective fading channel estimation in OFDM systems by proposing a deep learning-based blind estimator that eliminates the need for pilot symbols, achieving performance without them even in deep fading conditions.
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.