mlsanityData to 14 Oct 2026

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

arXiv 2206.07483 ↗First posted 30 May 2022eess.SP cs.LG stat.MLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 41 of its 134 references that are in the map. First posted on arXiv 30 May 2022.

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41 of 134 references.

References · 41

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Citing papers per year: 2023: 2.

2 papers.

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