We propose a modular and full-fledged physical layer receiver design for Orthogonal
Frequency Division Multiplexing (OFDM) wireless systems leveraging the advances of deep …

This paper develops novel deep learning-based architectures and design methodologies for
an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one …

This paper develops novel deep learning-based architectures and design methodologies for
an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one …

In this letter, we propose a model-driven deep learning (DL) approach that combines DL
with the expert knowledge to replace the existing orthogonal frequency-division multiplexing …

Machine learning in the physical layer of communication systems holds the potential to
improve performance and simplify design methodology. Many algorithms have been …

In this paper, a deep learning based receiver is proposed for a collection of multi-carrier
wave-forms including both current and next-generation wireless communication systems. In …

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many
wireless communi-cation systems. The artificial intelligence (AI)-aided OFDM receivers are …

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for
communication links in many current and emerging Internet of Things (IoT) applications …

The (inverse) discrete Fourier transform (DFT/IDFT) is often perceived as essential to
orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex …

With the 5G Evolution/6G, the carrier frequency is expected to increase compared to the
current 4G/5G operations. Transmission at higher carrier frequency provides new …