This paper proposes a simplified Temporal Convolutional Network-based estimator to improve channel estimation in vehicular communication.

This paper proposes DANCE, a diffusion-based channel estimator for OFDM systems using a sparse linear inverse problem and a noise-adaptive posterior correction.

This paper proposes an integrated sensing and communications framework for CP-OFDM-based systems that supports chirp signal transmission, ensuring interference-free concurrent data transmissions and i…

This paper proposes a cyclic-prefix OFDM system for distributed acoustic sensing (DAS) to eliminate spatial inter-symbol interference and enable shared-waveform integrated sensing and communication.

This paper derives closed-form expressions for the effective channel between users in a multiuser Zak-OTFS system with heterogeneous delay-Doppler periods, and shows negligible inter-user interference…

This paper proposes a unified deep learning framework for joint NBI cancellation and robust soft demodulation using NBI-CNet and LLR-CNet to eliminate error floors and improve performance in OFDM syst…

A novel method is proposed to reduce PAPR of AFDM signals without side information by circularly shifting the signal in the affine domain and detecting the shift using an MLB receiver.

This paper proposes a novel rate-splitting multiple access (RSMA) system for 6G vehicular networks with coexisting OFDM and OTFS users, providing downlink communication and managing inter-carrier inte…

This paper addresses the security vulnerability of OFDM-based Physical Layer Authentication (PLA) when channel fading exhibits correlation, proposing a new attack model and a measurable guideline to d…

This paper proposes a vision-assisted OFDM-ISAC framework that fuses wireless and visual modalities to resolve ambiguity in multi-target scenarios and achieve better localization and delay performance…

This paper proposes a model for cross-band CSI reconstruction in multi-band low-altitude wireless systems using radio-frequency metadata and pilot-guided cross-attention.