Sensing Integrated DFT-Spread OFDM Waveform and Deep Learning-powered Receiver Design for Terahertz Integrated Sensing and Communication Systems

TL;DR

This paper proposes a novel sensing integrated DFT-s-OFDM waveform and a deep learning-powered receiver for THz ISAC systems, achieving high accuracy in sensing and communication with improved BER and robustness.

Contribution

It introduces a new SI-DFT-s-OFDM waveform tailored for THz ISAC, and a deep learning-based receiver that enhances performance and robustness against system imperfections.

Findings

Achieves millimeter-level range and decimeter-per-second velocity estimation accuracy.

Improves BER performance by 5 dB at 10^{-3} level compared to OFDM.

Demonstrates robustness against Doppler effects, phase noise, and multi-target scenarios.

Abstract

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the $10^{-3}$ BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise, and multi-target estimation.