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
10β3 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