Channel Characterization and CE-OFDM Modulation for Terahertz System

Terahertz communication: detection and signal processing

“…A single carrier (QAM) has a weaker transmission performance than OFDM with a lower computational complexity. Researchers have proposed CE-OFDM [121], CP-OFDM [122], MIMO-OFDMA, discrete Fourier transform-OFDM (DFT-s-OFDM), and SI-DFTs-OFDM based on OFDM, the latter of which has a 10-fold improvement in accuracy over conventional OFDM modulation schemes. QAM-based OQAM/FBMC [123] and DFT-s-OTFS have also been actively researched as schemes that can provide a lower PAPR and reduce the interference caused by Doppler expansion.…”

“…Owing to the channel characteristics of terahertz, conventional signal processing methods cannot fully achieve the desired performance in the terahertz band, and scientists have gradually introduced machine learning methods [134]. Compared to conventional signal processing methods, deep learning can provide better robustness for ISAC in terms of data detection at the receiver [135], thus advancing 6 G and beyond [120][121][122]. Machine learning plays an important role in signal preprocessing in terahertz systems, and more models applying deep learning such as SISR and improved GAN models have been proposed [133], providing the possibility of high-quality images for non-destructive detection.…”