The Evolution of Applications, Hardware Design, and Channel Modeling for Terahertz (THz) Band Communications and Sensing: Ready for 6G?
<|reference_start|>The Evolution of Applications, Hardware Design, and Channel Modeling for Terahertz (THz) Band Communications and Sensing: Ready for 6G?: For decades, the terahertz (THz) frequency band had been primarily explored in the context of radar, imaging, and spectroscopy, where multi-gigahertz (GHz) and even THz-wide channels and the properties of terahertz photons offered attractive target accuracy, resolution, and classification capabilities. Meanwhile, the exploitation of the terahertz band for wireless communication had originally been limited due to several reasons, including (i) no immediate need for such high data rates available via terahertz bands and (ii) challenges in designing sufficiently high power terahertz systems at reasonable cost and efficiency, leading to what was often referred to as "the terahertz gap". This roadmap paper first reviews the evolution of the hardware design approaches for terahertz systems, including electronic, photonic, and plasmonic approaches, and the understanding of the terahertz channel itself, in diverse scenarios, ranging from common indoors and outdoors scenarios to intra-body and outer-space environments. The article then summarizes the lessons learned during this multi-decade process and the cutting-edge state-of-the-art findings, including novel methods to quantify power efficiency, which will become more important in making design choices. Finally, the manuscript presents the authors' perspective and insights on how the evolution of terahertz systems design will continue toward enabling efficient terahertz communications and sensing solutions as an integral part of next-generation wireless systems.<|reference_end|>
@article{jornet2024the,
    title={The Evolution of Applications, Hardware Design, and Channel Modeling for
  Terahertz (THz) Band Communications and Sensing: Ready for 6G?},
    author={Josep M. Jornet, Vitaly Petrov, Hua Wang, Zoya Popovic, Dipankar
  Shakya, Jose V. Siles, Theodore S. Rappaport},
    journal={arXiv preprint arXiv:2406.06105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06105},
    primaryClass={cs.NI cs.IT math.IT}
}