Abstract
Wireless networking is approaching a new era, which necessitates new frequency ranges and novel strategies. With recent circuit growth, communications over the Terahertz (THz) band is proving to be a viable option because of the tremendous bandwidth and low cost. On the other hand, THz signals suffer from significant direction loss, necessitating the use of precoding. In this paper, Deep Learning (DL) based precoding techniques for upcoming 6G networks were examined, along with their complexities. Based on the signal-to-noise ratio (SNR) and spectral efficiency (SE), the proposed DL-based precoding scheme is compared to traditional model-based precoding schemes. The proposed DL-based precoding technique is ideal for 6G networks, according to simulation results. Furthermore, the proposed DL-based precoding technique has lower computational complexity, making it suitable for parallel processing and high-speed data transmission.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
