TY - GEN
T1 - Mitigating Propagation Losses in Terahertz Communication Through Reconfigurable Intelligent Surface
AU - Molla, Yibeltal Abebaw
AU - Yetneberk, Zenebe Melesew
AU - Wang, Kewei
AU - Ayalew, Birhanu Dessie
AU - Zheng, Tong Xing
AU - Tiba, Isayiyas Nigatu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This paper proposes a novel approach to mitigate propagation losses in terahertz (THz) communication, a key enabler for future 6G networks, by integrating reconfigurable intelligent surfaces (RIS) with a proximal gradient method (PGM) optimization framework. Operating in the 0.1–10 THz range, THz communication offers ultrahigh data rates and low latency but faces challenges from severe propagation losses caused by atmospheric absorption and scattering. The proposed RIS, comprising passive reflective elements, dynamically adjusts the phase shifts to enhance signal strength and coverage. A PGM-based algorithm optimizes these shifts and effectively mitigates the losses under varying conditions. Simulations demonstrated that the PGM-optimized RIS significantly reduced path loss, lowered outage probability, and enhanced ergodic capacity, particularly over long distances. These results highlight the potential of RIS-PGM integration to address THz propagation challenges and advance high-speed low-latency communications for future 6G networks.
AB - This paper proposes a novel approach to mitigate propagation losses in terahertz (THz) communication, a key enabler for future 6G networks, by integrating reconfigurable intelligent surfaces (RIS) with a proximal gradient method (PGM) optimization framework. Operating in the 0.1–10 THz range, THz communication offers ultrahigh data rates and low latency but faces challenges from severe propagation losses caused by atmospheric absorption and scattering. The proposed RIS, comprising passive reflective elements, dynamically adjusts the phase shifts to enhance signal strength and coverage. A PGM-based algorithm optimizes these shifts and effectively mitigates the losses under varying conditions. Simulations demonstrated that the PGM-optimized RIS significantly reduced path loss, lowered outage probability, and enhanced ergodic capacity, particularly over long distances. These results highlight the potential of RIS-PGM integration to address THz propagation challenges and advance high-speed low-latency communications for future 6G networks.
KW - 6G wireless networks
KW - Proximal gradient method (PGM)
KW - Reconfigurable intelligent surface (RIS)
KW - Terahertz (THz) communication
UR - https://www.scopus.com/pages/publications/105019649219
U2 - 10.1007/978-981-96-5906-7_46
DO - 10.1007/978-981-96-5906-7_46
M3 - 会议稿件
AN - SCOPUS:105019649219
SN - 9789819659050
T3 - Lecture Notes in Electrical Engineering
SP - 563
EP - 573
BT - Proceedings of the 3rd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies
A2 - Jin, Peiquan
A2 - Zhao, Zhenyu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2024
Y2 - 27 December 2024 through 29 December 2024
ER -