Open AccessProviding URLLC Service in Multi-STAR-RIS Assisted and Full-Duplex Cellular Wireless Systems: A Meta-Learning Approach
Author(s) -
Yasoub Eghbali,
Shiva Kazemi Taskou,
Mohammad Robat Mili,
Mehdi Rasti,
Ekram Hossain
Publication year - 2024
Publication title -
ieee communications letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.929
H-Index - 148
eISSN - 1558-2558
pISSN - 1089-7798
DOI - 10.1109/lcomm.2023.3349377
Subject(s) - communication, networking and broadcast technologies
The Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) technology is an innovative approach that aims to enhance the performance of sixth-generation (6G) wireless networks. This study focuses on a multi-STAR-RIS and full-duplex (FD) communication system aimed at providing ultra-reliable low-latency communication (URLLC) services. To maximize the total uplink (UL) and downlink (DL) rates, beamforming and combining vectors at the base station (BS), the transmit power of UL users, the amplitude attenuations, and phase shifts of the STAR-RISs are jointly optimized. These optimizations take into account the maximum transmit power constraints of the BS and UL users, as well as the quality of service requirements of UL and DL users. Given the non-convex nature of the optimization problem, this study proposes a novel deep reinforcement learning algorithm called Meta DDPG, which combines meta-learning and deep deterministic policy gradient. Numerical results demonstrate that a multi-STAR-RIS assisted system can obtain a higher system total rate compared to the conventional multi-RIS assisted system.