TY - GEN
T1 - Robust Design of RIS-aided Full-Duplex RSMA System for V2X communication
T2 - 2023 IEEE Global Communications Conference, GLOBECOM 2023
AU - Pala, Sonia
AU - Katwe, Mayur
AU - Singh, Keshav
AU - Tsiftsis, Theodoros A.
AU - Li, Chih Peng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The proliferation of multiple devices and acceleration of spectral efficiency has become a pivotal requirement for the unprecedented connectivity and performance of vehicle-to-everything (V2X) networks. This paper investigates an unconventional framework of reconfigurable intelligent surface (RIS)-integrated full-duplex (FD) rate-splitting multiple access (RSMA) communication systems, which aims to maximize the spectral efficiency of uplink (UL) and downlink (DL) vehicles in V2X network. In particular, a robust spectral-efficient design for the considered RIS-integrated FD-RSMA system via joint beamforming design and power allocation at UL vehicles under imperfect channel state information is investigated. To tackle the non-convexity of the original sum-rate maximization problem, we adopt a deep reinforcement learning (DRL)-based proximal policy optimization (PPO) algorithm which leverages Markov decision process formulation. Simulation results demonstrate the effectiveness of the integration of RIS, RSMA, and FD schemes for V2X networks over half-duplex (HD) and multi-user linear precoding schemes. Furthermore, the superiority of the proposed PPO algorithm is validated over the counterpart deep deterministic policy gradient algorithm (DDPG).
AB - The proliferation of multiple devices and acceleration of spectral efficiency has become a pivotal requirement for the unprecedented connectivity and performance of vehicle-to-everything (V2X) networks. This paper investigates an unconventional framework of reconfigurable intelligent surface (RIS)-integrated full-duplex (FD) rate-splitting multiple access (RSMA) communication systems, which aims to maximize the spectral efficiency of uplink (UL) and downlink (DL) vehicles in V2X network. In particular, a robust spectral-efficient design for the considered RIS-integrated FD-RSMA system via joint beamforming design and power allocation at UL vehicles under imperfect channel state information is investigated. To tackle the non-convexity of the original sum-rate maximization problem, we adopt a deep reinforcement learning (DRL)-based proximal policy optimization (PPO) algorithm which leverages Markov decision process formulation. Simulation results demonstrate the effectiveness of the integration of RIS, RSMA, and FD schemes for V2X networks over half-duplex (HD) and multi-user linear precoding schemes. Furthermore, the superiority of the proposed PPO algorithm is validated over the counterpart deep deterministic policy gradient algorithm (DDPG).
KW - deep reinforcement learning (DRL)
KW - full-duplex (FD)
KW - rate-splitting multiple access (RSMA)
KW - Reconfigurable intelligent surface (RIS)
KW - robust beam-forming design
UR - https://www.scopus.com/pages/publications/85187353948
U2 - 10.1109/GLOBECOM54140.2023.10437254
DO - 10.1109/GLOBECOM54140.2023.10437254
M3 - Conference contribution
AN - SCOPUS:85187353948
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 2420
EP - 2425
BT - GLOBECOM 2023 - 2023 IEEE Global Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 December 2023 through 8 December 2023
ER -