TY - GEN
T1 - Symbol Level Precoding in the RF Domain for Low Hardware Complexity RIS-Assisted MU-MISO Systems
AU - Tsinos, Christos G.
AU - Tsiftsis, Theodoros A.
AU - Schober, Robert
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, a radio-frequency (RF) domain symbol level precoding technique is developed for reconfigurable intelligent surface (RIS)-assisted downlink multiuser multiple-input single-output (MU-MISO) systems. We study a system with a base station (BS) employing an analog architecture formed by a phase shifting network which serves a number of single antenna users with the help of a RIS. Such an architecture facilitates significant reductions in power consumption and hardware complexity. The objective of this paper is to jointly derive the optimal RF precoder, RIS reflection matrix and receive processing coefficients, subject to constraints on the BS analog architecture, the total transmit power, and the structure of the RIS reflection matrix. To that end, a difficult nonconvex optimization problem is formulated and solved. An efficient algorithmic solution is developed for the considered problem. Numerical results show that the derived solution offers significant energy efficiency gains when compared to non-RIS-assisted approaches.
AB - In this paper, a radio-frequency (RF) domain symbol level precoding technique is developed for reconfigurable intelligent surface (RIS)-assisted downlink multiuser multiple-input single-output (MU-MISO) systems. We study a system with a base station (BS) employing an analog architecture formed by a phase shifting network which serves a number of single antenna users with the help of a RIS. Such an architecture facilitates significant reductions in power consumption and hardware complexity. The objective of this paper is to jointly derive the optimal RF precoder, RIS reflection matrix and receive processing coefficients, subject to constraints on the BS analog architecture, the total transmit power, and the structure of the RIS reflection matrix. To that end, a difficult nonconvex optimization problem is formulated and solved. An efficient algorithmic solution is developed for the considered problem. Numerical results show that the derived solution offers significant energy efficiency gains when compared to non-RIS-assisted approaches.
UR - https://www.scopus.com/pages/publications/85177586684
U2 - 10.1109/ICASSP49357.2023.10094879
DO - 10.1109/ICASSP49357.2023.10094879
M3 - Conference contribution
AN - SCOPUS:85177586684
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -
