Performance Enhancement via Partitioning Large Intelligent Surfaces in Downlink NOMA Networks

Makin, Madi; Arzykulov, Sultangali; Rabie, Khaled M.; Nauryzbayev, Galymzhan

Performance Enhancement via Partitioning Large Intelligent Surfaces in Downlink NOMA Networks

Files

Performance Enhancement via Partitioning Large Intelligent Surfaces in Downlink NOMA Networks.pdf (923.71 KB)

Type

Conference Paper

Authors

Makin, Madi
Arzykulov, Sultangali

Rabie, Khaled M.
Nauryzbayev, Galymzhan

KAUST Department

King Abdullah University of Science and Technology,Computer, Electrical, and Mathematical Sciences & Engineering Division,Thuwal,Saudi Arabia
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

Date

2022-10-06

Abstract

Low latency, high-data rates, and massive connectivity are the requirements for the emerging wireless technologies that will give a chance to high-demanding and progressive innovations in many spheres. Reconfigurable intelligent surfaces (RISs) are considered to be a promising technology for the rising wireless communication standards. This paper studies the large intelligent surface (LIS) enabled wireless network deploying non-orthogonal multiple access (NOMA) over Nakagami-m channels with non-fixed LIS positions. We propose the LIS partitioning method, where various LIS elements serve different NOMA users depending on their quality of service. Moreover, we also examine the effect of imperfect successive interference cancellation, the number of LIS elements, and their allocation amongst the users. The simulations and followed-up discussions are provided regarding the system’s ergodic capacity measurements.

Citation

Makin, M., Arzykulov, S., Rabie, K. M., & Nauryzbayev, G. (2022). Performance Enhancement via Partitioning Large Intelligent Surfaces in Downlink NOMA Networks. 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). https://doi.org/10.1109/csndsp54353.2022.9907962

Acknowledgements

This work was supported by the Nazarbayev University Faculty Development Competitive Research Grants Program under Grant no. 240919FD3935 (PI: G. Nauryzbayev).