Performance Enhancement via Partitioning Large Intelligent Surfaces in Downlink NOMA Networks
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Conference PaperKAUST Department
King Abdullah University of Science and Technology,Computer, Electrical, and Mathematical Sciences & Engineering Division,Thuwal,Saudi ArabiaComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Date
2022-10-06Permanent link to this record
http://hdl.handle.net/10754/682323
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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/csndsp54353.2022.9907962Sponsors
This work was supported by the Nazarbayev University Faculty Development Competitive Research Grants Program under Grant no. 240919FD3935 (PI: G. Nauryzbayev).Publisher
IEEEConference/Event name
2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)Additional Links
https://ieeexplore.ieee.org/document/9907962/ae974a485f413a2113503eed53cd6c53
10.1109/csndsp54353.2022.9907962