Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS).
Al-Jumaily, Abdulmajeed; Sali, Aduwati; Jiménez, Víctor P Gilet al.
5G-BS; ANN; FSS Earth station; co-channel and adjacent channel; interference model; Computer Simulation; Information Technology; Learning; Neural Networks, Computer; 5g base station; Adjacent channels; Co channels; Earth stations; Interference modelling; Learning models; Neural network learning; Analytical Chemistry; Information Systems; Atomic and Molecular Physics, and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering
Abstract :
[en] Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques.
Disciplines :
Computer science
Author, co-author :
Al-Jumaily, Abdulmajeed; Wireless and Photonic Networks Research Centre of Excellence (WiPNET), Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia ; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, 28911 Madrid, Spain
Sali, Aduwati ; Wireless and Photonic Networks Research Centre of Excellence (WiPNET), Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Jiménez, Víctor P Gil ; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, 28911 Madrid, Spain
Natrah, Fatin Mohd Ikhsan ; Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Fontán, Fernando Pérez; Department of Signal Theory and Communications, Universidad de Vigo, 36310 Vigo, Spain
Hussein, Yaseein Soubhi; Department of Information Systems and Computer Science, Ahmed Bin Mohammed Military College (ABMMC), Doha P.O. Box 22988, Qatar
Singh, Mandeep Jit; Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Samat, Fazdliana ; Pusat Sains Angkasa Institut Perubahan Iklim, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Aljumaily, Harith; Department of Computer Science and Engineering, Universidad Carlos III de Madrid, 28912 Madrid, Spain
Al-Jumeily, Dhiya; School of Computer Science & Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
External co-authors :
yes
Language :
English
Title :
Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS).
Publication date :
05 July 2023
Journal title :
Sensors
ISSN :
1424-8220
eISSN :
1424-3210
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Switzerland
The authors would like to acknowledge IRENE (PID2020-115323RB-C33) (MINECO/AEI/FEDER, UE); MFOC: Madrid Flight on Chip—Innovation Cooperative Projects Comunidad of Madrid—HUBS 2018/Madrid Flight on Chip. In addition, this work was partly funded by IGNITE: Interference Modeling for 5G and FSS Coexistence at mmWave with Climate Change Considerations in the Tropical Region (FRGS/1/2021/TK0/UPM/01/1) and BIDANET: Parametric Big Data Analytics over Wireless Networks (UPM.RMC.800-3/3/1/GPB/2021/9696300, Vot No.: 9696300) for the financial assistance in the measurement campaign. This project is a collaboration with Rohde & Schwarz Malaysia for consultations on the measurement analysis. And in part by Grant ICARUS, PID2020-113240RB-I00 funded by MCIN/AEI/10.13039/501100011033.
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