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Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)

Al-Jumaily, A, Sali, A, Jiménez, VPG, Lagunas, E, Natrah, FMI, Fontán, FP, Hussein, YS, Singh, MJ, Samat, F, Aljumaily, H and Al-Jumeily, D (2023) Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS). Sensors, 23 (13). p. 6175. ISSN 1424-8220

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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.

Item Type: Article
Uncontrolled Keywords: Learning; Computer Simulation; Information Technology; Neural Networks, Computer; 5G-BS; ANN; FSS Earth station; co-channel and adjacent channel; interference model; Learning; Neural Networks, Computer; Computer Simulation; Information Technology; 0301 Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: MDPI
SWORD Depositor: A Symplectic
Date Deposited: 07 May 2024 08:32
Last Modified: 07 May 2024 08:32
DOI or ID number: 10.3390/s23136175
URI: https://researchonline.ljmu.ac.uk/id/eprint/23181
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