Title: Artificial intelligence enabled 5G-NR optimisation

Authors: Nidhi Sharma; Priyanka Ahlawat; Rajesh Kumar Aggarwal

Addresses: Department of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India ' Department of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India ' Department of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India

Abstract: Radio-frequency spectrum chunks/channels are like invisible traffic lanes that can be used simultaneously by multiple telecoms operators. For realising spectrum efficient intelligent communication in 5G-NR, the latency or time delay of corresponding channel may have to be reduced up to 5 ms. This paper discusses how artificial intelligence techniques can be applied at the physical layer of base station to train machines/devices for smooth user experience at application layer. An estimated channel can be trained by generating meaningful data/information at physical layer itself and then neural networks principal techniques like convolution neural networks (CNNs) can be applied to train the channel/spectrum for best schedules. Estimated best schedules can be predicted with negligible time delay by applying artificial intelligence techniques.

Keywords: machine learning; artificial intelligence; 5G new radio; channel estimation; beam forming; massive MIMO; millimetre waves; convolution neural networks; CNNs.

DOI: 10.1504/IJSNET.2023.131254

International Journal of Sensor Networks, 2023 Vol.42 No.1, pp.41 - 51

Received: 11 Jan 2023
Accepted: 25 Mar 2023
Published online: 01 Jun 2023 *

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