Intermodulation Interference Detection in 6G Networks: A Machine Learning Approach

Fuente: arXiv
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Main Author: Mismar, Faris B.
Format: Preprint
Published: 2021
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author Mismar, Faris B.
author_facet Mismar, Faris B.
contents This paper demonstrates the use of machine learning to detect the presence of intermodulation interference across several wireless carriers. We show a salient characteristic of intermodulation interference and propose a machine learning based algorithm that detects the presence of intermodulation interference through the use of supervised learning. This algorithm can use the radio access network intelligent controller or the sixth generation of wireless communication (6G) edge node as a means of computation. Our proposed algorithm runs in linear time in the number of resource blocks, making it a suitable radio resource management application in 6G.
format Preprint
id arxiv_https___arxiv_org_abs_2111_00524
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Intermodulation Interference Detection in 6G Networks: A Machine Learning Approach
Mismar, Faris B.
Networking and Internet Architecture
This paper demonstrates the use of machine learning to detect the presence of intermodulation interference across several wireless carriers. We show a salient characteristic of intermodulation interference and propose a machine learning based algorithm that detects the presence of intermodulation interference through the use of supervised learning. This algorithm can use the radio access network intelligent controller or the sixth generation of wireless communication (6G) edge node as a means of computation. Our proposed algorithm runs in linear time in the number of resource blocks, making it a suitable radio resource management application in 6G.
title Intermodulation Interference Detection in 6G Networks: A Machine Learning Approach
topic Networking and Internet Architecture
url https://arxiv.org/abs/2111.00524