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

TL;DR

This paper presents a machine learning-based algorithm for detecting intermodulation interference in 6G networks, enabling efficient radio resource management by leveraging supervised learning and edge computing.

Contribution

It introduces a novel supervised learning algorithm for intermodulation interference detection that operates in linear time and is suitable for 6G edge deployment.

Findings

The algorithm effectively detects intermodulation interference in 6G scenarios.

It operates in linear time, suitable for real-time applications.

The method leverages existing network controllers and edge nodes.

Abstract

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.