IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G

TL;DR

This paper introduces IIFNet, a machine learning-based fusion network designed to enhance preamble detection in noisy environments for 6G networks, improving reliability amid environmental and channel-induced noise.

Contribution

The paper presents a novel fusion network architecture and sampling strategy to improve noisy preamble detection in next-generation wireless networks.

Findings

Detection performance drops to 48% with 15% noise

IIFNet significantly improves detection accuracy in noisy conditions

Real dataset validation demonstrates practical effectiveness

Abstract

In this article, we present our vision of preamble detection in a physical random access channel for next-generation (Next-G) networks using machine learning techniques. Preamble detection is performed to maintain communication and synchronization between devices of the Internet of Everything (IoE) and next-generation nodes. Considering the scalability and traffic density, Next-G networks have to deal with preambles corrupted by noise due to channel characteristics or environmental constraints. We show that when injecting 15% random noise, the detection performance degrades to 48%. We propose an informative instance-based fusion network (IIFNet) to cope with random noise and to improve detection performance, simultaneously. A novel sampling strategy for selecting informative instances from feature spaces has also been explored to improve detection performance. The proposed IIFNet is tested on a real dataset for preamble detection that was collected with the help of a reputable commercial company.