IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G
This addresses noisy preamble detection for 6G and IoE devices, but appears incremental as it builds on existing ML techniques for a specific domain problem.
The paper tackles preamble detection in 6G networks under noisy conditions, showing that 15% random noise degrades performance to 48%, and proposes IIFNet to improve detection.
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.