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

arXiv: https://arxiv.org/abs/2204.07854 · HTML: https://mlsanity.com/paper/2204.07854/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology](https://mlsanity.com/paper/1806.06898/) (2018)
- [Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions](https://mlsanity.com/paper/1808.02924/) (2018)
- [Random Access Preamble Design and Detection for 3GPP Narrowband IoT Systems](https://mlsanity.com/paper/1605.05384/) (2016)

## Built on by (most cited first, 2 of 2)

- [AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends](https://mlsanity.com/paper/2208.10921/) (2022)
- [SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks](https://mlsanity.com/paper/2211.11321/) (2022)

## Abstract (arXiv)

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.