5G NR PRACH Detection with Convolutional Neural Networks (CNN): Overcoming Cell Interference Challenges

TL;DR

This paper introduces a CNN-based method for detecting PRACH signals in 5G NR networks, effectively addressing interference challenges to improve detection accuracy and network reliability.

Contribution

The study develops a novel CNN model specifically designed for PRACH detection under interference, outperforming traditional methods in accuracy and robustness.

Findings

CNN outperforms traditional detection methods

Enhanced detection accuracy and robustness

Potential for improved interference management in 5G

Abstract

In this paper, we present a novel approach to interference detection in 5G New Radio (5G-NR) networks using Convolutional Neural Networks (CNN). Interference in 5G networks challenges high-quality service due to dense user equipment deployment and increased wireless environment complexity. Our CNN-based model is designed to detect Physical Random Access Channel (PRACH) sequences amidst various interference scenarios, leveraging the spatial and temporal characteristics of PRACH signals to enhance detection accuracy and robustness. Comprehensive datasets of simulated PRACH signals under controlled interference conditions were generated to train and validate the model. Experimental results show that our CNN-based approach outperforms traditional PRACH detection methods in accuracy, precision, recall and F1-score. This study demonstrates the potential of AI/ML techniques in advancing interference management in 5G networks, providing a foundation for future research and practical applications in optimizing network performance and reliability.