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

Desire Guel, Arsene Kabore, Didier Bassole

TL;DR

This work tackles the problem of detecting PRACH signals under cell interference in 5G-NR. It proposes a CNN-based interference detection framework that leverages the spatio-temporal structure of PRACH signals and channel information to classify interference-perturbed transmissions. Experimental results demonstrate high recall (≈92.5%) and strong precision/F1 (≈84.7%/88.4%), though training vs. validation gaps indicate overfitting and sensitivity to sequence variations. The study demonstrates the potential of AI-driven interference management to improve PRACH reliability and informs future directions for more diverse datasets and regularization to enable real-time, robust network operation.

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.

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

TL;DR

This work tackles the problem of detecting PRACH signals under cell interference in 5G-NR. It proposes a CNN-based interference detection framework that leverages the spatio-temporal structure of PRACH signals and channel information to classify interference-perturbed transmissions. Experimental results demonstrate high recall (≈92.5%) and strong precision/F1 (≈84.7%/88.4%), though training vs. validation gaps indicate overfitting and sensitivity to sequence variations. The study demonstrates the potential of AI-driven interference management to improve PRACH reliability and informs future directions for more diverse datasets and regularization to enable real-time, robust network operation.

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.
Paper Structure (30 sections, 2 equations, 12 figures, 4 tables)

This paper contains 30 sections, 2 equations, 12 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. 5G Random Access Procedures
  4. PRACH Sequence Generation
  5. Conventional Approaches for PRACH Detection
  6. AI Applications in Wireless Communications
  7. AI/ML Approaches for PRACH Detection
  8. AI/ML Techniques in Mobile Networks
  9. Service Optimization
  10. Radio Planning
  11. Proposed CNN-based PRACH Interference Detection Model
  12. Methodological Approach
  13. AI/ML Model Selection for Interference Management
  14. Performance Evaluation Methods
  15. Training and Testing Methodology
  16. ...and 15 more sections

Figures (12)

  • Figure 1: NR-PRACH Procedure.
  • Figure 2: NR-PRACH structure.
  • Figure 3: Functional diagram of the 5G-NR PRACH receiver.
  • Figure 4: ML model for interference detection/classification.
  • Figure 5: Different layers Diagram of the CNN model.
  • ...and 7 more figures