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

• URL: http://arxiv.org/abs/2408.11659v1
• Date: Wed, 21 Aug 2024 14:33:43 GMT
• Title: 5G NR PRACH Detection with Convolutional Neural Networks (CNN): Overcoming Cell Interference Challenges
• Authors: Desire Guel, Arsene Kabore, Didier Bassole,
• Abstract summary: We present a novel approach to interference detection in 5G New Radio (5G-NR) networks using Convolutional Neural Networks (CNN) Our CNN-based model is designed to detect Physical Random Access Channel (PRACH) sequences amidst various interference scenarios. Experimental results show that our CNN-based approach outperforms traditional PRACH detection methods in accuracy, precision, recall and F1-score.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis [8.805162150763847]
  This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder. Our approach is grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoencoders.
  arXiv  Detail & Related papers  (2024-11-05T07:01:15Z)
• DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs [11.15939066175832]
  jamming attacks pose substantial risks to the 5G network. This work presents a novel deep learning-based technique for detecting jammers in CAV networks. Results show that the proposed method achieves 96.4% detection rate in extra low jamming power.
  arXiv  Detail & Related papers  (2024-03-05T04:29:31Z)
• Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN [63.90647197249949]
  In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. In this paper, we present a cooperative radio map estimation approach enabled by the generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2024-02-05T05:01:28Z)
• Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning [60.94661435297309]
  The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.
  arXiv  Detail & Related papers  (2023-01-20T17:06:34Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization [3.5573601621032935]
  This paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the Network Data Analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition. Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.
  arXiv  Detail & Related papers  (2022-09-21T15:21:59Z)
• Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
  We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT) The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
  arXiv  Detail & Related papers  (2022-05-22T12:16:43Z)
• IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G [11.605933594949251]
  Next-G networks have to deal with preambles corrupted by noise due to channel characteristics or environmental constraints. We propose an informative instance-based fusion network (IIFNet) to cope with random noise and to improve detection performance, simultaneously.
  arXiv  Detail & Related papers  (2022-04-16T18:14:14Z)
• Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization [67.93423427193055]
  Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies. In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
  arXiv  Detail & Related papers  (2021-11-23T22:25:10Z)
• ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting [0.0]
  We present complex-valued Convolutional Neural Networks (CNNs) for RF fingerprinting. We focus on the problem of fingerprinting wireless IoT devices in-the-wild using Deep Learning (DL) techniques.
  arXiv  Detail & Related papers  (2021-05-08T03:03:21Z)
• A Big Data Enabled Channel Model for 5G Wireless Communication Systems [71.93009775340234]
  This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN)
  arXiv  Detail & Related papers  (2020-02-28T05:56:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.