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Related Experiment Video

Updated: Oct 17, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-Enabled IoT.

Radwa Ahmed Osman1, Sherine Nagy Saleh2, Yasmine N M Saleh3

  • 1Basic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study introduces a deep learning model to reduce interference in 5G and Internet of Things (IoT) networks. It optimizes device distances for better communication, boosting system throughput and energy efficiency.

Keywords:
1D-CNN5GIoTdeep learningenergy efficiencyinterferenceoptimizationthroughput

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Last Updated: Oct 17, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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Area of Science:

  • Wireless Communication
  • Network Engineering
  • Artificial Intelligence

Background:

  • The integration of fifth-generation (5G) networks and the Internet of Things (IoT) presents challenges, primarily interference, due to shared spectrum usage.
  • Interference occurs between IoT devices (IoTDs), IoT Gateways (IoTG), cellular user equipment (CUE), and base stations (BS) in both uplink and downlink communications.

Purpose of the Study:

  • To propose a novel interference avoidance distributed deep learning model for 5G and IoT environments.
  • To optimize communication distances between various network entities (IoTD-D, CUE-IoTG, BS-IoTD, IoTG-CUE) for enhanced system performance.

Main Methods:

  • A distributed deep learning model was developed, trained using data generated by a Lagrange optimization technique.
  • The model predicts optimal distances for uplink and downlink data transmission to mitigate interference.
  • Performance was evaluated against state-of-the-art regression benchmarks.

Main Results:

  • The proposed deep learning model demonstrated significant improvements over existing benchmarks, evidenced by lower mean absolute error and root mean squared error.
  • Both analytical and deep learning approaches achieved optimal system throughput and energy efficiency.
  • Effective suppression of interference was confirmed for communications involving destinations and IoT Gateways.

Conclusions:

  • The developed deep learning model successfully addresses interference issues in combined 5G and IoT networks.
  • The approach enhances overall system throughput and energy efficiency by optimizing device communication parameters.
  • This method offers a robust solution for reliable and efficient wireless communication systems.