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Related Experiment Video

Updated: Sep 16, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

664

Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid

C H Ramesh Babu1, S Nandakumar2

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.

Scientific Reports
|July 11, 2025
PubMed
Summary

This study introduces a novel optimization method for 5G device-to-device (D2D) networks, enhancing relay selection and resource allocation for improved spectral and energy efficiency. The approach addresses interference and sustainability challenges in modern wireless communications.

Keywords:
Adaptive residual gated recurrent unitDevice-to-device communicationHybrid manta-ray foraging with chef based optimizationJoint relay selection and resource allocation

Related Experiment Videos

Last Updated: Sep 16, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

664

Area of Science:

  • Wireless Communication Networks
  • Optimization Algorithms
  • Machine Learning Applications

Background:

  • Fifth-generation (5G) networks aim to boost data rates for innovations like device-to-device (D2D) communication.
  • Relay-assisted D2D communication faces challenges including interference and energy consumption by relays, reducing system performance.
  • Existing methods struggle with efficient relay selection and resource allocation in complex network environments.

Purpose of the Study:

  • To propose an efficient relay selection and resource allocation strategy for 5G D2D communication.
  • To enhance system performance by addressing mutual interferences and energy sustainability issues.
  • To leverage advanced optimization and machine learning for improved network efficiency.

Main Methods:

  • A novel hybrid manta ray foraging with chef-based optimization (HMRFCO) algorithm was developed.
  • Relay selection criteria included spectral efficiency, energy efficiency, throughput, delay, and network capacity.
  • An adaptive residual gated recurrent unit (AResGRU) model, optimized by HMRFCO, was used for relay number prediction and resource allocation.

Main Results:

  • The HMRFCO algorithm effectively optimized the AResGRU model for prediction tasks.
  • The proposed method achieved efficient relay selection considering multiple performance metrics.
  • The AResGRU model successfully predicted optimal relay numbers and resource allocation.

Conclusions:

  • The developed HMRFCO and AResGRU integrated approach offers an efficient solution for relay selection and resource allocation in 5G D2D networks.
  • This method improves spectral and energy efficiency while mitigating interference.
  • The study highlights the potential of hybrid optimization and deep learning for sustainable and high-performance wireless networks.