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A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning.

Koteeswaran Seerangan1, Malarvizhi Nandagopal2, Tamilmani Govindaraju3

  • 1Department of CSE (AI&ML), S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India.

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|September 27, 2024
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Summary

This study introduces an Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework to boost energy efficiency in unmanned aerial vehicle (UAV) networks. The ADRL-NLF framework optimizes UAV 3D trajectories for enhanced wireless coverage and network lifespan.

Keywords:
Deep reinforcement learningEnergy efficiencyHybrid energy valley and hermit crabNovel loss functionUnmanned aerial vehicles

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Area of Science:

  • Wireless Communication Networks
  • Artificial Intelligence in Networking
  • Energy Efficiency in Mobile Systems

Background:

  • Unmanned Aerial Vehicle (UAV) networks face limited lifespan due to battery constraints, necessitating enhanced energy efficiency for air-to-ground transmissions.
  • UAVs offer flexible deployment and high maneuverability, making them ideal for extending Internet of Things (IoT) coverage and improving spectrum efficiency, especially for remote users.
  • Conventional UAV-aided efficiency approaches require optimization to overcome challenges in energy consumption and network functionality.

Purpose of the Study:

  • To design an innovative energy efficiency framework for UAV-assisted networks utilizing a reinforcement learning mechanism.
  • To optimize wireless coverage for static and mobile ground users by enhancing UAV energy efficiency.
  • To maximize the energy efficiency rate of UAV networks through joint optimization of 3D trajectory, interference energy, and user count.

Main Methods:

  • Development of an Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework.
  • Optimization of UAV 3D trajectory, considering energy consumption during interference and the number of connected users.
  • Parameter tuning of the ADRL framework using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm.

Main Results:

  • The proposed ADRL-NLF framework demonstrates improved energy efficiency in UAV networks compared to traditional methods.
  • The 3D trajectory optimization effectively minimizes interference and enhances wireless coverage for ground users.
  • Experimental observations validate the effectiveness of the proposed energy efficiency model for UAV-based networks.

Conclusions:

  • The ADRL-NLF framework offers a significant advancement in optimizing energy efficiency for UAV-assisted networks.
  • Joint optimization of UAV 3D trajectory and resource allocation is crucial for maximizing network performance and lifespan.
  • The HEVHC algorithm effectively tunes ADRL parameters, further enhancing the proposed model's capabilities.