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Enhancing reliable and energy-efficient UAV communications with RIS and deep reinforcement learning.

Wasim Ahmad1, Umar Islam2, Abdulkadhem A Abdulkadhem3

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Summary
This summary is machine-generated.

This study introduces a novel framework using deep reinforcement learning (DRL) and Quadrature Phase Shift Keying (QPSK) to mitigate electromagnetic interference (EMI) in uncrewed aerial vehicle (UAV) systems with reconfigurable intelligent surfaces (RIS). The system enhances signal quality, energy efficiency, and coverage in challenging environments.

Keywords:
DRLEMIEnergy efficiencyGaN power amplifierQPSKRISUAV communications

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Area of Science:

  • Wireless Communication
  • Artificial Intelligence
  • Electromagnetics

Background:

  • Growing demand for wireless communication necessitates advancements in reliability, coverage, and energy efficiency.
  • Uncrewed aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RIS) are key technologies for enhancing wireless systems.
  • Existing research integrating RIS with UAVs using deep reinforcement learning (DRL) often overlooks electromagnetic interference (EMI) challenges.

Purpose of the Study:

  • To propose a novel framework for RIS-assisted UAV communication systems that addresses EMI from Gallium nitride (GaN) power amplifiers.
  • To integrate DRL with Quadrature Phase Shift Keying (QPSK) modulation for real-time optimization of UAV deployment and RIS configurations.
  • To mitigate EMI effects, improve signal-to-interference-plus-noise ratio (SINR), and enhance energy efficiency.

Main Methods:

  • Development of a DRL-based framework for dynamic optimization of UAV and RIS parameters.
  • Integration of QPSK modulation to manage signal transmission under EMI conditions.
  • Real-time adaptation of system configurations to counteract EMI and optimize performance metrics.

Main Results:

  • Achieved up to 6.5 dB SINR improvement in interference-prone environments.
  • Demonstrated a 38% increase in energy efficiency compared to baseline models.
  • Reduced EMI impact by over 70% and extended coverage area by 35%.

Conclusions:

  • The proposed framework effectively mitigates EMI in RIS-assisted UAV systems, outperforming traditional methods.
  • The integration of QPSK and DRL enables real-time balancing of communication quality and energy consumption.
  • The system shows significant potential for deployment in dynamic and challenging environments like urban areas, disaster zones, and remote locations.