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February 5, 2026SensorsOpen Access

AI-Driven Real-Time Phase Optimization for Energy Harvesting-Enabled Dual-IRS Cooperative NOMA Under Non-Line-of-Sight Conditions

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Authors

YAYasir Al-GhafriHAHafiz M. AsifZNZia Nadir

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Overview

A framework optimizes communication performance in non-line-of-sight networks, highlighting energy efficiency benefits.

Key Points

  • This research aims to enhance communication performance by optimizing phase shifts in a wireless network using AI techniques.
  • Developed a wireless network architecture incorporating dual intelligent reflecting surfaces and energy harvesting.
  • Employed a machine learning model for real-time phase optimization of both IRSs without traditional iterative processes.
  • Conducted numerical analyses to compare performance with conventional systems.
  • Achieved significant improvements in spectral efficiency and service reliability.
  • Demonstrated that the AI approach effectively adapts under dynamic channel conditions.
  • Showed enhanced energy efficiency in non-line-of-sight communication scenarios.

Cite This Study

Al-Ghafri et al. (2026) studied this question.

synapsesocial.com/papers/698435f0f1d9ada3c1fb562ehttps://doi.org/10.3390/s26030980
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