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Research Article
Volume 2, Article ID: 2025.0024
Jehan Kadhim Shareef Al-Safi
jihan.k.shareef@utq.edu.iq
Abbas Thajeel Rhaif Alsahlanee
abbas.thajeel@utq.edu.iq
1 Digital Media Department, Faculty of Media, University of Thi-Qar, Nasiriyah, Thi-Qar, Iraq
2 Faculty of Education for Humanities, University of Thi-Qar, Nasiriyah, Thi-Qar, Iraq
* Author to whom correspondence should be addressed
Received: 09 Jul 2025 Accepted: 05 Nov 2025 Available Online: 10 Nov 2025
Intelligent Reflecting Surfaces (IRS) are pivotal for energy-efficient 6G networks, yet optimizing their performance for simultaneous high data rates and energy efficiency remains a complex challenge. This paper leverages artificial intelligence (AI) to address this problem by introducing a novel AI-based optimization algorithm: Dynamic and Static Particle Swarm Optimization (DS-PSO). The proposed AI method intelligently learns the optimal phase-shift configuration for IRS elements, dynamically balancing exploration and exploitation to maximize the signal-to-noise ratio at the receiver. Simulation results demonstrate the efficacy of our AI-driven approach, showing our AI-optimized IRS model achieves a superior energy efficiency up to 269% higher than a standard IRS at a 10 bits/s/Hz data rate. This study underscores the significant potential of AI algorithms in mastering the intricate trade-offs of next-generation communication systems, positioning our model as a formidable solution for sustainable 6G connectivity.
Disclaimer: This is not the final version of the article. Changes may occur when the manuscript is published in its final format.