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<p>Intelligent Reflecting Surface (IRS) have emerged as a transformative technology for enhancing wireless communication by dynamically shaping the propagation environment. However, optimizing beamforming in IRS-assisted systems presents significant challenges due to the high-dimensional, non-convex nature of jointly optimizing phase shifts and transmission power. Traditional optimization techniques often suffer from limited scalability and slow convergence, reducing their effectiveness in large-scale, real-time deployments. To address these challenges, this paper proposes a novel hybrid metaheuristic optimization framework that integrates the global exploration capabilities of the Grey Wolf Optimizer (GWO) with the local exploitation strengths of Differential Evolution (DE). The proposed GWO-DE algorithm efficiently minimizes total transmission power while satisfying signal-to-noise ratio (SNR), power, and unit-modulus phase shift constraints, without requiring training data or prior statistical models. Extensive simulations demonstrate that the proposed method consistently outperforms conventional techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and standalone GWO and DE, in terms of power efficiency, convergence speed, and scalability. These advantages make it suitable for practical applications in emerging scenarios such as UAV-mounted IRS, smart cities, and edge IoT networks. The results confirm that hybrid metaheuristic approaches can provide robust, adaptive, and low-complexity solutions for next-generation wireless systems.</p>

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Optimized Beamforming for IRS-Aided Wireless Networks: A Hybrid Grey Wolf and Differential Evolution Approach

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Zaid Albataineh,
  • Haythem Bany Salameh,
  • Jamil K. J. Bataineh

摘要

Intelligent Reflecting Surface (IRS) have emerged as a transformative technology for enhancing wireless communication by dynamically shaping the propagation environment. However, optimizing beamforming in IRS-assisted systems presents significant challenges due to the high-dimensional, non-convex nature of jointly optimizing phase shifts and transmission power. Traditional optimization techniques often suffer from limited scalability and slow convergence, reducing their effectiveness in large-scale, real-time deployments. To address these challenges, this paper proposes a novel hybrid metaheuristic optimization framework that integrates the global exploration capabilities of the Grey Wolf Optimizer (GWO) with the local exploitation strengths of Differential Evolution (DE). The proposed GWO-DE algorithm efficiently minimizes total transmission power while satisfying signal-to-noise ratio (SNR), power, and unit-modulus phase shift constraints, without requiring training data or prior statistical models. Extensive simulations demonstrate that the proposed method consistently outperforms conventional techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and standalone GWO and DE, in terms of power efficiency, convergence speed, and scalability. These advantages make it suitable for practical applications in emerging scenarios such as UAV-mounted IRS, smart cities, and edge IoT networks. The results confirm that hybrid metaheuristic approaches can provide robust, adaptive, and low-complexity solutions for next-generation wireless systems.