ABSTRACT The advancement of sixth‐generation (6G) wireless communication demands intelligent, adaptive solutions to meet the increasing requirements of ultra‐reliable low‐latency communication (URLLC), massive connectivity, and terahertz (THz) data rates. Intelligent Reflecting Metasurfaces (IRMs) have emerged as a revolutionary paradigm that reconfigures the wireless propagation environment by intelligently controlling the behavior of electromagnetic waves. However, traditional beamforming methods face significant challenges in dynamically adapting to complex, time‐varying wireless environments, especially in the presence of high user mobility and signal obstructions. These limitations hinder the efficiency of signal directionality and energy focusing, resulting in sub‐optimal communication performance. To overcome these issues, a novel framework, the Model‐Level Multi‐Scale Edge‐Guided Attention graph neural Network (M‐LMSedGAN), is introduced, enabling efficient learning from complex spatial relationships and heterogeneous signal interactions. Optimization of the reflective phase shifts is achieved using the Biruni Earth Radius Optimization (BERO) algorithm, which enhances the global search capability and convergence efficiency of the system. The proposed method enables real‐time and context‐aware beamforming adjustments, achieving superior signal quality and improved network performance. Experimental evaluations demonstrate a peak beamforming accuracy of 99.9%, indicating robust generalization across dynamic environments and diverse user distributions. This intelligent approach establishes a foundation for deploying energy‐efficient, high‐performance IRM‐assisted 6G communication infrastructures.
Dhanke et al. (Tue,) studied this question.