Advanced Puncturing-Based Resource Optimization in Wi-Fi 7 Using a Hybrid Framework

The surge in wireless traffic driven by applications such as augmented reality, cloud gaming, and IoT connectivity necessitates intelligent spectrum management in dense Wi-Fi 7 networks.This study presents an Advanced Puncturing–based Resource Optimization Model that combines stochastic exploration with memory-assisted stability through a hybrid Simulated Annealing–Tabu Search (SA–TS) framework. The approach learns to select puncturing patterns adaptively in response to varying interference conditions in balance between exploitation and exploration, and preserving network equilibrium with minimal signaling overhead. Simulation output indicates rapid convergence within 10 – 11 iterations with average throughput of 389.8 ± 63.6 Mbps, fairness index ranging from 0.90 – 0.98, and beacon airtime fraction of only 1.49 %. The model exhibits intelligent spectral adaptation, favoring full-bandwidth operation under low interference and narrower sub-bands under congestion. By embedding tabu-based memory into stochastic optimization, it advances distributed, self-organizing Wi-Fi 7 resource allocation toward high throughput, fairness, and stability. Future directions include machine learning–driven interference prediction, multi-link operation integration, and hardware-in-the-loop validation to bridge the gap toward real-world deployment.