Optimizing Wireless Connectivity Through Reconfigurable Intelligent Surfaces: An Examination of Channel Estimation Strategies - Model-Based Versus Model-Free Method

The advent of Reconfigurable Intelligent Surfaces (RIS) marks a significant advancement in wireless communications, promising enhanced connectivity through dynamic alteration of the propagation environment. This paper investigates channel estimation strategies essential for optimizing RIS-enabled connectivity, distinguishing between model-based methods, which utilize theoretical models and mathematical formulations, and model-free methods, which rely on machine learning algorithms to adapt to empirical data. We introduce a novel hybrid channel estimation framework that integrates the predictive precision of model-based approaches with the adaptive capabilities of model-free strategies. Extensive simulations validate the superior performance of this hybrid framework, demonstrating estimation accuracy of 92%, system throughput of 120 Mbps, and communication reliability of 95%, significantly outperforming traditional methods. This innovative framework optimizes performance in dynamic communication environments, setting a new standard for future research in intelligent communication systems.