Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bhardwaj, Vikash Kumar; Singh, Abhishek; Sharma, Shourya; Shukla, Mahendra K.; Pandey, Om Jee Title A name given to the resource BORCAE: Bayesian Optimized Residual Convolutional Autoencoder for Efficient Feedback Compression in RIS-Assisted Time-Varying IoT Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IEEE Transactions on Artificial Intelligence; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TAI.2025.3638329" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TAI.2025.3638329</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023566005?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023566005?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bhardwaj V.K., Indian Institute of Technology (BHU), Department of Electronics Engineering, Varanasi, 221005, India; Singh A., Indian Institute of Information Technology Bhagalpur, Department of Computer Science and Engineering, Bhagalpur, 813210, India; Sharma S., Christ University, Department of Electronics and Communication Engineering, Bengaluru, 560029, India; Shukla M.K., Christ University, ABVIIITM Gwalior, Department of Information Technology, 474015, India; Pandey O.J., Indian Institute of Technology (BHU), Department of Electronics Engineering, Varanasi, 221005, India Description An account of the resource Reconfigurable Intelligent Surfaces (RIS) have strong potential to improve the performance of time-varying Internet of Things (IoT) networks. However, a major challenge in operating RIS effectively is the need for frequent Quantized Phase Configuration (QPC) feedback bits from the Base Station (BS) to the controller. This challenge becomes more serious asthe RIS size grows, since the feedback bandwidth is limited. As a result, efficient compression of control signals is crucial for the practical deployment of RIS. In this work, we propose Bayesian Optimized Residual Convolutional AutoEncoder (BORCAE), a lightweight and noise-resilient feedback compression framework based on a 1D Convolutional Autoencoder with residual connections. The model is designed to reduce QPC feedback size while preserving high reconstruction fidelity. To ensure adaptability across varying deployment conditions, we employ Bayesian hyperparameter optimization using Optuna, which enables automatic tuning of key architectural hyperparameters. This optimization ensures that the architecture generalizes effectively across a wide range of operating scenarios. Additionally, we integrate the Limited Memory Broyden Fletcher Goldfarb Shanno (LBFGS) optimizer during the final training epochs, which accelerates convergence and improves stability. For performance evaluation, we use Normalized Mean Squared Error (NMSE) as the reconstruction metric. Extensive testing across different Signal-to-Interference-plus-Noise Ratio (SINR) levels demonstrates that BORCAE consistently achieves lower NMSE compared to DL-CsiNet and CsiNet. The results highlight the practical viability of BORCAE for RIS-assisted communication, offering improved efficiency, and scalability for real-world IoT and Sixth-Generation (6G) applications. 2020 IEEE. Subject The topic of the resource autoencoder; Bayesian optimization; Internet of Things (IoT); Limited Memory Broyden Fletcher Goldfarb Shanno (LBFGS).; Reconfigurable Intelligent Surface (RIS) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 26914581; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online