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Abstract

This research investigates how deep learning might be used to optimize beamforming in wireless communication systems that are helped by Reconfigurable Intelligent Surfaces (RIS). Our goal is to increase the possible data rates by dynamically forecasting the best phase shifts for RIS elements by utilizing Convolutional Neural Networks (CNN) and hybrid CNN-Long Short-Term Memory (CNN-LSTM) models. We assess the performance of these deep learning models against conventional genie-aided techniques by simulating real-world wireless settings using the DeepMIMO dataset. The findings demonstrate that beamforming based on deep learning can reach near-optimal performance, greatly lowering the overhead associated with channel estimation while improving communication efficiency. In order to support the development of next-generation wireless networks like 5G and 6G, this study shows how deep learning approaches can be used to increase the effectiveness of RIS-assisted systems.

Reason for Retraction

This article has been retracted at the request of the Editorial Office, following an internal investigation conducted in accordance with the Committee on Publication Ethics (COPE) Retraction Guidelines.

The investigation identified serious concerns affecting the integrity and reliability of the published work. Specifically, one or more of the following issues were confirmed:

  1. Undisclosed use of computer-generated text and/or data, in which substantial portions of the content were produced using algorithmic or artificial intelligence–based tools without transparent disclosure, contrary to the journal's authorship and transparency policies.

  2. Compromised peer-review process, indicating irregularities that undermine the validity, independence or authenticity of the review procedure.

  3. Inappropriate or misleading citations, including references that are irrelevant, improperly used, or appear to artificially inflate citation metrics, thereby distorting the scholarly record.

  4. Authorship-related concerns, including the addition of new author(s) at a later stage of the publication process without adequate justification, documentation, or transparent disclosure, raising unresolved questions regarding author contributions, responsibility, and compliance with the journal's authorship criteria.

The Editorial Office determined that these issues significantly compromise the scientific integrity of the article, and that correction alone would be insufficient to address the concerns. Retraction was therefore deemed necessary to maintain the accuracy and trustworthiness of the scholarly record.

The authors were informed of the findings and the retraction decision. While the authors do not respond to this retraction, the journal has proceeded with the retraction in line with COPE guidance, which permits retraction without author consent when editorial integrity is at risk.

This retraction is issued to alert readers that the findings and conclusions of the article should not be relied upon. The original article will remain accessible for the sake of the scholarly record, but it will be clearly marked as retracted.

Apologies are offered to readers of the journal that this was not detected during the submission process.

Please see the Retraction Notice available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/7

DOI: https://doi.org/10.52866/2788-7421.1361

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