Correction: Mayyahi et al. Distributed Prediction-Enhanced Beamforming Using LR/SVR Fusion and MUSIC Refinement in 5G O-RAN Systems. Appl. Sci. 2025, 15, 7428

In the original publication [1], some references have been updated to reflect the exact names of the authors, the publication title, and bibliographic data. Moreover, some references were erroneous and have been corrected. Specifically, we have provided the following corrections in our publication.

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References [3,17,18,39] in the original publication were incorrectly cited and need to be replaced with the following references.

• 3. Mao, Q.; Hu, F.; Hao, Q. Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey. IEEE Commun. Surv. Tutor. 2018, 20, 2595–2621.
• 17. Wang, D.; Zhou, Q.; Partani, S.; Qiu, A.; Schotten, H.D. Mobility prediction Based on Machine Learning Algorithms. In Proceedings of the Mobile Communication—Technologies and Applications, 25th ITG-Symposium, Osnabrueck, Germany, 3–4 November 2021; pp. 1–5.
• 18. Yin, H.; Wang, H.; Liu, Y.; Gesbert, D. Addressing the Curse of Mobility in Massive MIMO With Prony-Based Angular-Delay Domain Channel Predictions. IEEE J. Sel. Areas Commun. 2020, 38, 2903–2917. https://doi.org/10.1109/JSAC.2020.3005473.
• 39. Oliveira, A.; Suzuki, D.; Bastos, S.; Correa, I.; Klautau, A. Machine Learning-Based mmWave MIMO Beam Tracking in V2I Scenarios: Algorithms and Datasets. In Proceedings of the 2024 IEEE Latin-American Conference on Communications (LATINCOM), Medellin, Colombia, 6–8 November 2024; pp. 1–5. https://doi.org/10.1109/LATINCOM62985.2024.10770674.

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References [15,20,23] had incorrect authors’ names. Therefore, the list of authors has been updated in these publications.

• 15. Rahil, A.; Mbarek, N.; Togni, O.; Atieh, M.; Fouladkar, A. Statistical learning and multiple linear regression model for network selection using MIH. In Proceedings of the 3rd International Conference on e-Technologies and Networks for Development (ICeND2014), Beirut, Lebanon, 29 April–1 May 2014; pp. 189–194.
• 20. Spanos, T.; Fabra, F.; López-Salcedo, J.A.; Seco-Granados, G.; Kanistras, N.; Lapin, I.; Paliouras, V. Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios. arXiv 2024, arXiv:2411.16501.
• 23. Marenco, L.; Hupalo, L.E.; Andrade, N.F.; de Figueiredo, F.A.P. Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond. Sci. Rep. 2024, 14, 20103.

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Finally, the following references contained small typos and have been appropriately corrected:

• 6. Ahmed, A.U.; Arablouei, R.; De Hoog, F.; Kusy, B.; Jurdak, R.; Bergmann, N. Estimating Angle of Arrival and Time of Flight for Multipath Components Using WiFi Channel State Information. Sensors 2018, 18, 1753. https://doi.org/10.3390/s18061753.
• 9. Brik, B.; Chergui, H.; Zanzi, L.; Devoti, F.; Ksentini, A.; Siddiqui, M.S.; Costa-Pérez, X.; Verikoukis, C. Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research. arXiv 2023, arXiv:2307.00319.
• 10. O-RAN Alliance. WG2.AIML-v02.00; AI/ML Workflow Description and Requirements; O-RAN Alliance: Alfter, Germany, 2022.
• 12. Yeh, S.P.; Bhattacharya, S.; Sharma, R.; Moustafa, H. Deep Learning for Intelligent and Automated Network Slicing in 5G Open RAN (O-RAN) Deployment. IEEE Open J. Commun. Soc. 2023, 5, 64–70. https://doi.org/10.1109/OJCOMS.2023.3337854.
• 13. Saad, S.A.; Shayea, I.; Sid Ahmed, N. Artificial intelligence linear regression model for mobility robustness optimization algorithm in 5G cellular networks. Alex. Eng. J. 2024, 89, 125–148. https://doi.org/10.1016/j.aej.2024.01.014.
• 14. Möhring, L.; Gläscher, J. Prediction errors drive dynamic changes in neural patterns that guide behavior. Cell Rep. 2023, 42, 112931. https://doi.org/10.1016/j.celrep.2023.112931.
• 25. Liu, S.; Li, X.; Mao, Z.; Liu, P.; Huang, Y. Model-driven deep neural network for enhanced AoA estimation using 5G gNB. In Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada, 20–27 February 2024; Volume 38, pp. 214–221.
• 26. Batalla, J.M.; de la Cruz Llopis, L.J.; Gómez, G.P.; Andrukiewicz, E.; Krawiec, P.; Mavromoustakis, C.X.; Song, H.H. Multi-Layer Security Assurance of the 5G Automotive System Based on Multi-Criteria Decision Making. IEEE Trans. Intell. Transp. Syst. 2024, 25, 3496–3512.
• 27. O-RAN Alliance. WG1-ARCH-Overview, v5.00; O-RAN Architecture Description; O-RAN Alliance: Alfter, Germany, 2023.
• 28. O-RAN Alliance. WG4.CUS.0-v06.00; O-RAN Front Haul Working Group: Control, User and Synchronization Plane Specification; O-RAN Alliance: Alfter, Germany, 2021.
• 29. O-RAN Alliance. WG5.TS.C.1-R004-v14.00; O-RAN Open F1/W1/E1/X2/Xn Interfaces Working Group, NR C-Plane Profile; O-RAN Alliance: Alfter, Germany, 2025.
• 31. O-RAN Alliance. WG4.MP.0-R003-v14.00, O-RAN Management Plane Specification 14.0; O-RAN Alliance: Alfter, Germany, 2024.
• 36. O-RAN Alliance. O-RAN Minimum Viable Plan and Acceleration Towards Commercialization, White Paper. June 2021. Available online: https://mediastorage.o-ran.org/white-papers/O-RAN.Minimum-Viable-Plan-and-Acceleration-towards-Commercialization-white-paper-2021-06.pdf (accessed on 10 March 2024).
• 38. Henninger, M.; Mandelli, S.; Arnold, M.; ten Brink, S. A Computationally Efficient 2D MUSIC Approach for 5G and 6G Sensing Networks. arXiv 2021, arXiv:2104.15132.
• 41. Mohsin, M.; Batalla, J.M.; Pallis, E.; Mastorakis, G.; Markakis, E.K.; Mavromoustakis, C.X. On Analyzing Beamforming Implementation in O-RAN 5G. Electronics 2021, 10, 2162. https://doi.org/10.3390/electronics10172162.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.