Adversarial Robustness of Deep-Learning-Based Near-Field Beam Prediction In XL-MIMO Systems

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{208499,
        author = {Sneha Singh and Harshita Seth and Kashish Sharma and Prof. Guna Dhondwad},
        title = {Adversarial Robustness of Deep-Learning-Based Near-Field Beam Prediction In XL-MIMO Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {313-325},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208499},
        abstract = {.},
        keywords = {XL-MIMO, near-field communications, beam prediction, deep learning, adversarial machine learning, FGSM, PGD, adversarial training, beamforming, adversarial robustness.},
        month = {September},
        }

Cite This Article

Singh, S., & Seth, H., & Sharma, K., & Dhondwad, P. G. (2026). Adversarial Robustness of Deep-Learning-Based Near-Field Beam Prediction In XL-MIMO Systems. International Journal of Innovative Research in Technology (IJIRT), 313–325.

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