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This paper examines the assessment of AI calculations for the 6G radio asset assignment. It examines some of the difficulties 6G radio asset distribution applications face and proposes AI calculations as arrangements. It investigates the exhibition of different AI calculations, including support-vector machines, Guileless Bays, Arbitrary Woodland, k-Closest Neighbors, and Intermittent Brain Organizations. The paper investigates how each AI calculation performs when applied to various radio asset assignment undertakings and considers the outcomes across various situations. It then, at that point, gives a far-reaching rundown of the exhibition of these calculations for better direction.

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Evaluating the Performance of Machine Learning Algorithms for 6G Radio Resource Allocation

  • 会议论文

  • 发表日期：

    2025年

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  • Raman Batra,
  • Taskeen Zaidi,
  • Shweta Singh,
  • Deepak Kumar

摘要

This paper examines the assessment of AI calculations for the 6G radio asset assignment. It examines some of the difficulties 6G radio asset distribution applications face and proposes AI calculations as arrangements. It investigates the exhibition of different AI calculations, including support-vector machines, Guileless Bays, Arbitrary Woodland, k-Closest Neighbors, and Intermittent Brain Organizations. The paper investigates how each AI calculation performs when applied to various radio asset assignment undertakings and considers the outcomes across various situations. It then, at that point, gives a far-reaching rundown of the exhibition of these calculations for better direction.