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<p>This paper explores the pivotal role of machine learning (ML) in shaping the trajectory of wireless communication, particularly as it advances towards the sixth generation (6G). Through an exhaustive survey, it evaluates a spectrum of ML methodologies, spanning from conventional techniques to cutting-edge paradigms such as model agnostic meta learning (MAML), federated learning (FL), and generative adversarial networks (GANs), elucidating their transformative potential in redefining future wireless systems. By synthesizing insights from recent discussions on international mobile telecommunications for 2030 (IMT-2030) and ongoing research endeavors, the paper categorizes and assesses diverse 6G applications while identifying persistent challenges and directions for future research, particularly focusing on leveraging the capabilities of MAML, FL, and GANs in wireless communications.</p>

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Machine Learning in Future Wireless Communications: Innovations, Applications, and Open Challenges

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Shahnaz Fatima,
  • Shri Ramtej Kondamuri

摘要

This paper explores the pivotal role of machine learning (ML) in shaping the trajectory of wireless communication, particularly as it advances towards the sixth generation (6G). Through an exhaustive survey, it evaluates a spectrum of ML methodologies, spanning from conventional techniques to cutting-edge paradigms such as model agnostic meta learning (MAML), federated learning (FL), and generative adversarial networks (GANs), elucidating their transformative potential in redefining future wireless systems. By synthesizing insights from recent discussions on international mobile telecommunications for 2030 (IMT-2030) and ongoing research endeavors, the paper categorizes and assesses diverse 6G applications while identifying persistent challenges and directions for future research, particularly focusing on leveraging the capabilities of MAML, FL, and GANs in wireless communications.