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October 3, 2025Open Access

Machine Learning in Near-Field Communication for 6G: A Survey

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Authors

AIAmjad IqbalAAAla’a Al-HabashnaGWGabriel Wainer

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Overview

This survey discusses machine learning applications in channel estimation and beamforming design in near-field communication, highlighting key challenges.

Key Points

  • Machine learning significantly enhances channel estimation procedures, improving accuracy and efficiency in NFC systems.
  • Beamforming design is optimized using machine learning, which allows for smarter signal direction in challenging environments.
  • Challenges like data privacy and computational complexity are addressed through advanced machine learning techniques, ensuring robust communication.
  • The survey outlines future directions for integrating machine learning in near-field system design, indicating its pivotal role in 6G development.

Cite This Study

Iqbal et al. (2025) studied this question.

synapsesocial.com/papers/68e040f3a99c246f578b37c5https://doi.org/10.48550/arxiv.2509.16723
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning in Near-Field Communication for 6G: A Survey2025
  2. 2Near-Field Communications: Research Advances, Potential, and Challenges2024 · 10 citations
  3. 3Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey2025
  4. 4Machine Learning Approaches for Adaptive Signal Processing in 6G Networks2024 · 2 citations
  5. 5Machine Learning-Enabled 5G and 6G Networks: Methods, Challenges, and Opportunities2026 · 4 citations