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Summary
This summary is machine-generated.

This study introduces AI-native PHY-layer awareness for 6G networks, enabling waveform and numerology detection directly from radio signals. This advances self-optimizing and adaptive wireless systems.

Keywords:
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Area of Science:

  • Telecommunications Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • The transition to 6G wireless networks requires a move towards AI-native orchestration within the radio access network (RAN).
  • Current systems often rely on higher-layer signaling for network parameter identification, which can be inefficient.

Purpose of the Study:

  • To develop AI-based enablers for physical (PHY)-layer awareness in 6G networks.
  • To enable intrinsic intelligence within the RAN for improved spectrum awareness and system adaptability.

Main Methods:

  • Development of an AI-based waveform classifier to differentiate between Orthogonal Frequency-Division Multiplexing (OFDM) and Orthogonal Time Frequency Space (OTFS) signals using in-phase/quadrature (IQ) samples.
  • Implementation of an AI-based numerology detector to ascertain parameters like subcarrier spacing, FFT size, slot duration, and cyclic prefix type without higher-layer information.

Main Results:

  • Waveform classification achieved 99.5% accuracy.
  • Numerology detection exceeded 99% accuracy for most parameters.
  • Demonstrated robust joint inference of waveform and numerology features, confirming AI-native spectrum awareness feasibility.

Conclusions:

  • AI-native PHY-layer awareness is feasible for 6G wireless systems.
  • The developed enablers pave the way for self-optimizing, context-aware, and adaptive 6G networks.
  • Intrinsic intelligence in the RAN enhances spectrum utilization and system performance.