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Related Experiment Videos

Explainability guided model collapse mitigation for synthetic data driven wireless decision systems.

Hassam Ahmed Tahir1, Walaa Alayed2, Waqar Ul Hassan3

  • 1School of Computing, Swinburne University, Melbourne, Australia.

Scientific Reports
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

X-MCNet enhances 6G wireless networks by addressing synthetic data issues in indoor millimeter-wave deployments. This framework improves model stability and performance, ensuring reliable signal classification and spectrum allocation.

Keywords:
5GAI/MLData scienceXAI

Related Experiment Videos

Area of Science:

  • Wireless Communications
  • Machine Learning for Signal Processing
  • Intelligent Sensing Systems

Background:

  • Indoor millimeter-wave (mmWave) small-cell networks are crucial for 6G wireless systems.
  • Data-driven controllers trained on hybrid datasets (real and synthetic) face distributional drift.
  • This drift leads to model collapse, impacting inference stability and over-the-air (OTA) performance.

Purpose of the Study:

  • To propose X-MCNet, an explainability-guided training framework to mitigate synthetic data contamination in intelligent sensing.
  • To enhance the stability and accuracy of wireless network controllers.
  • To enable robust performance in resource-constrained, real-time sensor platforms.

Main Methods:

  • X-MCNet monitors Shapley value attributions of physical layer features (e.g., SNR, Doppler spread).
  • An Attribution Drift Index (ADI) quantifies attribution divergence.
  • A convex dual reweighting mechanism reintegrates high entropy OTA sensor instances to restore training stability.

Main Results:

  • X-MCNet demonstrated up to 19.7% lower exclusion AUC and 5.3% higher inclusion AUC.
  • Achieved a 29% reduction in bit error rate at 25 dB SNR.
  • Minimal compute overhead (under 8%) with an empirically calibrated residual risk bound.

Conclusions:

  • X-MCNet provides a robust, interpretable, and spectrum-aware solution for indoor mmWave sensor-driven wireless systems.
  • The framework effectively addresses challenges posed by heavy reliance on synthetic data.
  • Its edge-ready design confirms viability for real-time, resource-constrained deployments.