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<p>Indoor millimetre-wave (mmWave) small-cell deployments, a key use case for sixth-generation (6G) wireless networks, increasingly rely on data-driven decision controllers for signal classification, beam adaptation, and spectrum allocation in low-mobility, pedestrian environments. These controllers are commonly trained on hybrid datasets that mix real world sensor measurements with large volumes of synthetically generated traces. However, the divergence in noise characteristics, temporal dynamics, and spectral profiles between real and simulated sensor data induces distributional drift that can cause model collapse, manifesting as degraded inference stability, entropy loss, and over the air (OTA) performance degradation. To address this challenge, we propose X-MCNet, an explainability guided training framework designed for intelligent sensing systems operating under synthetic data contamination. The approach continuously monitors Shapley value attributions of critical physical layer features such as SNR, Doppler spread, delay spread, and angle of arrival. It quantifies attribution divergence using a Kullback–Leibler based <i>Attribution Drift Index (ADI)</i>, and applies a convex dual reweighting mechanism to dynamically restore training stability by reintegrating high entropy OTA sensor instances. Experiments on a real time Software Defined Radio (SDR) sensor testbed demonstrate that X-MCNet achieves up to 19.7% lower exclusion AUC, 5.3% higher inclusion AUC, and a 29% reduction in bit error rate at 25 dB SNR, all with under 8% additional compute overhead. The framework’s empirically calibrated residual risk bound and edge-ready design confirm its viability for deployment in real time, resource constrained sensor platforms. X-MCNet thus offers a robust, interpretable, and spectrum-aware solution for indoor mmWave sensor-driven wireless systems operating under heavy synthetic-data reliance.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Hassam Ahmed Tahir,
  • Walaa Alayed,
  • Waqar Ul Hassan,
  • Muhammad Usman Munir

摘要

Indoor millimetre-wave (mmWave) small-cell deployments, a key use case for sixth-generation (6G) wireless networks, increasingly rely on data-driven decision controllers for signal classification, beam adaptation, and spectrum allocation in low-mobility, pedestrian environments. These controllers are commonly trained on hybrid datasets that mix real world sensor measurements with large volumes of synthetically generated traces. However, the divergence in noise characteristics, temporal dynamics, and spectral profiles between real and simulated sensor data induces distributional drift that can cause model collapse, manifesting as degraded inference stability, entropy loss, and over the air (OTA) performance degradation. To address this challenge, we propose X-MCNet, an explainability guided training framework designed for intelligent sensing systems operating under synthetic data contamination. The approach continuously monitors Shapley value attributions of critical physical layer features such as SNR, Doppler spread, delay spread, and angle of arrival. It quantifies attribution divergence using a Kullback–Leibler based Attribution Drift Index (ADI), and applies a convex dual reweighting mechanism to dynamically restore training stability by reintegrating high entropy OTA sensor instances. Experiments on a real time Software Defined Radio (SDR) sensor testbed demonstrate that X-MCNet achieves up to 19.7% lower exclusion AUC, 5.3% higher inclusion AUC, and a 29% reduction in bit error rate at 25 dB SNR, all with under 8% additional compute overhead. The framework’s empirically calibrated residual risk bound and edge-ready design confirm its viability for deployment in real time, resource constrained sensor platforms. X-MCNet thus offers a robust, interpretable, and spectrum-aware solution for indoor mmWave sensor-driven wireless systems operating under heavy synthetic-data reliance.