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Summary

Objects near Internet of Things (IoT) antennas can cause detuning. A new resilient tuning strategy improves antenna system reliability by 10-40% in dynamic environments, overcoming detuning issues.

Keywords:
Gaussian processInternet of Things (IoT)algorithmmatching networkregression analysisreliabilitystatistical analysis

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Area of Science:

  • Electrical Engineering
  • Electromagnetics
  • Wireless Communication

Background:

  • Internet of Things (IoT) devices rely on embedded antennas.
  • Antenna performance degrades due to detuning and mismatch from nearby objects in the reactive near field.
  • Existing matching networks struggle in dynamic environments.

Purpose of the Study:

  • Characterize antenna reflection coefficient changes caused by objects.
  • Evaluate the reliability impact of dynamic environments on matching networks.
  • Propose a novel, resilient antenna tuning strategy.

Main Methods:

  • Gaussian process regression applied to experimental data to model reflection coefficient changes.
  • Simulations to assess matching network reliability under random object positions.
  • Development and testing of a new resilient network tuning strategy.

Main Results:

  • Antenna detuning is characterized by frequency and object distance.
  • Dynamic environments can reduce matching network reliability by up to 90%.
  • The proposed strategy enhances system reliability by 10% to 40%.

Conclusions:

  • Environmental factors significantly impact IoT antenna performance.
  • A new resilient tuning strategy offers a robust alternative to real-time adaptive matching.
  • The proposed method improves system reliability in dynamic scenarios.