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Bilevel Optimization for ISAC Systems with Proactive Eavesdropping Capabilities.

Tingyue Xue1, Wenhao Lu1, Mianyi Zhang1

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

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Summary
This summary is machine-generated.

This study optimizes integrated sensing and communication (ISAC) for enhanced surveillance. The proposed method improves both eavesdropping success and radar localization accuracy for public safety applications.

Keywords:
bilevel optimizationintegrated sensing and communicationproactive eavesdroppingradar detection

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Area of Science:

  • Wireless communication
  • Signal processing
  • Cybersecurity

Background:

  • Integrated Sensing and Communication (ISAC) is crucial for future wireless networks, enhancing spectrum efficiency.
  • Active surveillance is vital for public safety, improving detection rates via interference signals.
  • Optimizing both ISAC and surveillance performance presents a significant technical challenge.

Purpose of the Study:

  • To jointly optimize the performance of Integrated Sensing and Communication (ISAC) systems and active surveillance.
  • To maximize the probability of successful eavesdropping while simultaneously enhancing radar localization accuracy for suspicious transmitters.
  • To develop a robust algorithm for solving the complex bilevel optimization problem inherent in this joint optimization.

Main Methods:

  • Formulation of a bilevel optimization problem with competing objectives: maximizing eavesdropping probability and optimizing radar localization.
  • Application of the Rayleigh quotient and a decoupling strategy to simplify the optimization problem.
  • Introduction of penalty terms to ensure the convexity of the lower-level optimization problem.
  • Development of an algorithm to derive optimal analog transmit beamforming matrices and digital beamforming vectors.

Main Results:

  • The proposed algorithm successfully solves the bilevel optimization problem, yielding optimal beamforming solutions.
  • Performance analysis reveals a critical trade-off between the probability of successful eavesdropping and radar localization accuracy.
  • Simulation results confirm the algorithm's effectiveness in simultaneously improving eavesdropping success and localization precision.

Conclusions:

  • The developed algorithm effectively enhances both eavesdropping success probability and radar localization accuracy in ISAC systems.
  • This research provides a valuable framework for advancing surveillance capabilities in wireless sensor networks.
  • The findings have significant implications for crime prevention and public safety through improved sensing and communication strategies.