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Joint Task Offloading and Resource Allocation for Intelligent Reflecting Surface-Aided Integrated Sensing and

Liu Yang1, Yifei Wei1, Xiaojun Wang2

  • 1Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Egineering, Beijing University of Posts and Telecommunications, Xitucheng Road No. 10, Beijing 100876, China.

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Summary
This summary is machine-generated.

This study introduces an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to improve wireless systems. Deep reinforcement learning optimizes resource allocation for enhanced sensing, communication, and computation offloading.

Keywords:
deep reinforcement learningintegrated sensing and communicationintelligent reflecting surfaceresource allocation

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Area of Science:

  • Wireless communication
  • Signal processing
  • Artificial intelligence

Background:

  • Spectrum scarcity and poor wireless environments hinder integrated sensing and communication (ISAC) systems.
  • Optimizing resource allocation is crucial for balancing sensing, communication, and computational offloading performance.

Purpose of the Study:

  • To propose an intelligent reflecting surface (IRS)-aided ISAC framework.
  • To optimize system performance by jointly designing beamforming, phase shifts, and resource allocation.
  • To address the non-convex and NP-hard optimization problem using advanced AI techniques.

Main Methods:

  • Formulation as a Markov decision process for dynamic channel conditions.
  • Development of two deep reinforcement learning (DRL) schemes: Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG.
  • Inclusion of prioritized experience replay to accelerate DRL convergence.

Main Results:

  • The proposed DRL-based schemes effectively optimize spectrum and computing resource allocation.
  • Enhanced radar sensing quality and increased communication data rates were achieved.
  • Improved energy efficiency and reduced latency in computational offloading.

Conclusions:

  • The IRS-aided ISAC framework with DRL significantly outperforms benchmark methods.
  • The proposed DRL approaches provide a viable solution for complex optimization in dynamic wireless environments.
  • This work demonstrates the potential of AI in advancing ISAC systems.