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Robust Offloading for Edge Computing-Assisted Sensing and Communication Systems: A Deep Reinforcement Learning

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Summary

This study introduces an integrated sensing, communication, and computation (ISCC) system to reduce spectrum congestion and computation load. It optimizes resources for energy efficiency while meeting perception and delay needs using advanced algorithms.

Keywords:
computation uncertaintydeep reinforcement learningintegrated communication and sensingmobile edge computingrobust design

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Area of Science:

  • Wireless communication systems
  • Edge computing
  • Signal processing

Background:

  • Spectrum congestion and computation burden are critical challenges in modern wireless systems.
  • Integrated Sensing, Communication, and Computation (ISCC) systems offer a unified approach to address these issues.
  • Uncertainty in task complexity poses a significant challenge for computation resource allocation in edge computing.

Purpose of the Study:

  • To develop a robust resource allocation strategy for ISCC systems that minimizes energy consumption.
  • To address computation uncertainty by optimizing communication and computing resources.
  • To ensure system performance meets perception and delay constraints for target recognition applications.

Main Methods:

  • Formulation of a robust communication and computing resource allocation problem.
  • Optimization of transmit beamforming, offloading ratio, and computing resource allocation.
  • Application of a Markov decision process (MDP) with proximal policy optimization (PPO) for adaptive learning.

Main Results:

  • The proposed method effectively minimizes total energy consumption while satisfying perception and delay constraints.
  • The MDP-based PPO algorithm demonstrates rapid training speed and ensures latency compliance.
  • Simulation results validate the robustness and effectiveness of the proposed approach in ISCC systems.

Conclusions:

  • The developed adaptive learning strategy provides an effective solution for resource allocation in ISCC systems.
  • The integrated approach successfully balances communication, sensing, and computation tasks.
  • The findings contribute to more efficient and reliable edge computing solutions for target recognition.