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<p>This paper presents the design of an intelligent reflective surface (IRS)-assisted integrated sensing and communication (ISAC) system in a millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) environment. The ISAC system uses the same radio frequency signals for both communication (data transmission) and sensing (gathering target object information). By jointly optimizing the communication and sensing functions, ISAC enhances resource utilization and reduces the system overhead. The directional properties of mmWave and the reconfiguration capabilities of IRS make the system suitable for target detection and sensing. The proposed method aims to jointly optimize communication and sensing capabilities while minimizing interference, enhancing spectral efficiency, and integrating sensing functionality. Specifically, we leverage semidefinite programming to address the semidefinite relaxation problem for active beamforming at the base station and employ a proximal policy optimization-based deep reinforcement learning agent to intelligently configure reflective beamforming at the IRS. Simulation findings indicate that the proposed active-reflective beamforming design surpasses the Greedy-based beamforming and SCA-AO-based techniques by 10.57% and 13.82% with 64 IRS elements, respectively, at an SNR of 20 dB.</p>

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On the joint design of active and reflective beamforming for IRS-assisted integrated sensing and communication system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Irfan Ahmed,
  • Moath Awawdeh,
  • Tarig Faisal

摘要

This paper presents the design of an intelligent reflective surface (IRS)-assisted integrated sensing and communication (ISAC) system in a millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) environment. The ISAC system uses the same radio frequency signals for both communication (data transmission) and sensing (gathering target object information). By jointly optimizing the communication and sensing functions, ISAC enhances resource utilization and reduces the system overhead. The directional properties of mmWave and the reconfiguration capabilities of IRS make the system suitable for target detection and sensing. The proposed method aims to jointly optimize communication and sensing capabilities while minimizing interference, enhancing spectral efficiency, and integrating sensing functionality. Specifically, we leverage semidefinite programming to address the semidefinite relaxation problem for active beamforming at the base station and employ a proximal policy optimization-based deep reinforcement learning agent to intelligently configure reflective beamforming at the IRS. Simulation findings indicate that the proposed active-reflective beamforming design surpasses the Greedy-based beamforming and SCA-AO-based techniques by 10.57% and 13.82% with 64 IRS elements, respectively, at an SNR of 20 dB.