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Creators/Authors contains: "Xu, Lifan"

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  1. ; ; (, IEEE SAM Workshop)
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  2. ; ; ; (, IEEE journal of selected areas in sensors)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, IEEE)
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  4. ; ; ; (, IEEE Journal of Selected Areas in Sensors)
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  5. ; ; ; (, IEEE Transactions on Aerospace and Electronic Systems)
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  6. ; ; ; ; (, IEEE)
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  7. ; (, IEEE)
  8. ; ; ; (, IEEE ICASSP)
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  9. ; ; (, IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM))
    We present a deep reinforcement learning approach to design an automotive radar system with integrated sensing and communication. In the proposed system, sparse transmit arrays with quantized phase shifter are used to carry out transmit beamforming to enhance the performance of both radar sensing and communication. Through interaction with environment, the automotive radar learns a reward that reflects the difference between mainlobe peak and the peak sidelobe level in radar sensing mode or communication user feedback in communication mode, and intelligently adjust its beamforming vector. The Wolpertinger policy based action-critic network is introduced for beamforming vector learning, which solves the dimension curse due to huge beamforming action space. 
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