Patents by Inventor Xiaoshen LI

Xiaoshen LI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Low-power wide area network integrated sensing and communication method based on channel sensing and reinforcement learning
    Patent number: 12238543
    Abstract: The invention provides a low-power wide area network integrated sensing and communication method based on channel sensing and reinforcement learning, and the method comprises the steps: receiving an uplink signal transmitted by a terminal node through a gateway receiver, demodulating the uplink signal through employing a soft demodulation algorithm, and obtaining a bit log-likelihood ratio; the deep reinforcement learning model completes channel reconstruction of the corresponding link during communication according to the bit log-likelihood ratio; and calculating the current optimal network configuration according to the reconstructed channel so as to distribute the current optimal network configuration to each terminal node during next downlink transmission.
    Type: Grant
    Filed: October 10, 2024
    Date of Patent: February 25, 2025
    Assignees: The Hong Kong University of Science and Technology (Guangzhou), Shenzhen University, China Mobile Information Technology Co., Ltd
    Inventors: Kaishun Wu, Yongzhi Huang, Lu Wang, Xiaoshen Li, Hong Liu, Li Li, Min Sun
  • LOW-POWER WIDE AREA NETWORK INTEGRATED SENSING AND COMMUNICATION METHOD BASED ON CHANNEL SENSING AND REINFORCEMENT LEARNING
    Publication number: 20250039698
    Abstract: The invention provides a low-power wide area network integrated sensing and communication method based on channel sensing and reinforcement learning, and the method comprises the steps: receiving an uplink signal transmitted by a terminal node through a gateway receiver, demodulating the uplink signal through employing a soft demodulation algorithm, and obtaining a bit log-likelihood ratio; the deep reinforcement learning model completes channel reconstruction of the corresponding link during communication according to the bit log-likelihood ratio; and calculating the current optimal network configuration according to the reconstructed channel so as to distribute the current optimal network configuration to each terminal node during next downlink transmission.
    Type: Application
    Filed: October 10, 2024
    Publication date: January 30, 2025
    Applicants: The Hong Kong University of Science and Technology (Guangzhou), Shenzhen University, China Mobile Information Technology Co., Ltd
    Inventors: Kaishun WU, Yongzhi HUANG, Lu WANG, Xiaoshen LI, Hong LIU, Li LI, Min SUN