Low-power wide area network integrated sensing and communication method based on channel sensing and reinforcement learning

US12238543B2 · kind B2 · utility

Assignees

• The Hong Kong University of Science and Technology (Guangzhou) · CN
• SHENZHEN UNIVERSITY · CN
• China Mobile Information Technology Co., Ltd · CN

Inventors

• Kaishun Wu · Lo Wu, CN
• Yongzhi HUANG · Tangxia, CN
• Lu Wang · Beijing, CN
• Xiaoshen Li · Lo Wu, CN
• Hong Liu · Beijing, CN
• Li Li · Shaoxing, CN
• Min Sun · Shanghai, CN

Key dates

• Filing date: Oct 10, 2024
• Grant date: Feb 25, 2025
• Expiry date: Oct 10, 2044

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y02D30/70
• WIPO field: Digital communication
• WIPO sector: Electrical engineering

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. According to the method, the key environment information is mined by using the deep reinforcement learning model, so that the gateway can allocate optimal network configuration to different terminal nodes according to the current network environment condition, and the decoding capability of the gateway is improved.