无线电通信技术2026，Vol.52Issue(2)：259-269,11.DOI:10.3969/j.issn.1003-3114.2026.02.003
基于文本语义感知的无人机轨迹与数据收集联合优化
Joint Optimization of UAV Trajectory and Data Collection Based on Textual Semantic Awareness
摘要
Abstract
With the rapid advancement of the Internet of Things(IoT),the massive generation of data has imposed higher demands on the quality of wireless communication services.Unmanned Aerial Vehicle(UAV),leveraging their high mobility,rapid deployment capability,and flexible controllability,can serve as aerial mobile edge nodes to achieve efficient data collection and processing from ground-based IoT devices.However,in low signal-to-interference-plus-noise ratio environments,UAV-assisted data collection often faces challenges such as high bit error rates and limited transmission rates.Existing approaches are predominantly based on traditional communication models,often overlook the transmission characteristics and distribution features of semantic information,making it difficult to optimize UAV trajectory and spectrum resource utilization while ensuring semantic reconstruction quality.Therefore,a textual semantic awareness data collection and trajectory optimization method named Semantic Spectral Efficiency Optimization and Trajectory Selection(SSETS)is proposed.By integrating the geographical location,data volume,and semantic information distribution,an algorithm for balancing device clustering is designed.Then,a Multi-agent Deep Reinforcement Learning(MADRL)approach is adopted,where each UAV acts as an independent agent,making intelligent flight action decisions based on local dynamic environmental information to minimize overall energy consumption and maximize the system's Semantic Spectral Efficiency(S-SE).Compared with several baseline methods,the proposed algorithm can significantly reduce system energy consumption and improve the average S-SE.关键词
智联网/多无人机/数据收集/语义通信/深度强化学习Key words
Internet of Intelligence/multi-UAVs/data collection/semantic communication/DRL分类
信息技术与安全科学引用本文复制引用
翟象平,石奕琦,付爽,刘鑫,易畅言..基于文本语义感知的无人机轨迹与数据收集联合优化[J].无线电通信技术,2026,52(2):259-269,11.基金项目
国家自然科学基金(62531010) （62531010）
江苏省自然科学基金(BK20231439) National Natural Science Foundation of China(62531010) （BK20231439）
Jiangsu Provincial Natural Science Foundation of China(BK20231439) （BK20231439）
