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Query-Aware Semantic Encoder-Based Resource Allocation in Task-Oriented Communications

文献类型：期刊论文

作者Cai, Qing1,2,3; Zhou, Yiqing1,2,3; Liu, Ling1,2,3; Yu, Hanxiao1,2,3; Wu, Yihao1,2,3; Shi, Ningzhe1,2,3; Shi, Jinglin1,2,3
刊名IEEE TRANSACTIONS ON MOBILE COMPUTING
出版日期2025-07-01
卷号24期号:7页码:6413-6429
关键词Semantics Receivers Generators Feature extraction Costs Transmitters Decoding Data mining Bandwidth Vectors Semantic encoder Task-oriented communications Relevance-based data selection and bandwidth allocation Deep reinforcement learning
ISSN号1536-1233
DOI10.1109/TMC.2025.3541636
英文摘要Task-oriented communications with semantic encoders are promising to enhance the communication efficiency, by selecting and transmitting valuable data according to task requirements/queries. However, existing semantic encoders lack the capability to track the changing in queries, leading to biased data selection. This paper proposes a query-aware semantic encoder, i.e., Query-Data Cross (QDC) encoder for task-oriented communications. By consistently focusing on data features that are most relevant to the current query at the transmitter, QDC can adapt to changing queries. Based on the dynamic semantic relevance obtained by QDC, a relevance-based data selection and bandwidth allocation optimization (RDSBA) problem is formulated, considering a multi-device task-oriented communication system, where devices should transmit valuable data with high relevance to the queries broadcasted by the base station (BS). RDSBA aims to maximize the data profit of all devices, which is defined as the difference between the relevance of data selected for the BS and the cost of obtaining the data. Then, a DRL-based data selection and bandwidth allocation (DRL-DB) algorithm is proposed to solve the NP-hard optimization problem. Simulation results demonstrate that QDC can smartly track the changing in queries and achieve an accuracy of at least 85% in relevance evaluation, more than 8% higher than existing schemes. Based on the relevance provided by QDC, the proposed RDSBA scheme with DRL-DB can increase the data profit by at least 18%, comparing to existing schemes.
资助项目National Key Research and Development Program of China[2021YFB2900200] ; National Key Research and Development Program of China[2021YFB2900203] ; National Natural Science Foundation of China[U21A20449] ; CAS Project for Young Scientists in Basic Research[YSBR-035] ; National Natural Science Foundation of China[62201052]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:001504135300049
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/42345]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhou, Yiqing
作者单位1.Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Cai, Qing,Zhou, Yiqing,Liu, Ling,et al. Query-Aware Semantic Encoder-Based Resource Allocation in Task-Oriented Communications[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2025,24(7):6413-6429.
APA Cai, Qing.,Zhou, Yiqing.,Liu, Ling.,Yu, Hanxiao.,Wu, Yihao.,...&Shi, Jinglin.(2025).Query-Aware Semantic Encoder-Based Resource Allocation in Task-Oriented Communications.IEEE TRANSACTIONS ON MOBILE COMPUTING,24(7),6413-6429.
MLA Cai, Qing,et al."Query-Aware Semantic Encoder-Based Resource Allocation in Task-Oriented Communications".IEEE TRANSACTIONS ON MOBILE COMPUTING 24.7(2025):6413-6429.

入库方式： OAI收割

来源：计算技术研究所

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