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

doi:10.1109/TMC.2025.3541636

	Query-Aware Semantic Encoder-Based Resource Allocation in Task-Oriented Communications
	Cai, Qing 1,2,3; Zhou, Yiqing 1,2,3; Liu, Ling 1,2,3; Yu, Hanxiao 1,2,3; Wu, Yihao 1,2,3; Shi, Ningzhe 1,2,3; Shi, Jinglin 1,2,3
	2025-07-01
发表期刊	IEEE TRANSACTIONS ON MOBILE COMPUTING
ISSN	1536-1233
卷号	24 期号:7 页码:6413-6429
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.
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
DOI	10.1109/TMC.2025.3541636
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:001504135300049
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42346
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China 3.Beijing Key Lab Mobile Comp & Pervas Device, 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.