Distortion Resilience for Goal-Oriented Semantic Communication

作     者：Nguyen, Minh-Duong Do, Quang Vinh Yang, Zhaohui Pham, Quoc-Viet Hwang, Won-Joo 

作者机构：Pusan Natl Univ Dept Informat Convergence Engn Busan 46241 South Korea Ton Duc Thang Univ Fac Elect & Elect Engn Wireless Commun Res Grp Ho Chi Minh City 700000 Vietnam Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China Univ Dublin Trinity Coll Dublin Sch Comp Sci & Stat Dublin D02PN40 Ireland 

出 版 物：《IEEE TRANSACTIONS ON MOBILE COMPUTING》 (IEEE Trans. Mob. Comput.)

年 卷 期：2025年第24卷第5期

页      面：3489-3501页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Research Foundation of Korea (NRF) - Korea government (MSIT) [RS-2024-00336962] Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development - Korea government (MSIT) [IITP-2024-RS-2023-00254177] MSIT (Ministryof Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2024-RS-2023-00260098] Korea Agency for Infrastructure Technology Advancement (KAIA) - Ministry of Land Infrastructure and Transport [RS-2023-00256816] Ministry of Education National Research Foundation of Korea Young Elite Scientists Sponsorship Program by CAST [2023QNRC001] Zhejiang Key RD Program [2023C01021] Fundamental Research Funds for the Central Universities [K2023QA0AL02] 

主　　题：Semantics Artificial intelligence Distortion Measurement Optimization Accuracy Wireless communication Communication efficiency data compression goal-oriented semantic communication resource allocation 

摘      要：Recent research efforts on Semantic Communication (SemCom) have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these approaches introduce a paradox: the accuracy of Artificial Intelligence (AI) tasks should naturally emerge through training rather than being dictated by network constraints. Acknowledging this dilemma, this work introduces an innovative approach that leverages the rate distortion theory to analyze distortions induced by communication and compression, thereby analyzing the learning process. Specifically, we examine the distribution shift between the original data and the distorted data, thus assessing its impact on the AI model s performance. Founding upon this analysis, we can preemptively estimate the empirical accuracy of AI tasks, making the goal-oriented SemCom problem feasible. To achieve this objective, we present the theoretical foundation of our approach, accompanied by simulations and experiments that demonstrate its effectiveness. The experimental results indicate that our proposed method enables accurate AI task performance while adhering to network constraints, establishing it as a valuable contribution to the field of signal processing. Furthermore, this work advances research in goal-oriented SemCom and highlights the significance of data-driven approaches in optimizing the performance of intelligent systems.