{"title":"Cognitive semantic communications with codebook-based adaptive correction for image classification","authors":"Yabin Weng , Jinghao Sun , Junjie Wu","doi":"10.1016/j.phycom.2025.102764","DOIUrl":null,"url":null,"abstract":"<div><div>Conventional bit-oriented communication paradigms face several critical challenges in emerging sixth-generation (6G) wireless scenarios. One of the key challenges is low-efficient symbol transmission without considering task-oriented semantic importance, thus leading to high bandwidth consumption. To overcome this challenge, we propose a novel cognitive semantic communication (CSC) framework for the image classification, where the remote facial expression recognition for drivers is considered. Unlike conventional semantic communication methods that passively handle predefined semantic features, the proposed CSC framework actively identifies and adapts the most critical emotional features for transmission, substantially reducing bandwidth and signal quality requirements while preserving essential expression details. Moreover, for the first time, we design a cognitive-inspired semantic inference model equipped with a codebook-based correction mechanism to mitigate semantic distortion caused by physical channel impairments, enhancing reliability and interpretability under adverse conditions. Extensive simulation results demonstrate that proposed CSC can significantly outperform existing methods in recognition accuracy under constrained communication scenarios. By integrating cognitive-inspired semantic extraction, intelligent encoding–decoding processes, and context-aware inference, our proposed approach advances the state of the art in semantic communications for the image classification, offering a more robust and resource-efficient solution.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102764"},"PeriodicalIF":2.0000,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490725001673","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Conventional bit-oriented communication paradigms face several critical challenges in emerging sixth-generation (6G) wireless scenarios. One of the key challenges is low-efficient symbol transmission without considering task-oriented semantic importance, thus leading to high bandwidth consumption. To overcome this challenge, we propose a novel cognitive semantic communication (CSC) framework for the image classification, where the remote facial expression recognition for drivers is considered. Unlike conventional semantic communication methods that passively handle predefined semantic features, the proposed CSC framework actively identifies and adapts the most critical emotional features for transmission, substantially reducing bandwidth and signal quality requirements while preserving essential expression details. Moreover, for the first time, we design a cognitive-inspired semantic inference model equipped with a codebook-based correction mechanism to mitigate semantic distortion caused by physical channel impairments, enhancing reliability and interpretability under adverse conditions. Extensive simulation results demonstrate that proposed CSC can significantly outperform existing methods in recognition accuracy under constrained communication scenarios. By integrating cognitive-inspired semantic extraction, intelligent encoding–decoding processes, and context-aware inference, our proposed approach advances the state of the art in semantic communications for the image classification, offering a more robust and resource-efficient solution.
期刊介绍:
PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published.
Topics of interest include but are not limited to:
Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.