Ting Ma , Wei Yang , Feng Hu , Maode Ma , Chuang Qin
{"title":"针对支持mec的物联网网络的智能轻量级物理层认证方案","authors":"Ting Ma , Wei Yang , Feng Hu , Maode Ma , Chuang Qin","doi":"10.1016/j.phycom.2025.102777","DOIUrl":null,"url":null,"abstract":"<div><div>This paper proposes a lightweight Three-Layer Convolutional Neural Network(3L-CNN) based physical layer authentication (PLA) method for mobile edge computing-enabled IoT (MEC-IoT) networks. A novel channel state information (CSI) processing architecture is established where real and imaginary components are transformed into two-channel images with 64×64 resolution for network inputs. Two data augmentation techniques, Average Data Augmentation (ADA) and Exponentially Weighted Average(EWA) are developed, to enhance temporal correlation preservation and mobility pattern extraction in mobile scenarios, effectively mitigating training data scarcity for mobile devices. The core 3L-CNN architecture remains streamlined, employing progressive feature extraction through three convolutional layers with 64×8, 32×16, and 6×32 configurations, optimized by a hybrid loss function combining 50% Negative Log-Likelihood and 50% Cross-Entropy to refine classification boundaries. The architecture demonstrates 99% authentication accuracy for 10 devices configuration and maintains 96.6% accuracy for 30 devices respectively. It also exhibits superior robustness with 95% accuracy at 0 dB SNR. This lightweight solution achieves comparable performance to complex models while reducing training time by 66%, making it suitable for resource-constrained mobile edge computing-enabled IoT applications.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102777"},"PeriodicalIF":2.2000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An intelligent and lightweight physical layer authentication scheme for MEC-enabled IoT networks\",\"authors\":\"Ting Ma , Wei Yang , Feng Hu , Maode Ma , Chuang Qin\",\"doi\":\"10.1016/j.phycom.2025.102777\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper proposes a lightweight Three-Layer Convolutional Neural Network(3L-CNN) based physical layer authentication (PLA) method for mobile edge computing-enabled IoT (MEC-IoT) networks. A novel channel state information (CSI) processing architecture is established where real and imaginary components are transformed into two-channel images with 64×64 resolution for network inputs. Two data augmentation techniques, Average Data Augmentation (ADA) and Exponentially Weighted Average(EWA) are developed, to enhance temporal correlation preservation and mobility pattern extraction in mobile scenarios, effectively mitigating training data scarcity for mobile devices. The core 3L-CNN architecture remains streamlined, employing progressive feature extraction through three convolutional layers with 64×8, 32×16, and 6×32 configurations, optimized by a hybrid loss function combining 50% Negative Log-Likelihood and 50% Cross-Entropy to refine classification boundaries. The architecture demonstrates 99% authentication accuracy for 10 devices configuration and maintains 96.6% accuracy for 30 devices respectively. It also exhibits superior robustness with 95% accuracy at 0 dB SNR. This lightweight solution achieves comparable performance to complex models while reducing training time by 66%, making it suitable for resource-constrained mobile edge computing-enabled IoT applications.</div></div>\",\"PeriodicalId\":48707,\"journal\":{\"name\":\"Physical Communication\",\"volume\":\"72 \",\"pages\":\"Article 102777\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-07-25\",\"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/S1874490725001806\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490725001806","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}
An intelligent and lightweight physical layer authentication scheme for MEC-enabled IoT networks
This paper proposes a lightweight Three-Layer Convolutional Neural Network(3L-CNN) based physical layer authentication (PLA) method for mobile edge computing-enabled IoT (MEC-IoT) networks. A novel channel state information (CSI) processing architecture is established where real and imaginary components are transformed into two-channel images with 64×64 resolution for network inputs. Two data augmentation techniques, Average Data Augmentation (ADA) and Exponentially Weighted Average(EWA) are developed, to enhance temporal correlation preservation and mobility pattern extraction in mobile scenarios, effectively mitigating training data scarcity for mobile devices. The core 3L-CNN architecture remains streamlined, employing progressive feature extraction through three convolutional layers with 64×8, 32×16, and 6×32 configurations, optimized by a hybrid loss function combining 50% Negative Log-Likelihood and 50% Cross-Entropy to refine classification boundaries. The architecture demonstrates 99% authentication accuracy for 10 devices configuration and maintains 96.6% accuracy for 30 devices respectively. It also exhibits superior robustness with 95% accuracy at 0 dB SNR. This lightweight solution achieves comparable performance to complex models while reducing training time by 66%, making it suitable for resource-constrained mobile edge computing-enabled IoT applications.
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.