{"title":"An Enhanced Transfer Learning-Based Automatic Modulation Recognition Method for OFDM-VLC System","authors":"Hong Wen;Zixiong Gao;Qinghui Chen;Jun Zhou","doi":"10.1109/LPT.2025.3563388","DOIUrl":null,"url":null,"abstract":"In this letter, an enhanced transfer learning-based automatic modulation recognition (AMR) scheme is proposed and experimentally demonstrated in orthogonal frequency division multiplexing visible light communication (OFDM-VLC) systems. Within the proposed Gaussian transfer learning (GTL) framework, a ResNet deep convolutional neural network (DCNN) is constructed using Gaussian constellations as the training set. The performance of the proposed method is evaluated by using GoogLeNet, AlexNet and ImageNet transfer learning (ITL) as benchmarks. Experimental results show that, using only 120 training samples with a limited number of constellation points per sample, ResNet with ITL achieves accuracy improvements of 13% and 7% over GoogLeNet and AlexNet with ITL, respectively. Furthermore, ResNet with the proposed GTL achieves an additional 4% accuracy enhancement compared to ResNet utilising conventional ITL.","PeriodicalId":13065,"journal":{"name":"IEEE Photonics Technology Letters","volume":"37 12","pages":"671-674"},"PeriodicalIF":2.3000,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Photonics Technology Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10973278/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this letter, an enhanced transfer learning-based automatic modulation recognition (AMR) scheme is proposed and experimentally demonstrated in orthogonal frequency division multiplexing visible light communication (OFDM-VLC) systems. Within the proposed Gaussian transfer learning (GTL) framework, a ResNet deep convolutional neural network (DCNN) is constructed using Gaussian constellations as the training set. The performance of the proposed method is evaluated by using GoogLeNet, AlexNet and ImageNet transfer learning (ITL) as benchmarks. Experimental results show that, using only 120 training samples with a limited number of constellation points per sample, ResNet with ITL achieves accuracy improvements of 13% and 7% over GoogLeNet and AlexNet with ITL, respectively. Furthermore, ResNet with the proposed GTL achieves an additional 4% accuracy enhancement compared to ResNet utilising conventional ITL.
期刊介绍:
IEEE Photonics Technology Letters addresses all aspects of the IEEE Photonics Society Constitutional Field of Interest with emphasis on photonic/lightwave components and applications, laser physics and systems and laser/electro-optics technology. Examples of subject areas for the above areas of concentration are integrated optic and optoelectronic devices, high-power laser arrays (e.g. diode, CO2), free electron lasers, solid, state lasers, laser materials'' interactions and femtosecond laser techniques. The letters journal publishes engineering, applied physics and physics oriented papers. Emphasis is on rapid publication of timely manuscripts. A goal is to provide a focal point of quality engineering-oriented papers in the electro-optics field not found in other rapid-publication journals.