Ma Shuai, Yang Lei, Wanying Ding, Li Hang, Zhongdan Zhang, Xu Jing, Zongyan Li, Xu Gang, Shiyin Li
{"title":"水下无线光通信信号解调的机器学习","authors":"Ma Shuai, Yang Lei, Wanying Ding, Li Hang, Zhongdan Zhang, Xu Jing, Zongyan Li, Xu Gang, Shiyin Li","doi":"10.23919/JCC.ja.2023-0084","DOIUrl":null,"url":null,"abstract":"The underwater wireless optical communication (UWOC) system has gradually become essential to underwater wireless communication technology. Unlike other existing works on UWOC systems, this paper evaluates the proposed machine learning-based signal demodulation methods through the selfbuilt experimental platform. Based on such a platform, we first construct a real signal dataset with ten modulation methods. Then, we propose a deep belief network (DBN)-based demodulator for feature extraction and multi-class feature classification. We also design an adaptive boosting (AdaBoost) demodulator as an alternative scheme without feature filtering for multiple modulated signals. Finally, it is demonstrated by extensive experimental results that the AdaBoost demodulator significantly outperforms the other algorithms. It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power. A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio (SNR) is higher.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning for signal demodulation in underwater wireless optical communications\",\"authors\":\"Ma Shuai, Yang Lei, Wanying Ding, Li Hang, Zhongdan Zhang, Xu Jing, Zongyan Li, Xu Gang, Shiyin Li\",\"doi\":\"10.23919/JCC.ja.2023-0084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The underwater wireless optical communication (UWOC) system has gradually become essential to underwater wireless communication technology. Unlike other existing works on UWOC systems, this paper evaluates the proposed machine learning-based signal demodulation methods through the selfbuilt experimental platform. Based on such a platform, we first construct a real signal dataset with ten modulation methods. Then, we propose a deep belief network (DBN)-based demodulator for feature extraction and multi-class feature classification. We also design an adaptive boosting (AdaBoost) demodulator as an alternative scheme without feature filtering for multiple modulated signals. Finally, it is demonstrated by extensive experimental results that the AdaBoost demodulator significantly outperforms the other algorithms. It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power. A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio (SNR) is higher.\",\"PeriodicalId\":9814,\"journal\":{\"name\":\"China Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"China Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.23919/JCC.ja.2023-0084\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Communications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.23919/JCC.ja.2023-0084","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Machine learning for signal demodulation in underwater wireless optical communications
The underwater wireless optical communication (UWOC) system has gradually become essential to underwater wireless communication technology. Unlike other existing works on UWOC systems, this paper evaluates the proposed machine learning-based signal demodulation methods through the selfbuilt experimental platform. Based on such a platform, we first construct a real signal dataset with ten modulation methods. Then, we propose a deep belief network (DBN)-based demodulator for feature extraction and multi-class feature classification. We also design an adaptive boosting (AdaBoost) demodulator as an alternative scheme without feature filtering for multiple modulated signals. Finally, it is demonstrated by extensive experimental results that the AdaBoost demodulator significantly outperforms the other algorithms. It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power. A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio (SNR) is higher.
期刊介绍:
China Communications (ISSN 1673-5447) is an English-language monthly journal cosponsored by the China Institute of Communications (CIC) and IEEE Communications Society (IEEE ComSoc). It is aimed at readers in industry, universities, research and development organizations, and government agencies in the field of Information and Communications Technologies (ICTs) worldwide.
The journal's main objective is to promote academic exchange in the ICTs sector and publish high-quality papers to contribute to the global ICTs industry. It provides instant access to the latest articles and papers, presenting leading-edge research achievements, tutorial overviews, and descriptions of significant practical applications of technology.
China Communications has been indexed in SCIE (Science Citation Index-Expanded) since January 2007. Additionally, all articles have been available in the IEEE Xplore digital library since January 2013.