基于循环条件Wasserstein生成对抗网络的6G通信无线信道状态信息预估与反馈

IF 1.7 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Rajesh Kedarnath Navandar, Arun Ananthanarayanan, Shubhangi Milind Joshi, Nookala Venu
{"title":"基于循环条件Wasserstein生成对抗网络的6G通信无线信道状态信息预估与反馈","authors":"Rajesh Kedarnath Navandar,&nbsp;Arun Ananthanarayanan,&nbsp;Shubhangi Milind Joshi,&nbsp;Nookala Venu","doi":"10.1002/dac.70033","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In this manuscript, an Advanced Estimation and Feedback of Wireless Channels State Information for sixth generation (6G) Communication via Recurrent Conditional Wasserstein Generative Adversarial Network (AEF-WCSI-6G-RCWGAN) is proposed. Deep Learning (DL) based channel estimation algorithm using Recurrent Conditional Wasserstein Generative Adversarial Network (RCWGAN) is estimated the channel parameters in 6G, such as channel gains and delays from received signals, which is crucial for effective communication and resource allocation. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition and neural network design for 6G. The deep learning-dependent channel estimator refines the predicted channel output, which is subsequently used for increase the efficacy and dependability of the communication scheme. The proposed AEF-WCSI-6G-RCWGAN is implemented and the performance metrics, like Detection Success Probability, Mean Square Error (MSE), and Normalized Mean Square Error (NMSE) are analyzed. Finally, the performance of the proposed AEF-WCSI-6G-RCWGAN method achieves 30.73%, 28.35%, and 29.62% higher Detection Success Probability, 25.73%, 28.05%, and 24.62% lower MSE when compared with existing methods: towards DL-assisted wireless channel estimate and CSI feedback for sixth generation (WCE-CSI-6G-GAN), an effectual deep neural network channel state estimate for Orthogonal frequency-division multiplexing (OFDM)wireless systems (CSE-WS-BiLSTM), and distributed machine learning dependent downlink channel estimate for reconfigurable intelligent surfaces supported wireless communications (DCE-AWC-HDCENet) methods, respectively.</p>\n </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 6","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advanced Estimation and Feedback of Wireless Channels State Information for 6G Communication via Recurrent Conditional Wasserstein Generative Adversarial Network\",\"authors\":\"Rajesh Kedarnath Navandar,&nbsp;Arun Ananthanarayanan,&nbsp;Shubhangi Milind Joshi,&nbsp;Nookala Venu\",\"doi\":\"10.1002/dac.70033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>In this manuscript, an Advanced Estimation and Feedback of Wireless Channels State Information for sixth generation (6G) Communication via Recurrent Conditional Wasserstein Generative Adversarial Network (AEF-WCSI-6G-RCWGAN) is proposed. Deep Learning (DL) based channel estimation algorithm using Recurrent Conditional Wasserstein Generative Adversarial Network (RCWGAN) is estimated the channel parameters in 6G, such as channel gains and delays from received signals, which is crucial for effective communication and resource allocation. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition and neural network design for 6G. The deep learning-dependent channel estimator refines the predicted channel output, which is subsequently used for increase the efficacy and dependability of the communication scheme. The proposed AEF-WCSI-6G-RCWGAN is implemented and the performance metrics, like Detection Success Probability, Mean Square Error (MSE), and Normalized Mean Square Error (NMSE) are analyzed. Finally, the performance of the proposed AEF-WCSI-6G-RCWGAN method achieves 30.73%, 28.35%, and 29.62% higher Detection Success Probability, 25.73%, 28.05%, and 24.62% lower MSE when compared with existing methods: towards DL-assisted wireless channel estimate and CSI feedback for sixth generation (WCE-CSI-6G-GAN), an effectual deep neural network channel state estimate for Orthogonal frequency-division multiplexing (OFDM)wireless systems (CSE-WS-BiLSTM), and distributed machine learning dependent downlink channel estimate for reconfigurable intelligent surfaces supported wireless communications (DCE-AWC-HDCENet) methods, respectively.</p>\\n </div>\",\"PeriodicalId\":13946,\"journal\":{\"name\":\"International Journal of Communication Systems\",\"volume\":\"38 6\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2025-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Communication Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/dac.70033\",\"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":"International Journal of Communication Systems","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/dac.70033","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

摘要

本文提出了一种基于循环条件Wasserstein生成对抗网络(AEF-WCSI-6G-RCWGAN)的第六代(6G)通信无线信道状态信息的高级估计和反馈方法。基于深度学习(DL)的基于循环条件Wasserstein生成对抗网络(RCWGAN)的信道估计算法估计了6G信道参数,如信道增益和接收信号的延迟,这对有效通信和资源分配至关重要。本文的主要目的是讨论基于DL的无线信道估计和信道状态信息(CSI)反馈中的关键问题和可能的解决方案,包括DL模型选择、训练数据采集和6G神经网络设计。基于深度学习的信道估计器改进了预测的信道输出,随后用于提高通信方案的有效性和可靠性。实现了AEF-WCSI-6G-RCWGAN,并分析了检测成功率、均方误差(MSE)和归一化均方误差(NMSE)等性能指标。最后,与现有方法相比,提出的AEF-WCSI-6G-RCWGAN方法的检测成功率分别提高了30.73%、28.35%和29.62%,MSE分别降低了25.73%、28.05%和24.62%。分别研究了基于dl辅助的第六代无线信道估计和CSI反馈(WCE-CSI-6G-GAN)、用于正交频分复用(OFDM)无线系统的有效深度神经网络信道状态估计(CSE-WS-BiLSTM)和基于分布式机器学习的可重构智能表面支持无线通信下行信道估计(DCE-AWC-HDCENet)方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advanced Estimation and Feedback of Wireless Channels State Information for 6G Communication via Recurrent Conditional Wasserstein Generative Adversarial Network

Advanced Estimation and Feedback of Wireless Channels State Information for 6G Communication via Recurrent Conditional Wasserstein Generative Adversarial Network

In this manuscript, an Advanced Estimation and Feedback of Wireless Channels State Information for sixth generation (6G) Communication via Recurrent Conditional Wasserstein Generative Adversarial Network (AEF-WCSI-6G-RCWGAN) is proposed. Deep Learning (DL) based channel estimation algorithm using Recurrent Conditional Wasserstein Generative Adversarial Network (RCWGAN) is estimated the channel parameters in 6G, such as channel gains and delays from received signals, which is crucial for effective communication and resource allocation. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition and neural network design for 6G. The deep learning-dependent channel estimator refines the predicted channel output, which is subsequently used for increase the efficacy and dependability of the communication scheme. The proposed AEF-WCSI-6G-RCWGAN is implemented and the performance metrics, like Detection Success Probability, Mean Square Error (MSE), and Normalized Mean Square Error (NMSE) are analyzed. Finally, the performance of the proposed AEF-WCSI-6G-RCWGAN method achieves 30.73%, 28.35%, and 29.62% higher Detection Success Probability, 25.73%, 28.05%, and 24.62% lower MSE when compared with existing methods: towards DL-assisted wireless channel estimate and CSI feedback for sixth generation (WCE-CSI-6G-GAN), an effectual deep neural network channel state estimate for Orthogonal frequency-division multiplexing (OFDM)wireless systems (CSE-WS-BiLSTM), and distributed machine learning dependent downlink channel estimate for reconfigurable intelligent surfaces supported wireless communications (DCE-AWC-HDCENet) methods, respectively.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.90
自引率
9.50%
发文量
323
审稿时长
7.9 months
期刊介绍: The International Journal of Communication Systems provides a forum for R&D, open to researchers from all types of institutions and organisations worldwide, aimed at the increasingly important area of communication technology. The Journal''s emphasis is particularly on the issues impacting behaviour at the system, service and management levels. Published twelve times a year, it provides coverage of advances that have a significant potential to impact the immense technical and commercial opportunities in the communications sector. The International Journal of Communication Systems strives to select a balance of contributions that promotes technical innovation allied to practical relevance across the range of system types and issues. The Journal addresses both public communication systems (Telecommunication, mobile, Internet, and Cable TV) and private systems (Intranets, enterprise networks, LANs, MANs, WANs). The following key areas and issues are regularly covered: -Transmission/Switching/Distribution technologies (ATM, SDH, TCP/IP, routers, DSL, cable modems, VoD, VoIP, WDM, etc.) -System control, network/service management -Network and Internet protocols and standards -Client-server, distributed and Web-based communication systems -Broadband and multimedia systems and applications, with a focus on increased service variety and interactivity -Trials of advanced systems and services; their implementation and evaluation -Novel concepts and improvements in technique; their theoretical basis and performance analysis using measurement/testing, modelling and simulation -Performance evaluation issues and methods.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信