Survey on the research progress of generative adversarial networks for 6G

Chanyuan MENG; Ke XIONG; Bo GAO; Yu ZHANG; Pingyi FAN

doi:10.11959/j.issn.2096-3750.2024.00369

您当前的位置：

首页 >

文章列表页 >

Survey on the research progress of generative adversarial networks for 6G

Theory and Technology | 更新时间：2024-06-05

- Survey on the research progress of generative adversarial networks for 6G
- Chinese Journal on Internet of Things Vol. 8, Issue 1, Pages: 1-16(2024)
- 作者机构：
  
  1. 北京交通大学计算机与信息技术学院，北京 100044
  2. 北京交通大学高速铁路网络管理教育部工程研究中心，北京 100044
  3. 国网能源研究院有限公司，北京 102209
  4. 清华大学电子工程系，北京 100084
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2024.00369
  CLC： TN92
- Revised：2023-09-20，
  
  Online First：2024-03，
  
  Published：30 March 2024
- 稿件说明：
移动端阅览
Chanyuan MENG, Ke XIONG, Bo GAO, et al. Survey on the research progress of generative adversarial networks for 6G[J]. Chinese Journal on Internet of Things, 2024, 8(1): 1-16.
DOI：

Chanyuan MENG, Ke XIONG, Bo GAO, et al. Survey on the research progress of generative adversarial networks for 6G[J]. Chinese Journal on Internet of Things, 2024, 8(1): 1-16. DOI： 10.11959/j.issn.2096-3750.2024.00369.

摘要

人工智能（AI

artificial intelligence）与通信技术的深度融合是6G网络的典型特征。一方面，AI为6G网络发展注入了新动力，能够有效利用网络运行产生的历史数据，使网络具备自维护、自优化的功能，加速了网络智能化进程。另一方面，6G网络丰富的场景和大规模的物联设备入网应用为AI提供了广阔的应用渠道和海量的训练数据，使AI能够更好地训练和部署，充分发挥AI的内在优势，为用户提供更加优质的智能服务。尽管如此，在一些实际应用中，受复杂环境的影响，存在数据样本收集困难、收集成本高和样本普适性不足等问题，难以充分发挥AI的性能优势。为此，学术界和工业界将生成对抗网络（GAN

generative adversarial network）引入无线网络的设计中，利用GAN强大的特征学习和特征表达能力产生大量模拟实际的生成样本，实现无线数据库的扩充，从而有效提升面向无线网络的AI模型的泛化能力。由于其优秀的性能表现，以GAN为代表的生成式模型在无线网络领域受到越来越多的关注，并迅速发展成为6G网络新的研究热点。首先，综述了GAN的原理及其改进衍生模型，对各种衍生模型的框架及优缺点进行了分析归纳；然后，综述了这些模型在无线网络领域的研究及应用现状；最后，面向6G网络的需求展望了GAN在6G网络中的研究趋势，为未来的研究提供了一些有价值的探索。

Abstract

The deep integration of artificial intelligence (AI) and communication technology is the typical feature of the 6G network.On the one hand

AI injects new vitality into the development of the 6G network

which can effectively use the data generated by the historical operation of the network.It enables the network to be self-maintained and selfoptimized

and accelerates the process of network intelligence.On the other hand

the rich scenarios and IoT devices of the 6G network provide a large number of application fields and massive data for AI.These can enable the better deployment of AI

fully demonstrate the performance advantages of AI

and provide high-quality services for users.However

in practice

it is difficult to give full play to the performance advantages of AI due to the difficulty of sample collection

high cost of the collection

and lack of universality which caused by the complexity of the environment.Therefore

academia and industry introduce generative adversarial network (GAN) into the design of wireless networks.The powerful feature learning and feature expression ability of GAN can generate a large number of generated samples

which realizes the expansion of the wireless database.The introduction of GAN can effectively improve the generalization ability of AI models for wireless networks.Owing to the excellent performance of GAN

the generative model represented by GAN has attracted increased attention in the field of wireless networks

and rapidly became the new research hotspot of 6G networks.Firstly

the principle of GAN and its different versions of improved derived models were summarized.Then

the framework

advantages and disadvantages of each model were analyzed.Secondly

the research and application status of these models in wireless networks were reviewed.Finally

the research trends of GAN were proposed for the 6G network requirements

which provided some valuable exploration for future research.

关键词

Keywords

references

ALWIS C D , KALLA A , PHAM Q V , et al . Survey on 6G frontiers:trends,applications,requirements,technologies and future research [J ] . IEEE Open Journal of the Communications Society , 2021 , 2 : 836 - 886 .

张彤 , 任奕璟 , 闫实 , 等 . 人工智能驱动的6G网络:智慧内生 [J ] . 电信科学 , 2020 , 36 ( 9 ): 14 - 22 .

ZHANG T , REN Y J , YAN S , et al . Artificial intelligence driven 6G networks:endogenous intelligence [J ] . Telecommunications Science , 2020 , 36 ( 9 ): 14 - 22 .

张学工 . 关于统计学习理论与支持向量机 [J ] . 自动化学报 , 2000 , 26 ( 1 ): 32 - 42 .

ZHANG X G . Introduction to statistical learning theory and support vector machines [J ] . Acta Automatica Sinica , 2000 , 26 ( 1 ): 32 - 42 .

VERT J P , TSUDA K , SCHÖLKOPF B . A primer on KernelMethods [J ] . Kernel Methods in Computational Biology , 2012 : 35 - 70 .

ALSMADI M K , OMAR K B , NOAH S A , et al . Performance comparison of multi-layer perceptron (back propagation,delta rule and perceptron) algorithms in neural networks [C ] // Proceedings of 2009 IEEE International Advance Computing Conference . Piscataway:IEEE Press , 2009 : 296 - 299 .

DONG Y D , GUO H P , ZHI W M , et al . Class imbalance oriented logistic regression [C ] // Proceedings of 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery . Piscataway:IEEE Press , 2014 : 187 - 192 .

程学旗 , 靳小龙 , 王元卓 , 等 . 大数据系统和分析技术综述 [J ] . 软件学报 , 2014 , 25 ( 9 ): 1889 - 1908 .

CHENG X Q , JIN X L , WANG Y Z , et al . Survey on big data system and analytic technology [J ] . Journal of Software , 2014 , 25 ( 9 ): 1889 - 1908 .

BENGIO Y . Learning deep architectures for AI [J ] . Foundations and Trends ® in Machine Learning , 2009 , 2 ( 1 ): 1 - 127 .

GOODFELLOW I , BENGIO Y , COURVILLE A . Deep learning [M ] . Cambridge,Massachusetts : The MIT Press , 2016 .

LIU J C , DENG R C , ZHOU S , et al . Seeing the unobservable:channel learning for wireless communication networks [C ] // Proceedings of 2015 IEEE Global Communications Conference (GLOBECOM) . Piscataway:IEEE Press , 2016 : 1 - 6 .

LI J L , ZHANG Q , XIN X J , et al . Deep learning-based massive MIMO CSI feedback [C ] // Proceedings of 2019 18th International Conference on Optical Communications and Networks (ICOCN) . Piscataway:IEEE Press , 2019 : 1 - 3 .

KUMARESAN S P , TAN C K , NG Y H . Efficient user clustering using a low-complexity artificial neural network (ANN) for 5G NOMA systems [J ] . IEEE Access , 2020 , 8 : 179307 - 179316 .

徐潇潇 , 谢林柏 , 彭力 . 基于WiFi信号强度特征的室内定位系统设计 [J ] . 计算机工程 , 2015 , 41 ( 4 ): 87 - 91 .

XU X X , XIE L B , PENG L . Design of indoor positioning system based on WiFi signal intensity characteristic [J ] . Computer Engineering , 2015 , 41 ( 4 ): 87 - 91 .

SOMMER R , PAXSON V . Outside the closed world:on using machine learning for network intrusion detection [C ] // Proceedings of 2010 IEEE Symposium on Security and Privacy . Piscataway:IEEE Press , 2010 : 305 - 316 .

KIM H . Design and optimization for 5G wireless communications [M ] . John Wiley ＆ Sons , 2020 .

殷瑞刚 , 魏帅 , 李晗 , 等 . 深度学习中的无监督学习方法综述 [J ] . 计算机系统应用 , 2016 , 25 ( 8 ): 1 - 7 .

YIN R G , WEI S , LI H , et al . Introduction of unsupervised learning methods in deep learning [J ] . Computer Systems ＆ Applications , 2016 , 25 ( 8 ): 1 - 7 .

ZHENG H N , XIONG K , SUN M Y , et al . Age-energy efficiency in WPCNs:a deep reinforcement learning approach [C ] // Proceedings of IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Piscataway:IEEE Press , 2022 : 1 - 2 .

LU Y P , ZHANG C T , CHEN D E , et al . Handover enhancement in high-speed railway 5G networks:a LSTM-based prediction method [C ] // Proceedings of 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) . Piscataway:IEEE Press , 2022 : 1 - 6 .

REKKAS V P , SOTIROUDIS S , SARIGIANNIDIS P , et al . Unsupervised machine learning in 6G networks-state-of-the-art and future trends [C ] // Proceedings of 2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST) . Piscataway:IEEE Press , 2021 : 1 - 4 .

ZHANG R C , XIONG K , LU Y , et al . Joint coordinated beamforming and power splitting ratio optimization in MU-MISO SWIPTenabled HetNets:a multi-agent DDQN-based approach [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 40 ( 2 ): 677 - 693 .

ZHANG X , LUAN N , LU Y P , et al . AoI-based joint user scheduling and transmit power optimization in high-speed railway mobile networks [C ] // Proceedings of 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) . Piscataway:IEEE Press , 2022 : 1 - 6 .

ZHANG R C , XIONG K , TIAN X C , et al . Inverse reinforcement learning meets power allocation in multi-user cellular networks [C ] // Proceedings of IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Piscataway:IEEE Press , 2022 : 1 - 2 .

LIU L S , XIONG K , CAO J , et al . Average AoI minimization in UAV-assisted data collection with RF wireless power transfer:a deep reinforcement learning scheme [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 7 ): 5216 - 5228 .

ZHANG R C , XIONG K , GUO W , et al . Q-learning-based adaptive power control in wireless RF energy harvesting heterogeneous networks [J ] . IEEE Systems Journal , 2021 , 15 ( 2 ): 1861 - 1872 .

ZHANG R C , XIONG K , LU Y , et al . Energy efficiency maximization in RIS-assisted SWIPT networks with RSMA:a PPO-based approach [J ] . IEEE Journal on Selected Areas in Communications , 2023 , 41 ( 5 ): 1413 - 1430 .

MERCHANT K , NOUSAIN B . Securing IoT RF fingerprinting systems with generative adversarial networks [C ] // Proceedings of MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM) . Piscataway:IEEE Press , 2020 : 584 - 589 .

GOODFELLOW I , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial networks [J ] . Communications of the ACM , 2020 , 63 ( 11 ): 139 - 144 .

CAO Y J , JIA L L , CHEN Y X , et al . Recent advances of generative adversarial networks in computer vision [J ] . IEEE Access , 2018 , 7 : 14985 - 15006 .

MENG D L , WU B Y , LIU N H , et al . Semi-supervised deep learning seismic impedance inversion using generative adversarial networks [C ] // Proceedings of IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium . Piscataway:IEEE Press , 2021 : 1393 - 1396 .

DONAHUE C , LI B , PRABHAVALKAR R . Exploring speech enhancement with generative adversarial networks for robust speech recognition [C ] // Proceedings of 2018 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP) . Piscataway:IEEE Press , 2018 : 5024 - 5028 .

LOPEZ R , REGIER J , JORDAN M I , et al . Information constraints on auto-encoding variational bayes [J ] . Advances in neural information processing systems , 2018 .

BOX G E P , JENKINS G M , REINSEL G C , et al . Time series analysis:forecasting and control [M ] . John Wiley ＆ Sons , 2015 .

LI Y , SWERSKY K , ZEMEL R . Generative moment matching networks [C ] // International Conference on Machine Learning . PMLR , 2015 : 1718 - 1727 .

BISHOP C M , SVENSÉN M , WILLIAMS C K I . GTM:the generative topographic mapping [J ] . Neural Computation , 1998 , 10 ( 1 ): 215 - 234 .

BAUR M , FESL B , KOLLER M , et al . Variational autoencoder leveraged MMSE channel estimation [J ] . arXiv Print , 2022 :arXiv:2205.05345.

LI Q , XIANG Z , REN P , et al . Variational autoencoder based receiver for orthogonal time frequency space modulation [J ] . Digital Signal Processing , 2021 ( 117 ): 103170 .

ZHAO T , LI F . Variational-autoencoder signal detection for MIMO-OFDM-IM [J ] . Digital Signal Processing , 2021 ( 118 ): 103230 .

YUAN J D , NGO H Q , MATTHAIOU M . Machine learning-based channel estimation in massive MIMO with channel aging [C ] // Proceedings of 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . Piscataway:IEEE Press , 2019 : 1 - 5 .

YUAN J D , NGO H Q , MATTHAIOU M . Machine learning-based channel prediction in massive MIMO with channel aging [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 5 ): 2960 - 2973 .

LI C L , CHANG W C , CHENG Y , et al . MMD GAN:towards deeper understanding of moment matching network [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York:ACM Press , 2017 : 2200 - 2210 .

PARK S W , KWON J . Sphere generative adversarial network based on geometric moment matching [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 4287 - 4296 .

GAO H C , HUANG H . Joint generative moment-matching network for learning structural latent code [C ] // Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence . California:International Joint Conferences on Artificial Intelligence Organization , 2018 .

BISHOP C M , SVENSÉN M , WILLIAMS C K I . Developments of the generative topographic mapping [J ] . Neurocomputing , 1998 , 21 ( 1/2/3 ): 203 - 224 .

KIREEVA N , BASKIN I I , GASPAR H A , et al . Generative topographic mapping (GTM):universal tool for data visualization,structure-activity modeling and dataset comparison [J ] . Molecular Informatics , 2012 , 31 ( 3/4 ): 301 - 312 .

KARRAS T , AILA , LAINE S , et al . Progressive growing of GANs for improved quality,stability,and variation [J ] . arXiv Print , 2017 :arXiv:1710.10196.

ARORA S , GE R , LIANG Y , et al . Generalization and equilibrium in generative adversarial nets (GANs) [C ] // International Conference on Machine Learning . PMLR , 2017 : 224 - 232 .

SALIMANS T , GOODFELLOW I , ZAREMBA W , et al . Improved techniques for training GANs [C ] // Proceedings of the 30th International Conference on Neural Information Processing Systems . New York:ACM Press , 2016 : 2234 - 2242 .

BAU D , ZHU J , STROBELT H , et al . GAN dissection:visualizing and understanding generative adversarial networks [J ] . arXiv Print , 2018 :arXiv:1811.10597.

ARORA S , ZHANG Y . Do GANs actually learn the distribution? an empirical study [J ] . arXiv Print , 2017 :arXiv:1706.08224.

MIRZA M , OSINDERO S . Conditional generative adversarial nets [J ] . arXiv Print , 2014 :arXiv:1411.1784.

付兴兵 , 刘光远 . 粒子群多层感知器在地震预报中的应用研究 [J ] . 湖南工程学院学报(自然科学版) , 2007 , 17 ( 1 ): 23 - 26 .

FU X B , LIU G Y . Research on application of earthquake prediction using neural networks based on particle swarm optimization [J ] . Journal of Hunan Institute of Engineering (Natural Science Edition) , 2007 , 17 ( 1 ): 23 - 26 .

周飞燕 , 金林鹏 , 董军 . 卷积神经网络研究综述 [J ] . 计算机学报 , 2017 , 40 ( 6 ): 1229 - 1251 .

ZHOU F Y , JIN L P , DONG J . Review of convolutional neural network [J ] . Chinese Journal of Computers , 2017 , 40 ( 6 ): 1229 - 1251 .

RADFORD A , METZ L , CHINTALA S . Unsupervised representation learning with deep convolutional generative adversarial networks [J ] . arXiv Print , 2015 :arXiv:1511.06434.

CHEN X , DUAN Y , HOUTHOOFT R , et al . InfoGAN:interpretable representation learning by information maximizing generative adversarial nets [C ] // Proceedings of the 30th International Conference on Neural Information Processing Systems . New York:ACM Press , 2016 : 2180 - 2188 .

HUANG X , LI Y X , POURSAEED O , et al . Stacked generative adversarial networks [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2017 : 1866 - 1875 .

ODENA A , OLAH C , SHLENS J . Conditional image synthesis with auxiliary classifier GANs [C ] // Proceedings of the 34th International Conference on Machine Learning - Volume 70 . New York:ACM Press , 2017 : 2642 - 2651 .

ARJOVSKY M , CHINTALA S , BOTTOU L . Wasserstein Gan [J ] . arXiv Print , 2017 :arXiv:1701.07875.

GULRAJANI I , AHMED F , ARJOVSKY M , et al . Improved training of Wasserstein GANs [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York:ACM Press , 2017 : 5769 - 5779 .

SHE R , FAN P Y , LIU X Y , et al . Interpretable generative adversarial networks with exponential function [J ] . IEEE Transactions on Signal Processing , 2021 ( 69 ): 3854 - 3867 .

SHE R , LIU S Y , FAN P Y . Amplifying inter-message distance:on information divergence measures in big data [J ] . IEEE Access , 2017 , 5 : 24105 - 24119 .

O'SHEA T J , ROY T , WEST N , et al . Physical layer communications system design over-the-air using adversarial networks [C ] // Proceedings of 2018 26th European Signal Processing Conference (EUSIPCO) . Piscataway:IEEE Press , 2018 : 529 - 532 .

ZHANG D Y , ZHAO J H , YANG L H , et al . Generative adversarial network-based channel estimation in high-speed mobile scenarios [C ] // Proceedings of 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2021 : 1 - 5 .

WEI Y , ZHAO M M , ZHAO M J . Channel distribution learning:model-driven GAN-based channel modeling for IRS-aided wireless communication [C ] // Proceedings of IEEE Transactions on Communications . Piscataway:IEEE Press , 2022 : 4482 - 4497 .

YANG Y , LI Y , ZHANG W X , et al . Generative-adversarialnetwork-based wireless channel modeling:challenges and opportunities [J ] . IEEE Communications Magazine , 2019 , 57 ( 3 ): 22 - 27 .

CHEN W B , WANG D S , WANG D D , et al . Generative adversarial network-based channel modeling for free-space optical communication [C ] // Proceedings of 26th Optoelectronics and Communications Conference.Washington,D.C . :Optica Publishing Group , 2021 : 1 - 3 .

ZHAO S Q , FANG Y , QIU L . Deep learning-based channel estimation with SRGAN in OFDM Systems [C ] // Proceedings of 2021 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway:IEEE Press , 2021 : 1 - 6 .

ZHANG Y H , ZHANG Z Y , ZHANG Y X , et al . Blind denoiserbased beamspace channel estimation with GAN in millimeter- wave systems [C ] // Proceedings of 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) . Piscataway:IEEE Press , 2021 : 1 - 5 .

SUN L , WANG Y W , SWINDLEHURST A L , et al . Generativeadversarial-network enabled signal detection for communication systems with unknown channel models [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 1 ): 47 - 60 .

LI X F , ALKHATEEB A , TEPEDELENLIOĞLU C . Generative adversarial estimation of channel covariance in vehicular millimeter wave systems [C ] // Proceedings of 2018 52nd Asilomar Conference on Signals,Systems,and Computers . Piscataway:IEEE Press , 2019 : 1572 - 1576 .

YE H , LI G Y , JUANG B H F , et al . Channel agnostic end-to-end learning based communication systems with conditional GAN [C ] // Proceedings of 2018 IEEE Globecom Workshops (GC Wkshps) . Piscataway:IEEE Press , 2019 : 1 - 5 .

YANG H , NIU Z K , XIAO S L , et al . Fast and accurate optical fiber channel modeling using generative adversarial network [J ] . Journal of Lightwave Technology , 2021 , 39 ( 5 ): 1322 - 1333 .

ZHANG Q Q , FERDOWSI A , SAAD W , et al . Distributed conditional generative adversarial networks (GANs) for data-driven millimeter wave communications in UAV networks [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 3 ): 1438 - 1452 .

DONG Y D , WANG H X , YAO Y D . Channel estimation for onebit multiuser massive MIMO using conditional GAN [J ] . IEEE Communications Letters , 2021 , 25 ( 3 ): 854 - 858 .

BANERJEE B , ELLIOTT R C , KRZYMIEŃ W A , , et al . Downlink channel estimation for FDD massive MIMO using conditional generative adversarial networks [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 1 ): 122 - 137 .

YE M , ZHANG H , WANG J B . Channel estimation for intelligent reflecting surface aided wireless communications using conditional GAN [J ] . IEEE Communications Letters , 2022 , 26 ( 10 ): 2340 - 2344 .

BALEVI E , DOSHI A , JALAL A , et al . High dimensional channel estimation using deep generative networks [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 1 ): 18 - 30 .

DOSHI A , BALEVI E , ANDREWS J G . Compressed representation of high dimensional channels using deep generative networks [C ] // Proceedings of 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . Piscataway:IEEE Press , 2020 : 1 - 5 .

HU T Y , HUANG Y , ZHU Q M , et al . Channel estimation enhancement with generative adversarial networks [J ] . IEEE Transactions on Cognitive Communications and Networking , 2021 , 7 ( 1 ): 145 - 156 .

BALEVI E , ANDREWS J G . Wideband channel estimation with a generative adversarial network [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 5 ): 3049 - 3060 .

O'SHEA T J , ROY T , WEST N . Approximating the void:learning stochastic channel models from observation with variational generative adversarial networks [C ] // Proceedings of 2019 International Conference on Computing,Networking and Communications (ICNC) . Piscataway:IEEE Press , 2019 : 681 - 686 .

ROY D , MUKHERJEE T , CHATTERJEE M , et al . RFAL:adversarial learning for RF transmitter identification and classification [J ] . IEEE Transactions on Cognitive Communications and Networking , 2020 , 6 ( 2 ): 783 - 801 .

USAMA M , ASIM M , LATIF S , et al . Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems [C ] // Proceedings of 2019 15th International Wireless Communications ＆ Mobile Computing Conference (IWCMC) . Piscataway:IEEE Press , 2019 : 78 - 83 .

SHI Y , DAVASLIOGLU K , SAGDUYU Y E . Generative adversarial network in the air:deep adversarial learning for wireless signal spoofing [J ] . IEEE Transactions on Cognitive Communications and Networking , 2021 , 7 ( 1 ): 294 - 303 .

BANAEIZADEH F , BARBEAU M , GARCIA-ALFARO J , et al . Pilot contamination attack detection in 5G massive MIMO systems using generative adversarial networks [C ] // Proceedings of 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) . Piscataway:IEEE Press , 2021 : 479 - 484 .

HAN H , CUI L , LI W , et al . Radio frequency fingerprint based wireless transmitter identification against malicious attacker:an adversarial learning approach [C ] // Proceedings of 2020 International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2020 : 310 - 315 .

HAN H , WANG X M , GU F L , et al . Better late than never:GANenhanced dynamic anti-jamming spectrum access with incomplete sensing information [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 8 ): 1800 - 1804 .

ERPEK T , SAGDUYU Y E , SHI Y . Deep learning for launching and mitigating wireless jamming attacks [J ] . IEEE Transactions on Cognitive Communications and Networking , 2019 , 5 ( 1 ): 2 - 14 .

YANG J , LI T , LIANG G , et al . A simple recurrent unit model based intrusion detection system with DCGAN [J ] . IEEE Access , 2019 , 7 : 83286 - 83296 .

FERDOWSI A , SAAD W . Generative adversarial networks for distributed intrusion detection in the internet of things [C ] // Proceedings of 2019 IEEE Global Communications Conference (GLOBECOM) . Piscataway:IEEE Press , 2020 : 1 - 6 .

GONG J L , XU X D , LEI Y K . Unsupervised specific emitter identification method using radio-frequency fingerprint embedded InfoGAN [J ] . IEEE Transactions on Information Forensics and Security , 2020 ( 15 ): 2898 - 2913 .

GONG J L , XU X D , QIN Y F , et al . A generative adversarial network based framework for specific emitter characterization and identification [C ] // Proceedings of 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2019 : 1 - 6 .

SHE R , FAN P Y . From MIM-based GAN to anomaly detection:event probability influence on generative adversarial networks [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 19 ): 18589 - 18606 .

HAN Z J , GUO L C , LU Z M , et al . Deep adaptation networks based gesture recognition using commodity WiFi [C ] // Proceedings of 2020 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway:IEEE Press , 2020 : 1 - 7 .

WANG J , ZHANG L , WANG C C , et al . Device-free human gesture recognition with generative adversarial networks [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 8 ): 7678 - 7688 .

ZHANG J J , SHI Z G . Deformable deep convolutional generative adversarial network in microwave based hand gesture recognition system [C ] // Proceedings of 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway:IEEE Press , 2017 : 1 - 6 .

NABATI M , NAVIDAN H , SHAHBAZIAN R , et al . Using synthetic data to enhance the accuracy of fingerprint-based localization:a deep learning approach [J ] . IEEE Sensors Letters , 2020 , 4 ( 4 ): 1 - 4 .

TRAN D H , CHUNG B , JANG Y M . GAN-based data augmentation for UWB NLOS identification using machine learning [C ] // Proceedings of 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) . Piscataway:IEEE Press , 2022 : 417 - 420 .

NJIMA W , CHAFII M , SHUBAIR R M . GAN based data augmentation for indoor localization using labeled and unlabeled data [C ] // Proceedings of 2021 International Balkan Conference on Communications and Networking (BalkanCom) . Piscataway:IEEE Press , 2021 : 36 - 39 .

CHEN L F , ZHANG S T , TAN H B , et al . Progressive RSS data augmenter with conditional adversarial networks [J ] . IEEE Access , 2020 , 8 : 26975 - 26983 .

LI Q Y , QU H , LIU Z , et al . AF-DCGAN:amplitude feature deep convolutional GAN for fingerprint construction in indoor localization systems [J ] . IEEE Transactions on Emerging Topics in Computational Intelligence , 2021 , 5 ( 3 ): 468 - 480 .

LIM C , PAEK J . Cost reduction in fingerprint-based indoor localization using generative adversarial network [C ] // Proceedings of 2021 International Conference on Information and Communication Technology Convergence (ICTC) . Piscataway:IEEE Press , 2021 : 1024 - 1026 .

LI Q Y , QU H , LIU Z , et al . Wavelet transform DC-GAN for diversity promoted fingerprint construction in indoor localization [C ] // Proceedings of 2018 IEEE Globecom Workshops (GC Wkshps) . Piscataway:IEEE Press , 2019 : 1 - 7 .

FERRAG M A , MAGLARAS L A , JANICKE H , et al . Authentication protocols for Internet of things:a comprehensive survey [J ] . Security and Communication Networks , 2017 : 1 - 41 .

栾宁 , 熊轲 , 张煜 , 等 . 6G:典型应用、关键技术与面临挑战 [J ] . 物联网学报 , 2022 , 6 ( 1 ): 29 - 43 .

LUAN N , XIONG K , ZHANG Y , et al . 6G:typical applications,key technologies and challenges [J ] . Chinese Journal on Internet of Things , 2022 , 6 ( 1 ): 29 - 43 .

张彦 , 卢云龙 . 数字孪生边缘网络 [J ] . 中兴通讯技术 , 2023 .

ZHANG Y , LU Y L . Digital twin edge networks [J ] . ZTE Technology Journal , 2023 .

BEI Y , XIN L , SHENGNAN L , et al . Intelligent 6G wireless network with multi-dimensional information perception [EB ] . 2023 .

Views

494

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lightweight parallel denoising network-based OTFS channel estimation algorithm

Robust secure transmission design for spectral coexistence in satellite and terrestrial network

Channel estimation for ambient Internet of things

Channel estimation for double IRS-assisted millimeter wave MIMO systems based on tensor decomposition and manifold optimization

NOMA-based secure computation offloading in marine Internet of things

Related Author

XIONG Jun

HAN Xueqing

LIU Xiaoran

ZHANG Hui

ZHAO Haitao

WEI Jibo

JI Mingyi

WANG Zining

Related Institution

School of Internet of Things, Nanjing University of Posts and Telecommunications

College of Electronic Science and Technology, National University of Defense Technology

ZTE Nanjing Jiangjunshan R&D Center

Nanjing University of Posts and Telecommunications, School of Telecommunications and Information Engineering

The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University

⁰