Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3636534.3649348acmconferencesArticle/Chapter ViewAbstractPublication PagesmobicomConference Proceedingsconference-collections
research-article

RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion

Published: 29 May 2024 Publication History

Abstract

Along with AIGC shines in CV and NLP, its potential in the wireless domain has also emerged in recent years. Yet, existing RF-oriented generative solutions are ill-suited for generating high-quality, time-series RF data due to limited representation capabilities. In this work, inspired by the stellar achievements of the diffusion model in CV and NLP, we adapt it to the RF domain and propose RF-Diffusion. To accommodate the unique characteristics of RF signals, we first introduce a novel Time-Frequency Diffusion theory to enhance the original diffusion model, enabling it to tap into the information within the time, frequency, and complex-valued domains of RF signals. On this basis, we propose a Hierarchical Diffusion Transformer to translate the theory into a practical generative DNN through elaborated design spanning network architecture, functional block, and complex-valued operator, making RF-Diffusion a versatile solution to generate diverse, high-quality, and time-series RF data. Performance comparison with three prevalent generative models demonstrates the RF-Diffusion's superior performance in synthesizing Wi-Fi and FMCW signals. We also showcase the versatility of RF-Diffusion in boosting Wi-Fi sensing systems and performing channel estimation in 5G networks.

References

[1]
3rd Generation Partnership Project (3GPP). 2023. 5G; NR; Physical channels and modulation. Technical Specification (TS) 38.211. 3rd Generation Partnership Project (3GPP). Version 17.5.0.
[2]
Zeyuan Allen-Zhu and Yuanzhi Li. 2023. Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions. In The Eleventh International Conference on Learning Representations.
[3]
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. 2021. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems 34 (2021), 17981--17993.
[4]
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, and David J Fleet. 2023. Synthetic data from diffusion models improves imagenet classification. arXiv preprint arXiv:2304.08466 (2023).
[5]
Arjun Bakshi, Yifan Mao, Kannan Srinivasan, and Srinivasan Parthasarathy. 2019. Fast and efficient cross band channel prediction using machine learning. In The 25th Annual International Conference on Mobile Computing and Networking. 1--16.
[6]
Eren Balevi and Jeffrey G Andrews. 2021. Wideband channel estimation with a generative adversarial network. IEEE Transactions on Wireless Communications 20, 5 (2021), 3049--3060.
[7]
Yoshiaki Bando, Kouhei Sekiguchi, and Kazuyoshi Yoshii. 2020. Adaptive Neural Speech Enhancement with a Denoising Variational Autoencoder. In INTERSPEECH.
[8]
Michael Baur, Benedikt Fesl, Michael Koller, and Wolfgang Utschick. 2022. Variational autoencoder leveraged mmse channel estimation. In 2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE, 527--532.
[9]
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In International Conference on Learning Representations.
[10]
Hong Cai, Belal Korany, Chitra R Karanam, and Yasamin Mostofi. 2020. Teaching rf to sense without rf training measurements. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 4 (2020), 1--22.
[11]
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan. 2021. WaveGrad: Estimating Gradients for Waveform Generation. In International Conference on Learning Representations.
[12]
Guoxuan Chi, Zheng Yang, Jingao Xu, Chenshu Wu, Jialin Zhang, Jianzhe Liang, and Yunhao Liu. 2022. Wi-drone: wi-fi-based 6-DoF tracking for indoor drone flight control. In Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services. 56--68.
[13]
Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34 (2021), 8780--8794.
[14]
Akash S Doshi, Manan Gupta, and Jeffrey G Andrews. 2022. Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation. IEEE Journal on Selected Areas in Information Theory 3, 3 (2022), 557--573.
[15]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations.
[16]
Baris Erol, Sevgi Z Gurbuz, and Moeness G Amin. 2019. GAN-based synthetic radar micro-Doppler augmentations for improved human activity recognition. In 2019 IEEE Radar Conference (RadarConf). IEEE, 1--5.
[17]
Yuchong Gao, Guoxuan Chi, Guidong Zhang, and Zheng Yang. 2023. Wi-Prox: Proximity Estimation of Non-directly Connected Devices via Sim2Real Transfer Learning. In GLOBECOM 2023-2023 IEEE Global Communications Conference. IEEE.
[18]
Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249--256.
[19]
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong. 2023. DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. In The Eleventh International Conference on Learning Representations.
[20]
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2017. Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017).
[21]
Unsoo Ha, Junshan Leng, Alaa Khaddaj, and Fadel Adib. 2020. Food and liquid sensing in practical environments using {RFIDs}. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). 1083--1100.
[22]
Mutasem Q Hamdan and Khairi A Hamdi. 2020. Variational Autoencoders application in wireless Vehicle-to-Everything communications. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE.
[23]
Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and Xiaojuan Qi. 2023. Is synthetic data from generative models ready for image recognition?. In The Eleventh International Conference on Learning Representations.
[24]
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems, Vol. 30. Curran Associates, Inc.
[25]
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840--6851.
[26]
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. 2022. Cascaded diffusion models for high fidelity image generation. The Journal of Machine Learning Research 23, 1 (2022), 2249--2281.
[27]
Jonathan Ho and Tim Salimans. 2021. Classifier-Free Diffusion Guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications.
[28]
Chongwen Huang, George C Alexandropoulos, Alessio Zappone, Chau Yuen, and Mérouane Debbah. 2019. Deep learning for UL/DL channel calibration in generic massive MIMO systems. In ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 1--6.
[29]
Texas Instruments. 2022. Texas Instruments IWR1443BOOST. https://www.ti.com/tool/IWR1443BOOST
[30]
Liming Jiang, Bo Dai, Wayne Wu, and Chen Change Loy. 2021. Focal frequency loss for image reconstruction and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 13919--13929.
[31]
Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, et al. 2018. Towards environment independent device free human activity recognition. In Proceedings of the 24th annual international conference on mobile computing and networking. 289--304.
[32]
Florian Kaltenberger, David Gesbert, Raymond Knopp, and Marios Kountouris. 2008. Performance of multi-user MIMO precoding with limited feedback over measured channels. In IEEE GLOBECOM 2008-2008 IEEE Global Telecommunications Conference. IEEE, 1--5.
[33]
D Kinga, Jimmy Ba Adam, et al. 2015. Adam: A method for stochastic optimization. In International conference on learning representations (ICLR), Vol. 5. San Diego, California;, 6.
[34]
Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).
[35]
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2020. DiffWave: A Versatile Diffusion Model for Audio Synthesis. In International Conference on Learning Representations.
[36]
Belal Korany, Chitra R Karanam, Hong Cai, and Yasamin Mostofi. 2019. XModal-ID: Using WiFi for through-wall person identification from candidate video footage. In The 25th Annual International Conference on Mobile Computing and Networking. 1--15.
[37]
Sangyun Lee, Hyungjin Chung, Jaehyeon Kim, and Jong Chul Ye. 2022. Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis. In NeurIPS 2022 Workshop on Score-Based Methods. https://openreview.net/forum?id=KP8BrpZBbv
[38]
Zikun Liu, Gagandeep Singh, Chenren Xu, and Deepak Vasisht. 2021. FIRE: enabling reciprocity for FDD MIMO systems. In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking. 628--641.
[39]
Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In International Conference on Learning Representations.
[40]
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11461--11471.
[41]
Zhaoyang Lyu, Zhifeng Kong, Xudong Xu, Liang Pan, and Dahua Lin. 2021. A conditional point diffusion-refinement paradigm for 3d point cloud completion. arXiv preprint arXiv:2112.03530 (2021).
[42]
John W McKown and R Lee Hamilton. 1991. Ray tracing as a design tool for radio networks. IEEE Network 5, 6 (1991), 27--30.
[43]
Midjourney. 2023. Midjourney. https://www.midjourney.com/
[44]
Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved de-noising diffusion probabilistic models. In International Conference on Machine Learning. PMLR, 8162--8171.
[45]
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. 2022. GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. In Proceedings of the 39th International Conference on Machine Learning.
[46]
OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL]
[47]
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. 2018. Film: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.
[48]
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov. 2021. Grad-tts: A diffusion probabilistic model for text-to-speech. In International Conference on Machine Learning. PMLR, 8599--8608.
[49]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763.
[50]
Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).
[51]
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1, 2 (2022), 3.
[52]
Severi Rissanen, Markus Heinonen, and Arno Solin. 2023. Generative Modelling with Inverse Heat Dissipation. In The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=4PJUBT9f2Ol
[53]
Hamada Rizk, Ahmed Shokry, and Moustafa Youssef. 2019. Effectiveness of data augmentation in cellular-based localization using deep learning. In 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE.
[54]
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10684--10695.
[55]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5--9, 2015, Proceedings, Part III 18. Springer, 234--241.
[56]
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. 2022. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings. 1--10.
[57]
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. 2022. Image super-resolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 4 (2022), 4713--4726.
[58]
Clayton Shepard, Jian Ding, Ryan E Guerra, and Lin Zhong. 2016. Understanding real many-antenna MU-MIMO channels. In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 461--467.
[59]
C Shivashankar and Shane Miller. 2023. Semantic Data Augmentation With Generative Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 863--873.
[60]
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning. PMLR, 2256--2265.
[61]
Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. Advances in neural information processing systems 28 (2015).
[62]
Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning Structured Output Representation using Deep Conditional Generative Models. In Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett (Eds.), Vol. 28. Curran Associates, Inc.
[63]
Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32 (2019).
[64]
Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, Joao Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, and Christopher J Pal. 2018. Deep Complex Networks. In International Conference on Learning Representations.
[65]
Deepak Vasisht, Swarun Kumar, Hariharan Rahul, and Dina Katabi. 2016. Eliminating channel feedback in next-generation cellular networks. In Proceedings of the 2016 ACM SIGCOMM Conference. 398--411.
[66]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[67]
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13, 4 (2004), 600--612.
[68]
Eric W Weisstein. 2023. Convolution Theorem. https://mathworld.wolfram.com/ConvolutionTheorem.html
[69]
Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. 2022. Gan inversion: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).
[70]
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2022. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796 (2022).
[71]
Zheng Yang, Yi Zhang, Kun Qian, and Chenshu Wu. 2023. SLNet: A Spectrogram Learning Neural Network for Deep Wireless Sensing. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). 1221--1236.
[72]
Zheng Yang, Yi Zhang, and Qian Zhang. 2022. Rethinking fall detection with Wi-Fi. IEEE Transactions on Mobile Computing (2022).
[73]
Shuochao Yao, Ailing Piao, Wenjun Jiang, Yiran Zhao, Huajie Shao, Shengzhong Liu, Dongxin Liu, Jinyang Li, Tianshi Wang, Shaohan Hu, et al. 2019. Stfnets: Learning sensing signals from the time-frequency perspective with short-time fourier neural networks. In The World Wide Web Conference. 2192--2202.
[74]
Guidong Zhang, Guoxuan Chi, Yi Zhang, Xuan Ding, and Zheng Yang. 2023. Push the Limit of Millimeter-wave Radar Localization. ACM Transactions on Sensor Networks 19, 3 (2023), 1--21.
[75]
Xiaotong Zhang, Zhenjiang Li, and Jin Zhang. 2022. Synthesized Millimeter-Waves for Human Motion Sensing. In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. 377--390.
[76]
Xiaopeng Zhao, Zhenlin An, Qingrui Pan, and Lei Yang. 2023. NeRF2: Neural Radio-Frequency Radiance Fields. arXiv preprint arXiv:2305.06118 (2023).
[77]
Chenyu Zheng, Guoqiang Wu, and Chongxuan Li. 2023. Toward Understanding Generative Data Augmentation. arXiv preprint arXiv:2305.17476 (2023).
[78]
Yue Zheng, Yi Zhang, Kun Qian, Guidong Zhang, Yunhao Liu, Chenshu Wu, and Zheng Yang. 2019. Zero-effort cross-domain gesture recognition with Wi-Fi. In Proceedings of the ACM MobiSys.
[79]
Linqi Zhou, Yilun Du, and Jiajun Wu. 2021. 3d shape generation and completion through point-voxel diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5826--5835.

Cited By

View all
  • (2024)Commodity Wi-Fi-Based Wireless Sensing Advancements over the Past Five YearsSensors10.3390/s2422719524:22(7195)Online publication date: 10-Nov-2024
  • (2024)Deep Learning-Empowered RF Sensing in Outdoor Environments: Recent Advances, Challenges, and Future DirectionsElectronics10.3390/electronics1401012514:1(125)Online publication date: 31-Dec-2024
  • (2024)Artificial Intelligence of Things: A SurveyACM Transactions on Sensor Networks10.1145/369063921:1(1-75)Online publication date: 30-Aug-2024
  • Show More Cited By

Index Terms

  1. RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking
      December 2024
      2476 pages
      ISBN:9798400704895
      DOI:10.1145/3636534
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 29 May 2024

      Check for updates

      Badges

      Author Tags

      1. RF signal
      2. generative model
      3. time-frequency diffusion
      4. wireless sensing
      5. channel estimation

      Qualifiers

      • Research-article

      Conference

      ACM MobiCom '24
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 440 of 2,972 submissions, 15%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)1,741
      • Downloads (Last 6 weeks)185
      Reflects downloads up to 20 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Commodity Wi-Fi-Based Wireless Sensing Advancements over the Past Five YearsSensors10.3390/s2422719524:22(7195)Online publication date: 10-Nov-2024
      • (2024)Deep Learning-Empowered RF Sensing in Outdoor Environments: Recent Advances, Challenges, and Future DirectionsElectronics10.3390/electronics1401012514:1(125)Online publication date: 31-Dec-2024
      • (2024)Artificial Intelligence of Things: A SurveyACM Transactions on Sensor Networks10.1145/369063921:1(1-75)Online publication date: 30-Aug-2024
      • (2024)AirECG: Contactless Electrocardiogram for Cardiac Disease Monitoring via mmWave Sensing and Cross-domain Diffusion ModelProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785508:3(1-27)Online publication date: 9-Sep-2024
      • (2024)RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data AugmentationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596208:2(1-26)Online publication date: 15-May-2024
      • (2024)XFall: Domain Adaptive Wi-Fi-Based Fall Detection With Cross-Modal SupervisionIEEE Journal on Selected Areas in Communications10.1109/JSAC.2024.341399742:9(2457-2471)Online publication date: Sep-2024
      • (2024)MuSAC: Mutualistic Sensing and Communication for Mobile Crowdsensing2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00031(243-254)Online publication date: 23-Jul-2024

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media