Two-View Image Semantic Cooperative Nonorthogonal Transmission in Distributed Edge Networks
Authors: 
Wei Wang, Donghong Cai, Zhicheng Dong, Lisu Yu, Yanqing Xu, Zhiquan Liu Academic Editor: Weiwei JiangAuthors Info & Claims
Abstract
With the wide application of deep learning (DL) across various fields, deep joint source–channel coding (DeepJSCC) schemes have emerged as a new coding approach for image transmission. Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two-view distributed cooperative DeepJSCC (two-view‐DC-DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two-view‐DC-DeepJSCC with information disentanglement (two-view‐DC-DeepJSCC-D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two-view‐DC-DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two-view‐DC-DeepJSCC-D effectively captures both common and private information from two-view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two-view‐DC-DeepJSCC-D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two-view‐DC-DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two-view‐DC-DeepJSCC-D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two-view‐DC-DeepJSCC-D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two-view‐DC-DeepJSCC-D exhibits stronger robustness across various signal-to-noise ratios.
References
[1]
G. K. Wallace, “The JPEG Still Picture Compression Standard,” Communications of the ACM, vol. 34, no. 4, pp. 30–44, 1991.
[2]
C. Christopoulos, A. Skodras, and T. Ebrahimi, “The JPEG2000 Still Image Coding System: An Overview,” IEEE Transactions on Consumer Electronics, vol. 46, no. 4, pp. 1103–1127, 2000.
[3]
J. Chen, A. Dholakia, E. Eleftheriou, M. P. Fossorier, and X.-Y. Hu, “Reduced-Complexity Decoding of LDPC Codes,” IEEE Transactions on Communications, vol. 53, no. 8, pp. 1288–1299, 2005.
[4]
M. Gastpar, B. Rimoldi, and M. Vetterli, “To Code, or Not to Code: Lossy Source-Channel Communication Revisited,” IEEE Transactions on Information Theory, vol. 49, no. 5, pp. 1147–1158, 2003.
[5]
F. Hekland, P. A. Floor, and T. A. Ramstad, “Shannon-Kotel-Nikov Mappings in Joint Source-Channel Coding,” IEEE Transactions on Communications, vol. 57, no. 1, pp. 94–105, 2009.
[6]
V. Kostina, Y. Polyanskiy, and S. Verd, “Joint Source-Channel Coding With Feedback,” IEEE Transactions on Information Theory, vol. 63, no. 6, pp. 3502–3515, 2017.
[7]
J. Xu, B. Ai, N. Wang, and W. Chen, “Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 260–273, 2023.
[8]
D. B. Kurka and D. Gündüz, “Deepjscc-f: Deep Joint Source-Channel Coding of Images With Feedback,” IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 1, pp. 178–193, 2020.
[9]
D. B. Kurka and D. Gunduz, “Bandwidth-Agile Image Transmission With Deep Joint Source-Channel Coding,” IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 8081–8095, 2021.
[10]
T.-Y. Tung, D. B. Kurka, M. Jankowski, and D. Gündüz, “Deepjsccq: Constellation Constrained Deep Joint Source-Channel Coding,” IEEE Journal on Selected Areas in Information Theory, vol. 10, 2022.
[11]
M. Jankowski, D. Gündüz, and K. Mikolajczyk, “Wireless Image Retrieval at the Edge,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 89–100, 2021.
[12]
W. Zhang, H. Zhang, H. Ma, H. Shao, N. Wang, and V. C. M. Leung, “Predictive and Adaptive Deep Coding for Wireless Image Transmission in Semantic Communication,” IEEE Transactions on Wireless Communications, vol. 22, no. 8, pp. 5486–5501, 2023.
[13]
L. Sun, Y. Yang, M. Chen, C. Guo, W. Saad, and H. V. Poor, “Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2628–2644, 2023.
[14]
X. Zhang, J. Shao, and J. Zhang, “LDMIC: Learning-Based Distributed Multi-View Image Coding,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 2023.
[15]
S. Wang, K. Yang, J. Dai, and K. Niu, “Distributed Image Transmission Using Deep Joint Source-Channel Coding,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5208–5212, IEEE, Singapore, May 2022.
[16]
J. Chen, J. Wang, C. Jiang, and J. Wang, “Age of Incorrect Information in Semantic Communications for NOMA Aided XR Applications,” IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 5, pp. 1093–1105, 2023.
[17]
B. Patnaik, A. Agarwal, S. Mali, G. Misra, and K. Agarwal, “A Review on Non-Orthogonal Multiple Access Technique for Emerging 5G Networks and beyond,” in 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 698–703, Trichy, India, September 2020.
[18]
Y. M. Saidutta, A. Abdi, and F. Fekri, “VAE for Joint Source-Channel Coding of Distributed Gaussian Sources over AWGN MAC,” in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5, IEEE, Atlanta, Georgia, May 2020.
[19]
W. Li, H. Liang, C. Dong, X. Xu, P. Zhang, and K. Liu, “Non-orthogonal Multiple Access Enhanced Multi-User Semantic Communication,” IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 6, pp. 1438–1453, 2023.
[20]
J. Dai, S. Wang, K. Tan et al., “Nonlinear Transform Source-Channel Coding for Semantic Communications,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2300–2316, 2022.
[21]
J. Yan, J. Huang, and C. Huang, “Deep Learning Aided Joint Source-Channel Coding for Wireless Networks,” in 2021 IEEE/CIC International Conference on Communications in China (ICCC), pp. 805–810, Hangzhou, China, July 2021.
[22]
D. Huang, F. Gao, X. Tao, Q. Du, and J. Lu, “Toward Semantic Communications: Deep Learning-Based Image Semantic Coding,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 55–71, 2023.
[23]
J. Shao, Y. Mao, and J. Zhang, “Task-oriented Communication for Multidevice Cooperative Edge Inference,” IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 73–87, 2023.
[24]
J. Xu, Y. Ren, H. Tang et al., “Multi-VAE: Learning Disentangled View-Common and View-Peculiar Visual Representations for Multi-View Clustering,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9234–9243, Montreal, Canada, October 2021.
[25]
B. Omarali, F. Palermo, K. Althoefer, M. Valle, and I. Farkhatdinov, “Tactile Classification of Object Materials for Virtual Reality Based Robot Teleoperation,” in 2022 International Conference on Robotics and Automation (ICRA), pp. 9288–9294, Philadelphia, PA, May 2022.
[26]
H. Ye, G. Y. Li, and B.-H. F. Juang, “Deep Over-the-Air Computation,” in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, pp. 1–6, Taipei Taiwan, June 2020.
[27]
S. F. Yilmaz, C. Karamanlı, and D. Gündüz, “Distributed Deep Joint Source-Channel Coding Over a Multiple Access Channel,” in ICC 2023-IEEE International Conference on Communications, pp. 1400–1405, Rome, Italy, May 2023.
[28]
J. Xu, B. Ai, W. Chen, A. Yang, P. Sun, and M. Rodrigues, “Wireless Image Transmission Using Deep Source Channel Coding with Attention Modules,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 4, pp. 2315–2328, 2022.
[29]
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. SamingerPlatz, “Central Moment Discrepancy (Cmd) for Domain-Invariant Representation Learning,” in International Conference on Learning Representations, San Juan, Puerto Rico, May 2016.
[30]
L. Song, A. Smola, A. Gretton, K. M. Borgwardt, and J. Bedo, “Supervised Feature Selection via Dependence Estimation,” in In Proceedings of the 24th International Conference on Machine Learning, pp. 823–830, Vienna, Austria, April 2007.
[31]
N. S. Altman, “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression,” The American Statistician, vol. 46, no. 3, pp. 175–185, 1992.
[32]
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum Learning,” in Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48, Montreal, Canada, June 2009.
[33]
A. Paszke, S. Gross, F. Massa et al., “Pytorch: An Imperative Style, High-Performance Deep Learning Library,” Advances in Neural Information Processing Systems, vol. 32, 2019.
[34]
J. Tilly, H. Chen, S. Cao et al., “The Variational Quantum Eigensolver: A Review of Methods and Best Practices,” Physics Reports, vol. 986, pp. 1–128, 2022.
[35]
N. Mital, E. Özyılkan, A. Garjani, and D. Gündüz, “Neural Distributed Image Compression Using Common Information,” in 2022 Data Compression Conference (DCC), pp. 182–191, Snowbird, UT, March 2022.
Index Terms
- Two-View Image Semantic Cooperative Nonorthogonal Transmission in Distributed Edge Networks
Index terms have been assigned to the content through auto-classification.
Recommendations
Coding for the nonorthogonal amplify-and-forward cooperative channel
In this paper, we consider the problem of coding for the half-duplex nonorthogonal amplify-and-forward (NAF) cooperative channel where the transmitter to relay and the interrelay links are highly reliable. We derive bounds on the diversity order of the ...
Distributed Serially Concatenated Codes for Multi-Source Cooperative Relay Networks
In this paper, we propose a distributed turbo-like coding scheme for a multi-source relay scenario where multiple sources communicate with a destination with the help of a common relay, which uses the decode-and-forward strategy and operates in half-...
Reliable Joint Source-Channel Cooperative Transmission Over Relay Networks
Reliable transmission of a discrete memoryless source to multiple destinations over a relay network is considered. Motivated by sensor network applications, it is assumed that the relays and the destinations all have access to side information ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2024 Wei Wang et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
John Wiley and Sons Ltd.
United Kingdom
Publication History
Published: 01 January 2024
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jan 2025