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CS-LTP-Spinal: a cross-layer optimized rate-adaptive image transmission system for deep-space exploration

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Abstract

Reliable and efficient image transmission is crucial for deep-space exploration. However, the extremely long distance and complex deep-space environment introduce severe design and implementation challenges. In this work, we propose a novel high-efficiency system, CS-LTP-Spinal, to address the challenges encountered in deep-space image transmission. CS-LTP-Spinal is designed to work over the Licklider transmission protocol (LTP) of the delay-tolerant network (DTN). By incorporating compressed sensing (CS) and the Spinal codes as the application and physical layer techniques, CS-LTP-Spinal can satisfy the constraints originating from resource asymmetry between the space vehicles and the ground station. To match the time-varying deep-space channels, two coarse-grained rate-adaptive transmission strategies are designed that employ different CS decompression mechanisms based on erasure-tolerant and error-tolerant decompression, respectively, to exploit the robustness of CS reconstruction to erasures and errors. Then, the rates of CS compression and Spinal coding are optimized over the application, transport, and physical layers. A semi-physical deep-space communication platform is built, and extensive simulations on the Mars-to-Earth scenario are conducted. The results demonstrate that the designed CS-LTP-Spinal system with cross-layer optimized rate-adaptive transmission strategies has significant performance advantages over its counterparts by achieving near-ideal image transmission efficiency.

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References

  1. Zhao K L, Zhang Q Y. Network protocol architectures for future deep-space internetworking. Sci China Inf Sci, 2018, 61: 040303

    Article  MathSciNet  Google Scholar 

  2. Burleigh S, Cerf V G, Crowcroft J, et al. Space for Internet and Internet for space. Ad Hoc Networks, 2014, 23: 80–86

    Article  Google Scholar 

  3. Xu G, Song Z. Effects of solar scintillation on deep space communications: challenges and prediction techniques. IEEE Wireless Commun, 2019, 26: 10–16

    Article  Google Scholar 

  4. Wan P, Chen S, Yu T, et al. A hybrid multiple copy routing algorithm in space delay-tolerant networks. Sci China Inf Sci, 2017, 60: 042301

    Article  Google Scholar 

  5. Yang G, Wang R, Burleigh S C, et al. Analysis of Licklider transmission protocol for reliable file delivery in space vehicle communications with random link interruptions. IEEE Trans Veh Technol, 2019, 68: 3919–3932

    Article  Google Scholar 

  6. Bursalioglu O Y, Caire G, Divsalar D. Joint source-channel coding for deep-space image transmission using rateless codes. IEEE Trans Commun, 2013, 61: 3448–3461

    Article  Google Scholar 

  7. Xu H Z, Chen C, Zhu M, et al. Nonbinary LDPC cycle codes: efficient search, design, and code optimization. Sci China Inf Sci, 2018, 61: 089303

    Article  MathSciNet  Google Scholar 

  8. Dai S W, Wu J, Sun H X, et al. Chang’E-3 lunar rover’s scientific payloads. Chin J Space Sci, 2014, 34: 332–340

    Google Scholar 

  9. Kiely A, Klimesh M. The ICER progressive wavelet image compressor. IPN Progress Report, 2003, 42: 155

    Google Scholar 

  10. Donoho D L. Compressed sensing. IEEE Trans Inform Theor, 2006, 52: 1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  11. Perry J, Iannucci P A, Fleming K E, et al. Spinal codes. ACM SIGCOMM Comput Commun Rev, 2012, 42: 49–60

    Article  Google Scholar 

  12. Amerini I, Uricchio T, Ballan L, et al. Localization of JPEG double compression through multi-domain convolutional neural Networks. In: Proceedings of IEEE CVPR Workshop on Media Forensics, Honolulu, 2017. 1865–1871

  13. Liu F, Ahanonu E, Marcellin M W, et al. Visibility thresholds in reversible JPEG2000 compression. In: Proceedings of Data Compression Conference (DCC), Snowbird, 2017. 450

  14. Shi L, Jiao J, Sabbagh A, et al. Integration of reed-solomon codes to Licklider transmission protocol (LTP) for space DTN. IEEE Aerosp Electron Syst Mag, 2017, 32: 48–55

    Article  Google Scholar 

  15. Viterbi A. Convolutional codes and their performance in communication systems. IEEE Trans Comm Technol, 1971, 19: 751–772

    Article  MathSciNet  Google Scholar 

  16. Andrews K S, Divsalar D, Dolinar S, et al. The development of turbo and LDPC codes for deep-space applications. Proc IEEE, 2007, 95: 2142–2156

    Article  Google Scholar 

  17. Perry J, Balakrishnan H, Shah D, et al. Rateless Spinal codes. In: Proceedings of the 10th ACM Workshop on Hot Topics in Networks, 2011. 1–6

  18. Gabay A, Kieffer M, Duhamel P. Joint source-channel coding using real BCH codes for robust image transmission. IEEE Trans Image Process, 2007, 16: 1568–1583

    Article  MathSciNet  Google Scholar 

  19. Rajanna A, Dettmann C P. Adaptive transmission in cellular networks: fixed-rate codes with power control versus physical layer rateless codes. IEEE Trans Wireless Commun, 2019, 18: 3005–3018

    Article  Google Scholar 

  20. Xiang S, Cai L. Transmission control for compressive sensing video over wireless channel. IEEE Trans Wireless Commun, 2013, 12: 1429–1437

    Article  Google Scholar 

  21. Xu Y, Li J, Lin X, et al. An optimal bit-rate allocation algorithm to improve transmission efficiency of images in deep space exploration. China Commun, 2020, 17: 94–100

    Article  Google Scholar 

  22. Peron G, Brante G, Souza R D, et al. Physical and MAC cross-layer analysis of energy-efficient cooperative MIMO networks. IEEE Trans Commun, 2018, 66: 1940–1954

    Article  Google Scholar 

  23. Zhao X, Chen W. Non-orthogonal multiple access for delay-sensitive communications: a cross-layer approach. IEEE Trans Commun, 2019, 67: 5053–5068

    Article  Google Scholar 

  24. Li C, Wang J, Li M. Data transmission optimization algorithm for network utility maximization in wireless sensor networks. Int J Distrib Sens Netw, 2016, 12: 155014771667064

    Article  Google Scholar 

  25. Dong D, Wu S, Li D, et al. Joint source-channel rate allocation with unequal error protection for space image transmission. Int J Distrib Sens Netw, 2017, 13: 155014771772114

    Article  Google Scholar 

  26. Luo J, Wu S, Xu S, et al. A cross-layer image transmission scheme for deep space exploration. In: Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Toronto, 2017. 1–5

  27. Colonnese S, Rinauro S, Cusani R, et al. The restricted isometry property of the Radon-like CS matrix. In: Proceedings of IEEE International Workshop on Multimedia Signal Processing (MMSP), Pula, 2013. 248–253

  28. Yang D, Sun J. BM3D-Net: a convolutional neural network for transform-domain collaborative filtering. IEEE Signal Process Lett, 2018, 25: 55–59

    Article  Google Scholar 

  29. Metzler C A, Maleki A, Baraniuk R G. From denoising to compressed sensing. IEEE Trans Inform Theor, 2016, 62: 5117–5144

    Article  MathSciNet  MATH  Google Scholar 

  30. Xu S, Wu S, Luo J, et al. Low complexity decoding for spinal codes: sliding feedback decoding. In: Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Toronto, 2017. 1–5

  31. Sung U I, Gao L J. CFDP performance over weather-dependent Ka-band channel. In: Proceedings of SpaceOps Conference, Rome, 2006. 1–5

  32. Gilbert E N. Capacity of a burst-noise channel. Bell Syst Technical J, 1960, 39: 1253–1265

    Article  MathSciNet  Google Scholar 

  33. Huang K, Liang C, Ma X, et al. Unequal error protection by partial superposition transmission using low-density parity-check codes. IET Commun, 2014, 8: 2348–2355

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61871147, 61831008, 62071141, 61525103, 61371102) and Guangdong Special Support Plan (Grant No. 2016TX03X226).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China

    Shaohua Wu, Dongqing Li, Jian Jiao & Qinyu Zhang

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  1. Shaohua Wu
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  2. Dongqing Li
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  3. Jian Jiao
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  4. Qinyu Zhang
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Correspondence to Shaohua Wu.

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Wu, S., Li, D., Jiao, J. et al. CS-LTP-Spinal: a cross-layer optimized rate-adaptive image transmission system for deep-space exploration. Sci. China Inf. Sci. 65, 112303 (2022). https://doi.org/10.1007/s11432-020-3164-5

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  • DOI: https://doi.org/10.1007/s11432-020-3164-5

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