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SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing (2405.15542v1)

Published 24 May 2024 in cs.NI, cs.DC, cs.LG, and eess.SP

Abstract: Low Earth Orbit satellite Internet has recently been deployed, providing worldwide service with non-terrestrial networks. With the large-scale deployment of both non-terrestrial and terrestrial networks, limited spectrum resources will not be allocated enough. Consequently, dynamic spectrum sharing is crucial for their coexistence in the same spectrum, where accurate spectrum sensing is essential. However, spectrum sensing in space is more challenging than in terrestrial networks due to variable channel conditions, making single-satellite sensing unstable. Therefore, we first attempt to design a collaborative sensing scheme utilizing diverse data from multiple satellites. However, it is non-trivial to achieve this collaboration due to heterogeneous channel quality, considerable raw sampling data, and packet loss. To address the above challenges, we first establish connections between the satellites by modeling their sensing data as a graph and devising a graph neural network-based algorithm to achieve effective spectrum sensing. Meanwhile, we establish a joint sub-Nyquist sampling and autoencoder data compression framework to reduce the amount of transmitted sensing data. Finally, we propose a contrastive learning-based mechanism compensates for missing packets. Extensive experiments demonstrate that our proposed strategy can achieve efficient spectrum sensing performance and outperform the conventional deep learning algorithm in spectrum sensing accuracy.

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References (62)
  1. X. Lin, S. Cioni, G. Charbit, N. Chuberre, S. Hellsten, and J.-F. Boutillon, “On the Path to 6G: Embracing the Next Wave of Low Earth Orbit Satellite Access,” IEEE Communications Magazine, vol. 59, no. 12, pp. 36–42, 2021.
  2. Z. Xiao, J. Yang, T. Mao, C. Xu, R. Zhang, Z. Han, and X.-G. Xia, “LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches,” IEEE Wireless Communications, 2022.
  3. N. Okati, T. Riihonen, D. Korpi, I. Angervuori, and R. Wichman, “Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry,” IEEE Transactions on Communications, vol. 68, no. 8, pp. 5120–5134, 2020.
  4. J. C. McDowell, “The Low Earth Orbit Satellite Population and Impacts of the SpaceX Starlink Constellation,” The Astrophysical Journal Letters, vol. 892, no. 2, p. L36, 2020.
  5. N. Pachler, I. del Portillo, E. F. Crawley, and B. G. Cameron, “An Updated Comparison of Four Low Earth Orbit Satellite Constellation Systems to Provide Global Broadband,” in Proc. ICC Workshop, 2021, pp. 1–7.
  6. Z. Lin, Z. Chen, Z. Fang, X. Chen, X. Wang, and Y. Gao, “FedSN: A General Federated Learning Framework over LEO Satellite Networks,” arXiv preprint arXiv:2311.01483, 2023.
  7. (2021) Spacex’s elon musk and amazon’s project kuiper stir up a war of words over satellites. [Online]. Available: https://www.geekwire.com/2021/spacexs-elon-musk-amazons-project-kuiper-stir-war-words-satellites/
  8. K. An, M. Lin, W.-P. Zhu, Y. Huang, and G. Zheng, “Outage Performance of Cognitive Hybrid Satellite–terrestrial Networks with Interference Constraint,” IEEE Transactions on Vehicular Technology, vol. 65, no. 11, pp. 9397–9404, 2016.
  9. K. An, T. Liang, G. Zheng, X. Yan, Y. Li, and S. Chatzinotas, “Performance Limits of Cognitive-uplink FSS and Terrestrial FS for Ka-band,” IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 5, pp. 2604–2611, 2018.
  10. (2023) Spectrum for 6G Explained. [Online]. Available: https://www.nokia.com/about-us/newsroom/articles/spectrum-for-6G-explained/
  11. Z. Lin, L. Wang, B. Tan, and X. Li, “Spatial-spectral Terahertz Networks,” IEEE Transactions on Wireless Communications, vol. 21, no. 6, pp. 3881–3892, 2021.
  12. Q. Zhao and B. M. Sadler, “A Survey of Dynamic Spectrum Access,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79–89, 2007.
  13. C. Liu, J. Wang, X. Liu, and Y.-C. Liang, “Deep CM-CNN for Spectrum Sensing in Cognitive Radio,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2306–2321, 2019.
  14. D. Uvaydov, S. D’Oro, F. Restuccia, and T. Melodia, “Deepsense: Fast Wideband Spectrum Sensing Through Real-time In-the-loop Deep Learning,” in Proc. INFOCOM, 2021, pp. 1–10.
  15. L. Baldesi, F. Restuccia, and T. Melodia, “Charm: Nextg Spectrum Sharing Through Data-driven Real-time O-ran Dynamic Control,” in Proc. INFOCOM, 2022, pp. 240–249.
  16. H. Zhang, J. Yang, and Y. Gao, “Machine Learning Empowered Spectrum Sensing Under a Sub-sampling Framework,” IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 8205–8215, 2022.
  17. X. Lin, S. Rommer, S. Euler, E. A. Yavuz, and R. S. Karlsson, “5G from Space: An Overview of 3GPP Non-terrestrial Networks,” IEEE Communications Standards Magazine, vol. 5, no. 4, pp. 147–153, 2021.
  18. V. M. Baeza, E. Lagunas, H. Al-Hraishawi, and S. Chatzinotas, “An Overview of Channel Models for NGSO Satellites,” in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022, pp. 1–6.
  19. M. Höyhtyä, J. Kyröläinen, A. Hulkkonen, J. Ylitalo, and A. Roivainen, “Application of Cognitive Radio Techniques to Satellite Communication,” in 2012 IEEE International Symposium on Dynamic Spectrum Access Networks, 2012, pp. 540–551.
  20. C. Zhang, C. Jiang, J. Jin, S. Wu, L. Kuang, and S. Guo, “Spectrum Sensing and Recognition in Satellite Systems,” IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2502–2516, 2019.
  21. F. Benedetto, G. Giunta, and L. Pallotta, “Cognitive Satellite Communications Spectrum Sensing Based on Higher Order Moments,” IEEE Communications Letters, vol. 25, no. 2, pp. 574–578, 2020.
  22. Q. Tian, Y. Wu, F. Shen, F. Zhou, Q. Wu, and O. A. Dobre, “ED-Based Spectrum Sensing for the Satellite Communication Networks Using Phased-Array Antennas,” IEEE Communications Letters, 2023.
  23. M. Jia, X. Zhang, J. Sun, X. Gu, and Q. Guo, “Intelligent Resource Management for Satellite and Terrestrial Spectrum Shared Networking Toward B5G,” IEEE Wireless Communications, vol. 27, no. 1, pp. 54–61, 2020.
  24. 3GPP. (2020) Study on New Radio (NR) to Support Non-Terrestrial Networks. 3rd Generation Partnership Project (3GPP). [Online]. Available: https://www.3gpp.org/ftp/Specs/archive/38_series/38.811/
  25. E. Lagunas, S. K. Sharma, S. Maleki, S. Chatzinotas, and B. Ottersten, “Resource Allocation for Cognitive Satellite Communications with Incumbent Terrestrial Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 3, pp. 305–317, 2015.
  26. F. Li, G. Li, Z. Li, Y. Wang, and C. Lu, “Wideband Spectrum Compressive Sensing for Frequency Availability in LEO-based Mobile Satellite Systems,” International Journal of Satellite Communications and Networking, vol. 35, no. 5, pp. 481–502, 2017.
  27. R. Krishnan, J. P. Sterbenz, W. M. Eddy, C. Partridge, and M. Allman, “Explicit Transport Error Notification (ETEN) for Error-prone Wireless and Satellite Networks,” Computer Networks, vol. 46, no. 3, pp. 343–362, 2004.
  28. E. Cianca, R. Prasad, M. De Sanctis, A. De Luise, M. Antonini, D. Teotino, and M. Ruggieri, “Integrated Satellite-HAP Systems,” IEEE Communications Magazine, vol. 43, no. 12, pp. supl–33, 2005.
  29. Y. Chen, M. Zhang, X. Li, T. Che, R. Jin, J. Guo, W. Yang, B. An, and X. Nie, “Satellite-Enabled Internet of Remote Things Network Transmits Field Data from the Most Remote Areas of the Tibetan Plateau,” Sensors, vol. 22, no. 10, p. 3713, 2022.
  30. V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, “Efficient Processing of Deep Neural Networks: A Tutorial and Survey,” Proceedings of the IEEE, vol. 105, no. 12, pp. 2295–2329, 2017.
  31. Y. Zhang, A. Li, J. Li, D. Han, T. Li, R. Zhang, and Y. Zhang, “SpecKriging: GNN-based Secure Cooperative Spectrum Sensing,” IEEE Transactions on Wireless Communications, vol. 21, no. 11, pp. 9936–9946, 2022.
  32. D. Janu, S. Kumar, and K. Singh, “A Graph Convolution Network Based Adaptive Cooperative Spectrum Sensing in Cognitive Radio Network,” IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 2269–2279, 2022.
  33. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A Comprehensive Survey on Graph Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4–24, 2020.
  34. J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, “Graph Neural Networks: A Review of Methods and Applications,” AI open, vol. 1, pp. 57–81, 2020.
  35. P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio et al., “Graph Attention Networks,” stat, vol. 1050, no. 20, pp. 10–48 550, 2017.
  36. E. J. Candès and M. B. Wakin, “An Introduction to Compressive Sampling,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21–30, 2008.
  37. J. Huang, H. Zhang, C. Huang, and W. Zhang, “Compressed Random Access for Noncoherent Massive Machine-type Communications with Energy Modulation,” IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5175–5190, 2021.
  38. J. Huang, H. Zhang, C. Huang, L. Yang, and W. Zhang, “Noncoherent Massive Random Access for Inhomogeneous Networks: From Message Passing to Deep Learning,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 5, pp. 1457–1472, 2022.
  39. M. Mishali and Y. C. Eldar, “From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals,” IEEE Journal on Selected Areas in Communications, vol. 4, no. 2, pp. 375–391, 2010.
  40. T. Moon, H. W. Choi, N. Tzou, and A. Chatterjee, “Wideband Sparse Signal Acquisition with Dual-rate Time-interleaved Undersampling Hardware and Multicoset Signal Reconstruction Algorithms,” IEEE Transactions on Signal Processing, vol. 63, no. 24, pp. 6486–6497, 2015.
  41. Y. Ma, Y. Gao, Y.-C. Liang, and S. Cui, “Reliable and Efficient Sub-Nyquist Wideband Spectrum Sensing in Cooperative Cognitive Radio Networks,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 10, pp. 2750–2762, 2016.
  42. J. Yang, Z. Song, Y. Gao, X. Gu, and Z. Feng, “Adaptive Compressed Spectrum Sensing for Multiband Signals,” IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7642–7654, 2021.
  43. Z. Song, J. Yang, H. Zhang, and Y. Gao, “Approaching Sub-Nyquist Boundary: Optimized Compressed Spectrum Sensing Based on Multicoset Sampler for Multiband Signal,” IEEE Transactions on Signal Processing, vol. 70, pp. 4225–4238, 2022.
  44. D. Bank, N. Koenigstein, and R. Giryes, “Autoencoders,” Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, pp. 353–374, 2023.
  45. T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A Simple Framework for Contrastive Learning of Visual Representations,” in Proc. ICML, 2020, pp. 1597–1607.
  46. K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum Contrast for Unsupervised Visual Representation Learning,” in Proc. CVPR, 2020, pp. 9729–9738.
  47. J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al., “Bootstrap Your Own Latent-a New Approach to Self-supervised Learning,” Advances in neural information processing systems, vol. 33, pp. 21 271–21 284, 2020.
  48. A. Morello and V. Mignone, “DVB-S2: The Second Generation Standard for Satellite Broad-band Services,” Proc IEEE Inst Electr Electron Eng, vol. 94, no. 1, pp. 210–227, 2006.
  49. W. Lee, M. Kim, and D.-H. Cho, “Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 3005–3009, 2019.
  50. I. K. M. Jais, A. R. Ismail, and S. Q. Nisa, “Adam Optimization Algorithm for Wide and Deep Neural Network,” Knowledge Engineering and Data Science, vol. 2, no. 1, pp. 41–46, 2019.
  51. P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He, “Accurate, Large Minibatch SGD: Training Imagenet in 1 Hour,” arXiv preprint arXiv:1706.02677, 2017.
  52. Z. Lin, G. Zhu, Y. Deng, X. Chen, Y. Gao, K. Huang, and Y. Fang, “Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks,” IEEE Transactions on Mobile Computing, 2024.
  53. Z. Chen, C. Cai, T. Zheng, J. Luo, J. Xiong, and X. Wang, “Rf-based Human Activity Recognition Using Signal Adapted Convolutional Neural Network,” IEEE Transactions on Mobile Computing, vol. 22, no. 1, pp. 487–499, 2021.
  54. H. Yuan, Z. Chen, Z. Lin, J. Peng, Z. Fang, Y. Zhong, Z. Song, X. Wang, and Y. Gao, “Graph Learning for Multi-Satellite based Spectrum Sensing,” in Proc. ICCT, 2023.
  55. Z. Lin, G. Qu, X. Chen, and K. Huang, “Split Learning in 6G Edge Networks,” IEEE Wireless Communications, 2024.
  56. T. Zheng, A. Li, Z. Chen, H. Wang, and J. Luo, “Autofed: Heterogeneity-aware Federated Multimodal Learning for Robust Autonomous Driving,” in Proc. Mobicom, 2023, pp. 1–15.
  57. Y. Qiu, H. Chen, X. Dong, Z. Lin, I. Y. Liao, M. Tistarelli, and Z. Jin, “Ifvit: Interpretable fixed-length representation for fingerprint matching via vision transformer,” arXiv preprint arXiv:2404.08237, 2024.
  58. Z. Lin, G. Qu, Q. Chen, X. Chen, Z. Chen, and K. Huang, “Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities,” arXiv preprint arXiv:2309.16739, 2023.
  59. Z. Fang, Z. Lin, Z. Chen, X. Chen, Y. Gao, and Y. Fang, “Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models,” arXiv preprint arXiv:2404.06448, 2024.
  60. Z. Lin, G. Qu, W. Wei, X. Chen, and K. K. Leung, “AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks,” arXiv preprint arXiv:2403.13101, 2024.
  61. M. Liu, H. Zhang, Z. Liu, and N. Zhao, “Attacking Spectrum Sensing with Adversarial Deep Learning in Cognitive Radio-enabled Internet of Things,” IEEE Transactions on Reliability, 2022.
  62. X. Ding, T. Ni, Y. Zou, and G. Zhang, “Deep Learning for Satellites Based Spectrum Sensing Systems: A Low Computational Complexity Perspective,” IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 1366–1371, 2022.
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