Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
120 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge (2401.04837v2)

Published 9 Jan 2024 in cs.LG, cs.NI, cs.SY, and eess.SY

Abstract: Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. As this paradigm continues to spread, wireless systems must also evolve to identify active transmitters and unauthorized waveforms in real time under intentional distortion of preambles, extremely low signal-to-noise ratios and challenging channel conditions. We overcome limitations of correlation-based preamble matching methods in such conditions through the design of T-PRIME: a Transformer-based machine learning approach. T-PRIME learns the structural design of transmitted frames through its attention mechanism, looking at sequence patterns that go beyond the preamble alone. The paper makes three contributions: First, it compares Transformer models and demonstrates their superiority over traditional methods and state-of-the-art neural networks. Second, it rigorously analyzes T-PRIME's real-time feasibility on DeepWave's AIR-T platform. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Results reveal nearly perfect (i.e. $>98\%$) classification accuracy under simulated scenarios, showing $100\%$ detection improvement over legacy methods in low SNR ranges, $97\%$ classification accuracy for OTA single-protocol transmissions and up to $75\%$ double-protocol classification accuracy in interference scenarios.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (23)
  1. W. Zhang and M. Krunz, “Machine learning based protocol classification in unlicensed 5 GHz bands,” in 2022 IEEE International Conference on Communications Workshops (ICC Workshops).   IEEE, 2022, pp. 752–757.
  2. M. Schmidt, D. Block, and U. Meier, “Wireless interference identification with convolutional neural networks,” in 2017 IEEE 15th international conference on industrial informatics (INDIN).   IEEE, 2017, pp. 180–185.
  3. A. Jagannath and J. Jagannath, “Multi-task learning approach for automatic modulation and wireless signal classification,” in ICC 2021-IEEE International Conference on Communications.   IEEE, 2021, pp. 1–7.
  4. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  5. T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz et al., “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, 2020, pp. 38–45.
  6. J. Cai, F. Gan, X. Cao, and W. Liu, “Signal modulation classification based on the transformer network,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 3, pp. 1348–1357, 2022.
  7. Q. Zheng, P. Zhao, H. Wang, A. Elhanashi, and S. Saponara, “Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation,” IEEE Communications Letters, vol. 26, no. 6, pp. 1298–1302, 2022.
  8. S. Hamidi-Rad and S. Jain, “Mcformer: A transformer based deep neural network for automatic modulation classification,” in 2021 IEEE Global Communications Conference (GLOBECOM).   IEEE, 2021, pp. 1–6.
  9. Genesys Lab, “T-PRIME Repository,” https://github.com/genesys-neu/t-prime, 2023, [Accessed 2024-01-05].
  10. “IEEE Standard for Information Technology–Telecommunications and Information Exchange between systems - local and metropolitan area networks–specific requirements - part 11: Wireless lan medium access control (MAC) and physical layer (PHY) specifications,” IEEE Std 802.11-2020 (Revision of IEEE Std 802.11-2016), pp. 1–4379, 2021.
  11. T. J. O’Shea, J. Corgan, and T. C. Clancy, “Convolutional radio modulation recognition networks,” in Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings 17.   Springer, 2016, pp. 213–226.
  12. Y. Shi, K. Davaslioglu, Y. E. Sagduyu, W. C. Headley, M. Fowler, and G. Green, “Deep learning for RF signal classification in unknown and dynamic spectrum environments,” in 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).   IEEE, 2019, pp. 1–10.
  13. H. Elyousseph and M. L. Altamimi, “Deep learning radio frequency signal classification with hybrid images,” in 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).   IEEE, 2021, pp. 7–11.
  14. T. J. O’Shea, T. Roy, and T. C. Clancy, “Over-the-air deep learning based radio signal classification,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 168–179, 2018.
  15. C. Gravelle and R. Zhou, “SDR demonstration of signal classification in real-time using deep learning,” in 2019 IEEE Globecom Workshops (GC Wkshps).   IEEE, 2019, pp. 1–5.
  16. T. Huynh-The, C.-H. Hua, Q.-V. Pham, and D.-S. Kim, “MCNet: An efficient CNN architecture for robust automatic modulation classification,” IEEE Communications Letters, vol. 24, no. 4, pp. 811–815, 2020.
  17. J. Zhang, T. Wang, Z. Feng, and S. Yang, “AMC-Net: An Effective Network for Automatic Modulation Classification,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2023, pp. 1–5.
  18. S. Rajendran, W. Meert, D. Giustiniano, V. Lenders, and S. Pollin, “Deep learning models for wireless signal classification with distributed low-cost spectrum sensors,” IEEE Transactions on Cognitive Communications and Networking, vol. 4, no. 3, pp. 433–445, 2018.
  19. Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, pp. 48–62, 2021.
  20. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
  21. J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler et al., “Emergent abilities of large language models,” arXiv preprint arXiv:2206.07682, 2022.
  22. “AIR-T Overview - Deepwave Digital Docs — docs.deepwavedigital.com,” May 2022, [Accessed 30-07-2023]. [Online]. Available: https://docs.deepwavedigital.com/AIR-T/
  23. “Quick Start Guide : NVIDIA Deep Learning TensorRT Documentation — docs.nvidia.com,” https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/index.html, [Accessed 30-07-2023].

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now