DeepSweep: Parallel and Scalable Spectrum Sensing via Convolutional Neural Networks
Abstract: Spectrum sensing is an essential component of modern wireless networks as it offers a tool to characterize spectrum usage and better utilize it. Deep Learning (DL) has become one of the most used techniques to perform spectrum sensing as they are capable of delivering high accuracy and reliability. However, current techniques suffer from ad-hoc implementations and high complexity, which makes them unsuited for practical deployment on wireless systems where flexibility and fast inference time are necessary to support real-time spectrum sensing. In this paper, we introduce DeepSweep, a novel DL-based transceiver design that allows scalable, accurate, and fast spectrum sensing while maintaining a high level of customizability to adapt its design to a broad range of application scenarios and use cases. DeepSweep is designed to be seamlessly integrated with well-established transceiver designs and leverages shallow convolutional neural network (CNN) to "sweep" the spectrum and process captured IQ samples fast and reliably without interrupting ongoing demodulation and decoding operations. DeepSweep reduces training and inference times by more than 2 times and 10 times respectively, achieves up to 98 percent accuracy in locating spectrum activity, and produces outputs in less than 1 ms, thus showing that DeepSweep can be used for a broad range of spectrum sensing applications and scenarios.
- S.-M. Kim, J. Kim, H. Cha, M. S. Sim, J. Choi, S.-W. Ko, C.-B. Chae, and S.-L. Kim, “Opportunism in spectrum sharing for beyond 5g with sub-6 ghz: A concept and its application to duplexing,” IEEE Access, vol. 8, pp. 148 877–148 891, 2020.
- D. Uvaydov, S. D’Oro, F. Restuccia, and T. Melodia, “Deepsense: Fast wideband spectrum sensing through real-time in-the-loop deep learning,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
- F. Restuccia, S. D’Oro, A. Al-Shawabka, B. C. Rendon, S. Ioannidis, and T. Melodia, “Deepfir: Channel-robust physical-layer deep learning through adaptive waveform filtering,” IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 8054–8066, 2021.
- X. Zha, H. Peng, X. Qin, G. Li, and S. Yang, “A Deep Learning Framework for Signal Detection and Modulation Classification,” Sensors, vol. 19, no. 18, p. 4042, Jan. 2019, number: 18 Publisher: Multidisciplinary Digital Publishing Institute.
- Y. Arjoune, F. Salahdine, M. S. Islam, E. Ghribi, and N. Kaabouch, “A novel jamming attacks detection approach based on machine learning for wireless communication,” in 2020 International Conference on Information Networking (ICOIN). IEEE, 2020, pp. 459–464.
- D. Uvaydov, S. D’Oro, F. Restuccia, and T. Melodia, “DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, May 2021, pp. 1–10, iSSN: 2641-9874.
- S. Soltani, Y. E. Sagduyu, R. Hasan, K. Davaslioglu, H. Deng, and T. Erpek, “Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification,” in MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), Nov. 2019, pp. 1–6, iSSN: 2155-7586.
- J. Gao, X. Yi, C. Zhong, X. Chen, and Z. Zhang, “Deep learning for spectrum sensing,” IEEE Wireless Communications Letters, vol. 8, no. 6, pp. 1727–1730, 2019.
- F. A. Bhatti, M. J. Khan, A. Selim, and F. Paisana, “Shared Spectrum Monitoring Using Deep Learning,” IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 4, pp. 1171–1185, Dec. 2021.
- 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.
- L. Zhang, M. Xiao, G. Wu, M. Alam, Y.-C. Liang, and S. Li, “A survey of advanced techniques for spectrum sharing in 5g networks,” IEEE Wireless Communications, vol. 24, no. 5, pp. 44–51, 2017.
- S. Xie, Y. Liu, Y. Zhang, and R. Yu, “A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 8, pp. 4079–4092, Oct. 2010.
- W. Liu, D. Pareit, E. D. Poorter, and I. Moerman, “Advanced spectrum sensing with parallel processing based on software-defined radio,” EURASIP Journal on Wireless Communications and Networking, vol. 2013, no. 1, p. 228, Sep. 2013.
- K. Davaslioglu, S. Soltani, T. Erpek, and Y. E. Sagduyu, “DeepWiFi: Cognitive WiFi with Deep Learning,” IEEE Transactions on Mobile Computing, vol. 20, no. 2, pp. 429–444, Feb. 2021.
- Y. Arjoune and N. Kaabouch, “A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions,” Sensors, vol. 19, p. 126, 01 2019.
- A. Ranjan, Anurag, and B. Singh, “Design and analysis of spectrum sensing in cognitive radio based on energy detection,” in 2016 International Conference on Signal and Information Processing (IConSIP), 2016, pp. 1–5.
- S. Zheng, S. Chen, P. Qi, H. Zhou, and X. Yang, “Spectrum sensing based on deep learning classification for cognitive radios,” China Communications, vol. 17, no. 2, pp. 138–148, 2020.
- J. Xie, C. Liu, Y.-C. Liang, and J. Fang, “Activity pattern aware spectrum sensing: A cnn-based deep learning approach,” IEEE Communications Letters, vol. 23, no. 6, pp. 1025–1028, 2019.
- K. Yang, Z. Huang, X. Wang, and X. Li, “A blind spectrum sensing method based on deep learning,” Sensors, vol. 19, no. 10, 2019.
- S. Riyaz, K. Sankhe, S. Ioannidis, and K. Chowdhury, “Deep learning convolutional neural networks for radio identification,” IEEE Communications Magazine, vol. 56, pp. 146–152, 09 2018.
- T. Highlander and A. Rodriguez, “Very efficient training of convolutional neural networks using fast fourier transform and overlap-and-add,” CoRR, vol. abs/1601.06815, 2016. [Online]. Available: http://arxiv.org/abs/1601.06815
- D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2017-01-29. [Online]. Available: http://arxiv.org/abs/1412.6980
- T. C. Clancy, “Efficient ofdm denial: Pilot jamming and pilot nulling,” in 2011 IEEE International Conference on Communications (ICC), 2011, pp. 1–5.
- L. Bertizzolo, L. Bonati, E. Demirors, A. Al-shawabka, S. D’Oro, F. Restuccia, and T. Melodia, “Arena: A 64-antenna SDR-based ceiling grid testing platform for sub-6GHz 5G-and-Beyond radio spectrum research,” Computer Networks, vol. 181, p. 107436, Nov. 2020.
- B. Bloessl, M. Segata, C. Sommer, and F. Dressler, “An ieee 802.11 a/g/p ofdm receiver for gnu radio,” in Proceedings of the second workshop on Software radio implementation forum, 2013, pp. 9–16.
- K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2014. [Online]. Available: https://arxiv.org/pdf/1409.1556
- S. Benazzouza, M. Ridouani, F. Salahdine, and A. Hayar, “A novel prediction model for malicious users detection and spectrum sensing based on stacking and deep learning,” Sensors, vol. 22, no. 17, 2022. [Online]. Available: https://www.mdpi.com/1424-8220/22/17/6477
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