# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 5 Analysis and Results
### Stage 4: Selection of the Quantization Level per Layer
In Proceedings of the 2017 51 st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 29 October-1 November 2017; pp. 915-919. [CrossRef]* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modulation Classification Based on Signal Constellation Diagrams and Deep Learning. _IEEE Trans. Neural Netw. Learn. Syst._**2019**, _30_, 718-727. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Attentive Siamese Networks for Automatic Modulation Classification Based on Multitiming Constellation Diagrams. _IEEE Trans. Neural Netw. Learn. Syst._**2021**, _1_-15. [CrossRef]
* [PERSON] (2019) [PERSON] Will rule the airwaves: A DARPA grand challenge seeks autonomous radios to manage the wireless spectrum. _IEEE Spectr._**2019**, _56_, 28-33. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] [PERSON] A General Approach for Traffic Classification in Wireless Networks using Deep Learning. _IEEE Trans. Netw. Serv. Manag._**2021**, \(1\). [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON] [PERSON]; [PERSON] [PERSON] O-RAN: Disrupting the Virtualized RAN Ecosystem. _IEEE Commun. Stand. Mag._**2021**, 1-8. [CrossRef]
* [PERSON] (2022) [PERSON]; [PERSON] [PERSON] Quantized neural networks for modulation recognition. In Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV SPIE, Orlando, FL, USA, 3 April-13 June 2022; Volume 12113, pp. 397-412. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] FINN-R: An end-to-end deep-learning framework for fast exploration of quantized neural networks. _ACM Trans. Reconfigurable Technol. Syst. (TRETS)_**2018**, _11_, 1-23. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Automatic Modulation Recognition: An FPGA Implementation. _IEEE Commun. Lett._**2022**, _26_, 2062-2066. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON] Software-defined radio transceiver design using FPGA-based system-on-chip embedded platform with adaptive digital predistortion. _IEEE Access_**2020**, \(8\), 214882-214893. [CrossRef]
* [PERSON] et al. (2017) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Finn: A framework for fast, scalable binarized neural network inference. In Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA, 22-24 February 2017; pp. 65-74. * [PERSON] et al. (2022) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; et al. QONN: Representing Arbitrary-Precision Quantized Neural Networks. _arXiv_**2022**, arxiv:2206.07527. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] [PERSON] FPGA-SDR integration and experimental validation of a joint DA ML SNR and doppler spread estimator for 5G cognitive transceivers. _IEEE Access_**2019**, \(7\), 69464-69480. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] FPGA acceleration of a quantized neural network for remote-sensed cloud detection. In Proceedings of the 7 th International Workshop on On-Board Payload Data Compression, Virtual, 21-23 September 2020. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Threats of adversarial attacks in DNN-based modulation recognition. In Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications, Toronto, ON, Canada, 6-9 July 2020; pp. 2469-2478. * [PERSON] et al. (2016) [PERSON]; [PERSON] [PERSON]; [PERSON] _Deep Learning_; MIT Press: Cambridge, MA, USA, 2016. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Lightweight deep learning model for automatic modulation classification in cognitive radio networks. _IEEE Access_**2020**, \(8\), 197532-197541. [CrossRef]
* [PERSON] et al.