# 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 future work, we want to use our methodology to find the appropriate quantization level using other DNN architectures for AMR. In addition, the combination of our method with other quantization techniques or in combination with pruning remains to be addressed, which could provide an even more significant reduction in the inference cost and validation to the generality of the proposed approach. We will also validate our methodology with other DL architectures solving the same problem and DL architectures used to solve other related problems at the radio receiver side that will benefit from obtaining low-computational-cost models. Finally, we will complement our experimental results with performance evaluations of some of the resulting models as a part of a wireless communication system running on an FPGA. This is fundamental to provide further quantitative results of the trade-off between model accuracy and other metrics related to the model size, such as energy consumption and processing speed. Conceptualization, all authors; methodology, D.G. and M.C. ; validation, D.G. ; software, D.G. ; writing--original draft preparation, D.G., M.C., PS. ; writing--review and editing, all authors; supervision, S.L., N.G., M.C. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 101017109 (DAEMON), and by the Colombian Ministry of Science Technology and Innovation (Minciencias) and the Communications Regulation Commission (CRC), under the contract CT 80740-035-2022. Not applicable. Data Availability Statement: Restrictions apply to the availability of these data. The data set was obtained from Deepsig Inc. and is available at [[https://opendata.deepsig.io/datasets/2018.01/2018.01.0](https://opendata.deepsig.io/datasets/2018.01/2018.01.0) SC.0001_1024x2M.h5.tar.gz]([https://opendata.deepsig.io/datasets/2018.01/2018.01.0](https://opendata.deepsig.io/datasets/2018.01/2018.01.0) SC.0001_1024x2M.h5.tar.gz) (accessed on 18 October 2022). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AI & Artificial intelligence \\ ML & Machine learning \\ CNN & Convolutional neural network \\ FC & Fully connected layers \\
5G & Fifth-generation technology standard for broadband cellular networks \\
5G-NR & 5G new radio \\ SDR & Software-defined radio \\ ISM & Industrial, scientific, and medical \\ ANN & Artificial neural network \\ GNN & Graph neural network \\ SNR & Signal-to-noise ratio \\ BW & Bandwidth \\ AWGN & Additive white Gaussian noise \\ DL & Deep learning \\ NUC & Next unit of computing \\ RU & Radio unit \\ DU & Distributed unit \\ \end{tabular} 

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