# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 3 Related Work
### Amr  
Numerous works have investigated DL architectures that seek to improve the ability to detect and classify modulations compared to traditional methods such as LB and FB [6]. DNN-based classifiers have overcome the performance of traditional methods and methods based on statistical ML [5]. Moreover, DNN [22], i.e., ANN with more than one layer between the input and the output layers, have shown the best performance without explicitly requiring an FB method to extract the features of the radio signal [5; 7; 8; 9]. DNN architectures can perform both the feature extraction and the classification of the radio signals using a unique ANN. Authors in [5] showed that a DNN based on a CNN outperformed several expert FB methods using different classifiers. CNNs are ANNs that use convolution in place of general matrix multiplication in at least one of their layers [22]. The CNN achieved a rough accuracy of 87% across all the 11 types of modulated radio signals and SNR in the test dataset, and using only raw I/Q samples of modulated radio signals as input. Similarly, [PERSON] et al. [8] proposed a CNN classifier for images generated after data conversion from raw I/Q samples to gray images. Again, improvements and better classification accuracy were achieved over cumulant and ML-based AMR algorithms. The authors of [23] proposed a CNN architecture trained on the DeepSig dataset named RadioML Dataset 2018.01A, which has 24 modulation classes. The results indicated that the classification accuracy improves in the range of SNR from \(-4\) dB to 20 dB. In [24], the authors proposed a method that uses I/Q samples to form images with the \"Stockwell discrete orthonormal transform\". Another technique is to perform transformations on the input I/Q samples to find other types of characteristics of the signal. Furthermore, it is possible to combine models trained on different datasets for better performance, such as in [25], where two models from CNN were integrated into a single general model that allows more accurate classification of different modulations. However, this method is not computationally cheap since it does not use dimensionality reduction methods. Instead of using CNN, authors in [26] proposed a DL architecture based on ENN for AMR in multipath fading channels. The proposed DNN is based on the natural logarithmic energy model. The proposed ENN uses three smaller DNNs, each trained to recognize the radio signal's amplitude, phase, and frequency. The results showed that the proposed ENN has a higher probability of correct classification than traditional algorithms for classifying modulations within the same training sequence and SNR. In [27], a CNN and an LSTM were combined to create an HDMF. A DL-based AMR method employing SCF was introduced. In the proposed method, a DBN is applied for pattern recognition and classification with high accuracy, even in the presence of environmental noise. Recently, works such as [28; 29; 30] addressed the problem of AMR based on the design and implementation of new AI architectures with improved performance. Figure 2: Workflow from Brevitas to the FPGA. Other approaches focus on the preprocessing of the input signal, which allows highlighting features that would otherwise not be observed, reduced, or extracted. For example, using different input types, an SAE was evaluated in [31]. The I/Q samples, the centroids of the constellation points, obtained using a fuzzy C-means algorithm, and the high-order cumulants of the received samples were used as training inputs. Each autoencoder layer was trained using unsupervised learning followed by a softmax classifier. The results showed that the accuracy of the proposed architecture outperformed AMR methods using an LB classifier, cumulant-based genetic programming in combination with KNN classifiers, and DNN using cumulants and instantaneous PSD as the features. Finally, the authors in [32] proposed an alternative approach to extract relevant features for AMR in the preprocessing phase. The authors used a GNN for AMR.