# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 3 Related Work
### AMR with Low Computational Cost  
Although the results of modulation detection and classification using the DL techniques presented above are promising, integrating these kinds of algorithms at the radio side to perform the task in real time requires high-end devices due to the high computational cost [10; 11]. This computational cost comes from the many floating-point operations that must be performed while performing inference. As a solution, compressing the DL models has the advantage of reducing the number of floating-point operations and, in some cases, replacing them with fixed-point functions to trade-off the accuracy/performance of the model against its complexity, i.e., its computational cost.  
The main advantage of compressing DL models is that it allows their deployment on resource-constrained devices while increasing processing speed and improving energy efficiency. In the case of new-generation communications such as 5G-NR, DL algorithms running on DU (e.g., an INTEL NUC or an NVIDIA Jetson) or at the RU (e.g., an SDR), will need to be compressed to guarantee real-time operation on constrained devices such as the ones deployed at the edge or far-edge. These devices are at the other end of capabilities compared to the cloud/core network, where, traditionally, DL models are trained and run for other AI tasks.  
To reduce the model's complexity, two well-known approaches are used to compress DL models: the first is based on network pruning techniques, and the second is based on the quantization of the input parameters of the network. Although they are entirely different techniques, they can be used individually or in combination on the same model to improve results. Pruning is the process that searches and identifies the weights of connections among neurons in DL models [33] that are redundant or are not needed such that the drop in accuracy is minimal when they are eliminated [23].  
Reducing computational cost through quantization is an entirely different process than pruning since it replaces floating-point operations with fixed-point operations. With this approach, the authors of [13] proposed a QNN model for AMR. The quantized model achieved a classification accuracy of 75% with a signal-to-noise ratio SNR of 10 dB, similar to an unquantized model. In [15], a more versatile approach was followed. The authors suggested combining pruning and quantization techniques to reduce the model size further. The authors used different quantization levels and found no suitable combination, as the results indicated that the accuracy degraded considerably. As a solution, the authors applied pruningtechniques to the quantized model. The results showed a reduced inference cost with a limited drop in accuracy.  
While the literature on DL models for AMR is extensive, this is not the case for compression methods that allow such models to run on constrained devices such as those found at the edge or far edge. In addition, the few works that provide evaluation results on compressed models for AMR focus on the performance of a single model with all layers quantized with the same quantization value. Having the same quantization level for all the model's layers limits the application of such results since they do not provide insight about how to design the model so that we can trade-off the accuracy and model size.