## 5 Performance Comparison
### Comparison to Benchmark DL Models
#### 5.2.3 Performance of Residual Networks (ResNet)  
Residual Networks (ResNets) are very deep convolutional network models proposed in 2016 [78]. ResNet is derived from the VGG deep convolutional networks by adding Residual blocks. Residual block consists of two convolutional layers. ReLU activation function is used for each convolutional layer. The output of each block is combined using a shortcut connection. ResNet-V2 is a modified residual network using Residual Inception Blocks, which provides good detection performance but is costlier than ResNet or Inception-V3 [65].  
For our experiment, the effect of the output pooling layer is studied for ResNet-V2. The total parameters for each ResNet-V2 are stated in Table 3. The input images were resized to \((299,299)\) to fit the developed architecture of ResNet-V2. The ResNet-V2-GAP classifier starts to improve the accuracy of classification of different protocols in the light noise scenario (SNR 20 dB with AWGN) in comparing with ResNet-V2-MAX.  
The Inception-V3-GAP classifier can start identifying the protocols in the light noise scenario with the AWGN scenario, as indicated in Table 3. We also observed that the Inception-V3 with GAP layer performs better than ResNet-V2-GAP in classifying the spectrograms. However, the Inception-V3-GAP classification accuracy is low compared with our developed emitter classifier based on CDAE weights (CNN3 + AE). Inception-V3-GAP achieved 59.8% for SNR 20 dB in the AWGN channel scenario, which is considered a light noise condition in our experiment. Our CNN3 + AE model achieves 100% accuracy for the same dataset.  
In summary, we observed that using DL models like inception networks, VGG blocks, or residual networks protocol identification does not improve the classification accuracy for protocols operating in unlicensed bands, even in light noise conditions such as SNR 20 dB and channel affected by AWGN only (scenario 5). ResNet-V2 and Inception-V3 with the GAP layer achieve very low accuracy even in a light noise than our developed model using DAE weights (CNN3 + AE). Our (CNN3 + AE) pipeline achieves 100% accuracy. Our (CNN3 + AE) has the lowest number of parameters compared to other DL models as highlighted in Table 3.  
Complexity-wise, the number of Floating Point Operations (FLOs) expresses how computationally expensive a CNN model is. The FLOs of our proposed emitter model and other benchmark DL models were computed using the TensorFlow built-in profiler. Table 4 details the number of FLOs.  
The number of FLOs for our proposed model (CNN3 + AE) is 68.4 million, which is much less than the number of FLOs for benchmark DL models. The number of FLOs for Inception-V3 and ResNet-V2 is 11.4 billion and 26.4 billion, respectively. The complexity is therefore very promising for the deployment of our proposed DL model in real-time applications.  
We named our pipeline (CNN3 + AE) \"**ConvAE**\" DL model for protocol identification in unlicensed spectrum. **ConvAE** shows very high accuracy in identifying a range of radio access technologies in the unlicensed bands in harsh environments, which outperforms other well-known DL models in terms of accuracy and number of FLOs.