# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors
### Network Architecture Design
#### 4.2.1 Hierarchical LSTM with 1 DCNN  
The proposed DNN model consists of a 1 DCNN network at the SU level and an LSTM classifier at the fusion center. In Figure 4, a 1 DCNN is shown, which consists of two convolutional blocks at the SU level. Each block includes a one-dimensional convolutional (Conv1D) layer and a rectified linear unit (ReLU) activation function. These blocks are then followed by a max pooling layer and a fully connected (FC) layer. At the fusion center level, there is a concatenation layer, an LSTM layer and a classifier network, respectively.  
As is known, the 1 DCNN is well suited for the extraction of local features from sequential data [55]. Employing a two-layer 1 DCNN could obtain su  
Figure 4: Proposed architecture 1: hierarchical LSTM with 1 DCNN (CNN_LSTM).  
when tackling the SS problem, especially in low-SNR environments [32]. To leverage this capability, our proposed model includes two convolutional neural blocks to extract local patterns from the vectorized input signal. We also employ ReLU activation functions to enhance the nonlinear relationships between layers, which is essential in handling complex time series data. Additionally, we use max pooling layers to reduce the dimensionality of the resulting features and mitigate overfitting. The FC layer maps an input feature to a lower-dimensional vector. The output from the fully connected layer is then flattened and used as the feature representation for each SU. In our proposed approach, we consider the transmission of the feature representation obtained from the SU level to the fusion center. The feature representation is transmitted from the SUs to the fusion center through their respective transmitting antennas. Transmitting calculated features instead of raw data yields key benefits such as reduced dimensionality, data compression and reduced redundancy. This approach optimizes bandwidth utilization while preserving data integrity and privacy. Indeed, in this specific context, when transmitting a feature vector of a fixed size containing 256 points, even without the utilization of network optimization strategies, there is typically no necessity for high bandwidth allocation in the control channel. However, due to factors such as distortion or errors during the transmission process, the transmitted data may experience errors or corruption. To model this behavior, we incorporate a bit error rate that reflects the probability of a bit being received incorrectly at the fusion center, aiming to emulate the impact of transmission errors on the received feature representation at the fusion center. This allows us to assess the robustness and performance of our proposed model in scenarios where the transmitted data may be affected by errors.  
As previously mentioned, while 1 DCNNs excel at extracting local features, they often struggle to capture long-term temporal connections in the input data. To address this limitation, we incorporate an LSTM layer after concatenating the feature representations from all SUs at the fusion center. This enables us to efficiently capture the hidden connections among the extracted features by taking into account the timing regularity of the input data. By using an LSTM in the fusion center, we can also extract temporal features from the sensing data of the group level over multiple sensing periods. Thus, the cooperative LSTM models the SU-level temporal features of all cooperative SUs to learn the group-level PU activity pattern. Finally, to obtain the classification result, the LSTM layer is followed by the FC layer and Softmax layer.