# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors
### Network Architecture Design  
The fundamental concept underlying our proposed designs is the integration of an end-to-end architecture comprising a 1 DCNN and LSTM network. The objective of this choice is to effectively capture both local patterns and temporal dependencies within the received signal, optimizing the extraction of informative features from the SS observations. Given the cooperative nature of spectrum sensing, where multiple SUs contribute their processed inputs to a fusion center, we propose two distinct DNN designs. The first design employs an end-to-end 1 DCNN and LSTM at the SU level. This architecture ensures that each SU benefits from the comprehensive feature extraction capabilities of the 1 DCNN and LSTM layers, contributing enhanced and refined information for cooperative decision making at the fusion center. In the second design, we introduce a variation by incorporating a BiLSTM in conjunction with the 1 DCNN, maintaining an end-to-end framework. This choice allows the model to consider bidirectional temporal dependencies, potentially capturing more nuanced patterns in the spectrum sensing signals. When replacing the LSTM with a BiLSTM, we anticipate a modest improvement in accuracy. However, it is acknowledged that this potential enhancement comes with a trade-off: an increase in computational resource requirements, system complexity and processing time. In the context of CSS, where real-time decision making is crucial, minimizing the processing time and resource utilization is of paramount importance. By presenting both designs, we aim to thoroughly evaluate the trade-off between enhanced accuracy and the practical constraints of computational efficiency. The decision to offer two alternatives underscores our commitment to providing a nuanced understanding of the implications of each design choice. Both designs feature a fusion center utilizing MLP networks. This choice enables flexible and fast adaptive information fusion, optimizing cooperative decision making based on processed inputs from multiple SUs. MLP networks are generally fast to compute, thus reducing the processing time at the fusion center, which allows us to mitigate one of the main CSS constraints. In the third design, a distinct approach was taken by employing a 1 DCNN solely at the SU level, while introducing a cooperative LSTM network at the fusion center level. The decision to utilize a 1 DCNN at the SU level is rooted in its ability to effectively capture local patterns, providing a focused feature extraction process tailored to the characteristics of the individual SUs. This localized processing helps in extracting relevant information before transmitting it to the fusion center. At the fusion center level, the introduction of a cooperative LSTM serves a dual purpose. Rather than an end-to-end LSTM, the cooperative LSTM is designed to undertake two critical tasks. First, it plays a role akin to an end-to-end LSTM, capturing and understanding the temporal dependencies within the data received from the SUs. This functionality ensures that the fusion center comprehensively analyzes the sequential nature of the spectrum sensing data, considering the historical context for decision making. The second task of the cooperative LSTM at the FC involves processing the gathered output from the SUs. This distinctive feature allows the LSTM to function as a central platform, aggregating and interpreting information from multiple SUs, thereby promoting cooperative decision making. With the aim to address potential overfitting issues, all proposed models have been equipped with effective L2 regularization techniques. We have applied L2 regularization to both the weights of the layers and the output of the layers. L2 regularization helps to control the magnitude of the models' parameters, preventing them from becoming overly large during training and reducing the risk of overfitting.