# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 6 Conclusions and Future Work  
In our study, we presented a CSS model based on a tree deep neural network architecture that combines a one-dimensional convolutional neural network (1 DCNN) and a long short-term memory (LSTM) network. The purpose of this model is to learn the activity patterns of the primary user. In CSS, the collaboration of all SUs is essential in determining the PU state. Therefore, our model is designed to learn the PU activity pattern at the SU level by utilizing a vectorized input obtained from multiple antennas. By incorporating 1 DCNN and LSTM layers into our model, we enable the learning of group-level PU activity patterns by capturing local features and global correlations. This integration enhances the overall detection performance. Through a comprehensive simulation analysis, we have determined that the DNN method based on bidirectional LSTM outperforms conventional methods and other proposed techniques in terms of detection performance, in scenarios involving imperfect reporting channels. This improvement can be attributed to its ability to capture substantial global correlations compared to the alternative methods. Furthermore, this approach strikes a favorable balance between detection accuracy and online detection, outperforming conventional and alternative approaches, which makes it a promising solution for CSS applications. Lastly, we observed that utilizing samples with larger lengths contributed to higher detection accuracy. This finding emphasizes the importance of considering longer sample lengths when aiming for improved performance in PU activity detection. Our study focused on evaluating the performance of the proposed model in various SNR scenarios for SU devices. The results obtained highlight the model's ability to generalize effectively. These results underline the significant influence of the SNR range on the overall model performance. However, the proposed approaches are affected by some limitations that could be explored in successive studies. * The cooperative architecture is based only on centralized processing at the fusion center. In practical applications, this could lead to delays in decision making, particularly if there are difficulties in transmitting data quickly from the SUs to the fusion center. Other network topologies and/or distributed processing can be investigated. * The current simulation involved only four secondary users and four antennas for the SU. A more complete dataset encompassing broader SU and antenna configurations would enable a more nuanced and comprehensive assessment. * The study did not explicitly consider scenarios in which certain SUs might be sub-optimally positioned for data transmission. Factors such as SU mobility, obstacles on the communication path or SU malfunctions could be modeled in a future work. Effectively addressing these real-world challenges can lead to a more authentic evaluation of the cooperative spectrum sensing system. * Finally, situations in which cooperation fails should be addressed. A thorough understanding and effective mitigation of these cases of cooperation failure can give information about the resilience and reliability of the proposed models in dynamic wireless environments. Regardless of these limitations, the three DNN models proposed for cooperative spectrum sensing can already have direct and important applications in various scenarios. The proposed models offer significant advantages in applications such as dynamic spectrum access, cognitive radio networks and others. In dynamic spectrum access, where the spectrum availability changes dynamically, the ability to capture temporal dependencies can ensure adaptive and efficient spectrum use. In cognitive radio networks, the proposed models can enable collaborative sensing among secondary users, improving the overall reliability and accuracy of spectrum occupancy detection. These applications extend to scenarios in which the environmental conditions, signal characteristics and interference patterns evolve over time.