# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 2 Related Works
While deep learning-based spectrum sensing offers high accuracy and adaptability to complex and dynamic environments, it is not without its drawbacks. The requirement for a large amount of labeled data and substantial computing resources for training can be prohibitive. Moreover, issues related to interpretability and transparency in deep learning models remain a challenge, making it difficult to understand the decision-making process and trust the outcomes, especially in critical applications. Furthermore, when it comes to deep learning-based spectrum sensing, the focus on single-node detection poses its own set of challenges. Deep learning models, often trained on centralized datasets, can struggle to adapt seamlessly to diverse, decentralized sensing environments. The ability to capture nuanced patterns and spectrum variations between different nodes becomes crucial to ensure the effectiveness of these models in real-world scenarios, where nodes may exhibit distinct signal characteristics. Nevertheless, overfittingis a common concern in deep learning, especially when dealing with complex architectures. Regularization techniques, though beneficial, require careful tuning to strike a balance between preventing overfitting and preserving model performance. Federated learning (FL) and federated reinforcement learning (FRL) frameworks can be successfully applied in order to take advantage of knowledge sharing without leading to problems of privacy and network overloading [44; 45]. An FL framework for CSS was proposed in [46]. This framework can perform collaborative training while ensuring local data privacy and greatly reducing the traffic load between the SUs and fusion center. In [47], the authors proposed a spectrum access model based on the federated deep Q network (DQN), which adopts the asynchronous federated weighted learning algorithm to share and update the weights of the DQN in multiple agents, to decrease the time cost and accelerate convergence. The decentralized nature of FL comes with its own set of challenges. Coordinating computations across many nodes is a major challenge, particularly in scenarios where devices may have different computational capabilities and network conditions. The need to aggregate model updates introduces a communication overload, which can lead to increased latency and resource consumption, which is a critical consideration in real-time spectrum sensing applications. This communication overhead becomes a critical consideration in ensuring the efficiency of FL implementations. FL relies on decentralized data across multiple devices, addressing privacy concerns by keeping sensitive information local. However, data diversity between nodes becomes crucial. Imbalances or biases in the data distribution may impact the federated model's ability to generalize effectively to different spectral conditions. In addition, the collaborative nature of federated learning, with models trained on diverse datasets across nodes, introduces challenges in managing overfitting. Striking a balance between model convergence and overfitting prevention becomes a delicate exercise. Ensuring that the federated model converges collectively without sacrificing performance on individual nodes requires the careful consideration of the hyper-parameters and regularization techniques to maintain robustness and generalization capabilities. In this paper, we propose three distinct DNN schemes tailored to CSS in a multi-antenna and multi-SU device scenario. These schemes have been carefully designed to address the specific requirements of the scenario. Motivated by the need to reduce the amount of data transmitted and maintain the privacy of the information, as mentioned in [42; 43], we have incorporated these components into our network designs. The proposed designs in our work represent an enhancement in terms of accuracy, rapidity and computational efficiency for secondary user (SU) devices in cooperative spectrum sensing, all while ensuring privacy protection.