SIMO Cooperative Spectrum Sensing Scenario: A Dataset for Testing DNN-Based Models

This dataset focuses on cooperative spectrum sensing in a cognitive radio network, where multiple secondary users collaborate to detect the presence of a primary user. We introduce multiple cooperative spectrum sensing schemes based on a tree deep neural network architecture, incorporating a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity pattern of the primary user. The dataset provides instructions for utilizing the dataset, including executing the code in Google Colab, importing IPython notebooks, generating training and test data, training the models, and testing the models to obtain ROC curves and calculate Pd_SNR. The dataset also includes pre-trained models, which can be easily loaded and evaluated using the provided Python notebook.

本数据集聚焦于认知无线电网络（cognitive radio network）中的协作频谱感知任务，该任务下多台次级用户协同检测主用户的存在。本数据集提出了多种基于树形深度神经网络架构的协作频谱感知方案，该架构融合了一维卷积神经网络（one-dimensional convolutional neural network）与长短期记忆网络（long short-term memory network）。上述方案的核心目标为有效学习主用户的活动模式。本数据集附带数据集使用指南，涵盖在Google Colab中运行代码、导入IPython笔记本、生成训练与测试数据集、训练模型，以及对模型进行测试以获取受试者工作特征曲线（Receiver Operating Characteristic curve，简称ROC曲线）并计算Pd_SNR。本数据集同时包含预训练模型，可通过提供的Python笔记本轻松加载并完成评估。