Spectrum sensing (SS) is an essential task of the secondary user (SU) in a cognitive radio system. SS monitors the primary user (PU) activity in order to avoid any collision with SU, as the latter should be silent when PU is active on a given channel. Hybrid SS (HSS) is one of the powerful methods used to monitor PU activity. It consists of using different detectors together to make a final decision on the PU status. In this manuscript, artificial neural networks (ANN) are used to perform HSS. Since our data is composed from the test statistics (TSs) of several detectors, thus it can be modeled as tabular. Fully connected neural networks become the most suitable ANN model. We applied cutting-edge techniques in the field of deep learning in order to get the best possible accurate neural network model in our application. These techniques boil down to: embedding, regularization, batch normalization and smart learning rate selection. With the help TSs related to several detectors, ANN is trained to distinguish between two hypotheses, \(H_0\): PU is absent and \(H_1\): PU is active. Numerical results show the effectiveness of our proposed ANN-based HSS, as it outperforms the classical ANN-based energy detector and proves its capability to detect PU signal at very low SNR.

频谱感测（SS）是认知无线电系统中次要用户（SU）的一项基本任务。SS监视主要用户（PU）活动，以避免与SU发生任何冲突，因为当PU在给定通道上处于活动状态时，后者应保持静音。混合SS（HSS）是用于监视PU活动的强大方法之一。它包括一起使用不同的检测器来最终确定PU状态。在此手稿中，人工神经网络（ANN）用于执行HSS。由于我们的数据是由几个检测器的测试统计数据（TS）组成的，因此可以将其建模为表格形式。完全连接的神经网络成为最合适的ANN模型。我们在深度学习领域应用了尖端技术，以便在我们的应用程序中获得最佳的准确神经网络模型。这些技术可以归结为：嵌入，正则化，批量归一化和智能学习率选择。借助与多个检测器相关的帮助TS，训练了ANN来区分两个假设，\（H_0 \）：不存在PU，而\（H_1 \）：不处于激活状态。数值结果证明了我们提出的基于ANN的HSS的有效性，因为它优于经典的基于ANN的能量检测器，并证明了其在非常低的SNR下检测PU信号的能力。