Spectrum sensing (SS) is a concept of cognitive radio systems at base transceiver stations that can find the white space i.e. licensed spectrum owned by primary users (PU), for transmission over a wireless network without any channel interference. The cognitive radio network is designed to overcome the problem of the limited radio frequency spectrum as most of the applications are dependent on wireless devices in 5G. The major concern that arises here is the detection of spectrum availability. The traditional approaches can solve this issue but consume a large amount of time and prior information about PU and spectrum. The objective of this paper is to give a solution to resolve such issues. In this paper, we have used the learning capabilities of deep learning algorithms such as Convolution neural network (CNN) and Recurrent neural network (RNN) for spectrum sensing without prior knowledge of PU. The proposed model is termed ensemble CNN and RNN (ECRNN) to learn the features of spectrum data and predict the spectrum availability at base transceiver stations in 5G. The simulation result of the ECRNN showed the improvement of accuracy of the system with a reduction in losses that occurred during the false alarm of prediction as well as an improvement in the probability of detection. ECRNN had analyzed PU statistics and result in better spectrum sensing. This paper also supported multiple SUs that would increase the speed of spectrum sensing and data transmission over the available limited spectrum at the same time.

频谱感测 (SS) 是基站收发器上的认知无线电系统的概念，它可以找到空白空间，即主要用户 (PU) 拥有的许可频谱，用于在没有任何信道干扰的情况下通过无线网络进行传输。认知无线电网络旨在克服无线电频谱有限的问题，因为大多数应用都依赖于 5G 中的无线设备。这里出现的主要问题是频谱可用性的检测。传统方法可以解决这个问题，但会消耗大量时间和关于 PU 和频谱的先验信息。本文的目的是提供解决此类问题的解决方案。在本文中，我们已经使用了深度学习算法的学习能力，例如卷积神经网络 (CNN) 和循环神经网络 (RNN)，在没有 PU 的先验知识的情况下进行频谱感知。所提出的模型被称为集成 CNN 和 RNN (ECRNN)，用于学习频谱数据的特征并预测 5G 中基站收发器的频谱可用性。ECRNN的仿真结果表明，系统的精度提高，预测虚警损失减少，检测概率提高。ECRNN 分析了 PU 统计数据并产生了更好的频谱感知。本文还支持多个 SU，这将同时提高可用有限频谱上的频谱感知和数据传输速度。