The evolving 5G and beyond 5G (B5G) wireless technologies are envisioned to provide ubiquitous connectivity and great heterogeneity in communication infrastructure by connecting diverse devices and providing multifarious services. Recently, the Internet of Things (IoT) and unmanned aerial vehicles (UAVs) are realized as an essential component of the upcoming 5G/B5G networks, enabling enhanced communication capacity, high reliability, low latency, and massive connectivity. However, one limiting factor in the expansion of 5G/B5G technology is the finite radio spectrum, which necessitates managing the anticipated spectrum crunch for future wireless networks. One potential solution is to develop intelligent cognitive methods to dynamically optimize the use of spectrum in 5G/B5G networks to solve the imminent problem of spectrum congestion and improve radio efficiency. This paper addresses the opportunistic spectrum access (OSA) problem in the 5G/B5G cognitive radio (CR) network of IoTs and UAVs through the novel deep learning-based detector, dubbed as Deep-CRNet. The proposed detector employs residual connections with cascaded multi-kernel convolutions to identify the primary user (PU) spectrum usage by extracting the inherent multi-scale signal and noise features in the sensed transmission patterns. Thereby, Deep-CRNet intelligently learns and locates the spectrum holes so that secondary users (SUs) and PUs can dynamically share network spectrum resources. The efficacy of Deep-CRNet is validated through simulation results, where it achieved 99.74% accuracy with 99.65% precision and 99.83% recall in accurately classifying the PU status. In addition, the average correct detection probability of Deep-CRNet in the low signal-to-noise ratio ($-20$ dB to $-15$ dB) range is 38.21% higher than the second best-performing detector.

不断发展的 5G 和 5G 之后 (B5G) 无线技术旨在通过连接不同的设备和提供多种多样的服务，在通信基础设施中提供无处不在的连接性和巨大的异构性。最近，物联网 (IoT) 和无人机 (UAV) 被实现为即将到来的 5G/B5G 网络的重要组成部分，可实现增强的通信容量、高可靠性、低延迟和海量连接。然而，5G/B5G 技术扩展的一个限制因素是有限的无线电频谱，这需要管理未来无线网络的预期频谱紧缩。一种潜在的解决方案是开发智能认知方法，动态优化 5G/B5G 网络中的频谱使用，以解决迫在眉睫的频谱拥塞问题，提高无线电效率。本文通过称为 Deep-CRNet 的新型基于深度学习的检测器，解决了物联网和无人机的 5G/B5G 认知无线电 (CR) 网络中的机会频谱访问 (OSA) 问题。所提出的检测器采用带有级联多核卷积的残差连接，通过提取传感传输模式中固有的多尺度信号和噪声特征来识别主用户 (PU) 频谱使用。从而，Deep-CRNet 智能地学习和定位频谱空洞，从而使二级用户（SU）和 PU 可以动态共享网络频谱资源。通过仿真结果验证了 Deep-CRNet 的有效性，它在准确分类 PU 状态方面达到了 99.74% 的准确率和 99.65% 的准确率和 99.83% 的召回率。此外，Deep-CRNet在低信噪比下的平均正确检测概率（$-20$ 分贝至 $-15$ dB) 范围比性能第二好的检测器高 38.21%。