A summary of related work in CRN.

Cognitive radio networks (CRN) enable wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables to satisfy Quality of Service (QoS) needs by reducing energy utilization. In CRN, spectrum sensing is an essential process that is highly challenging and can be addressed by several traditional techniques, such as energy detection, match filtering, etc. For now, the current models’ performance is impacted by the comparatively low Signal to Noise Ratio (SNR) of recognized signals and the insignificant quantity of traditional signal samples. This research proposals a new spectral sensing technique for cognitive radio networks (SST-CRN) that addresses the drawbacks of predictable energy detection models. With the use of a deep belief network (DBN), the suggested model contributes to accomplish a nonlinear threshold based on the chicken swarm algorithm (CSA). The proposed DBN enabled SST-CRN technique goes through two phases in a organized process: offline and online. Throughout the offline phase, the DBN model is methodically trained on pre-gathered data, developing the aptitude to identify problematic patterns and examples from the spectral features of the radio environment. This stage involves extensive feature extraction, validation, and model development to ensure that the DBN can professionally represent complicated spectral dynamics. Additionally, online spectrum sensing is conducted during the real communication phase to enable real-time adaptation to dynamic changes in the spectrum environment. Offline spectrum sensing is typically performed during a devoted sensing period before actual communication begins. When combined with DBN’s deep learning capabilities and CSO’s innate nature-inspired algorithms, a synergistic framework is created that enables CRNs to explore and allocate incidences on their own with astonishing accuracy. The proposed solution considerably improves the spectrum efficiency and resilience of CRNs by harnessing the power of DBN, which leads to more effective resource utilization and less interference. The Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments. The result parameters such as probability of detection, SNR of -24dB, the SST-CRN perfect has increased a developed Pd of 0.810, whereas the existing methods RMLSSCRN-100 and RMLSSCRN-300 have accomplished a lower Pd of 0.577 and 0.736, respectively. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches. Future research might focus on improving CSO algorithms to better optimize the spectrum sensing process, enhancing the reliability of DBN-enabled sensing techniques.

认知无线电网络（Cognitive Radio Networks, CRN）可使无线设备实现无线电频谱感知，识别各信道的频率状态，并重新配置通信参数，在降低能耗的同时满足服务质量（Quality of Service, QoS）需求。在CRN场景中，频谱感知是一项极具挑战性的核心流程，可通过能量检测、匹配滤波等多种传统技术实现。当前，现有模型的性能受限于待识别信号较低的信噪比（Signal to Noise Ratio, SNR）以及传统信号样本量不足的问题。
本研究提出一种面向认知无线电网络的新型频谱感知技术（SST-CRN），以弥补传统能量检测模型的固有缺陷。所提模型借助深度置信网络（Deep Belief Network, DBN），结合鸡群算法（Chicken Swarm Algorithm, CSA）构建基于非线性阈值的感知方案。所提出的融合DBN的SST-CRN技术按照严谨流程分为两个阶段：离线阶段与在线阶段。
离线阶段中，DBN模型将基于预先采集的数据集开展系统化训练，掌握从无线电环境的频谱特征中识别异常模式与样本的能力。该阶段包含大规模特征提取、验证与模型构建环节，以确保DBN能够精准表征复杂的频谱动态特性。此外，在线频谱感知环节将在实际通信阶段执行，以实现对频谱环境动态变化的实时适配。离线频谱感知通常在正式通信开始前的专属感知周期内完成。
将DBN的深度学习能力与鸡群算法固有的仿生智能算法优势相结合，可构建出协同感知框架，使CRN能够自主探索与分配频谱资源，并达到极高的感知准确率。所提方案通过充分利用DBN的性能优势，显著提升了CRN的频谱效率与鲁棒性，进而实现更高效的资源利用与更低的干扰水平。
仿真结果表明，所提策略能够实现更为精准的频谱占用评估。在检测概率（Pd）、信噪比为-24dB的测试场景下，SST-CRN方案的检测概率可达0.810，而现有方法RMLSSCRN-100与RMLSSCRN-300的检测概率分别仅为0.577与0.736。本研究所采用的深度学习方法借助卷积神经网络自动学习并适配动态复杂的无线电环境，相较于传统频谱感知方法，具备更高的精度与灵活性。
未来的研究可聚焦于优化鸡群算法以进一步完善频谱感知流程，提升基于DBN的感知技术的可靠性。