Department of Information and Communication Technology (ICT), Islamic University, Kushtia 7003, Bangladesh.
Department of Biomedical Engineering (BME), Islamic University, Kushtia 7003, Bangladesh.
Sensors (Basel). 2020 Apr 29;20(9):2525. doi: 10.3390/s20092525.
Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback-Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.
频谱感知在认知无线电网络（CRN）中起着至关重要的作用，用于识别频谱空洞。然而，在噪声不确定的环境中，CRN 中的单个认知无线电用户使用传统的检测技术（如能量检测（ED）技术）无法获得足够的感知性能和主、次链路的和速率，无法支持未来的物联网（IoT）。在包含噪声不确定性的环境中，由于噪声温度、干扰和滤波引起的噪声波动，传统的能量检测技术的性能会显著下降。为了解决这个问题，我们提出了一种基于 Kullback-Leibler 散度（KLD）的协作频谱感知技术，用于认知无线电物联网（CR-IoT）。在提出的方法中，每个能够进行频谱感知的无执照物联网设备，称为 CR-IoT 用户，使用 KLD 技术做出本地决策。与其他传统方法（例如能量检测）相比，使用 KLD 进行的频谱感知需要更少的样本，即使在噪声不确定的环境中也能实现可靠的感知。做出本地决策后，每个 CR-IoT 用户将其自己的本地决策结果发送到相应的融合中心，融合中心使用软融合规则做出全局决策。通过仿真获得的结果表明，与传统的 ED 方案相比，所提出的 KLD 方案在各种衰落信道下具有更好的感知性能，即更高的检测和更低的虚警概率，提高了和速率，并降低了总时间。
