Effective utilization of spectrum resources is an important factor in wireless communication which reduces spectrum scarcity. Over the years, communication systems use different frequency bands, and the users are categorized into licensed and unlicensed users. Most of the wireless bands are typically licensed; as a result, accommodation of new technologies such as Internet of Things and machine to machine communication becomes difficult. So it is essential to obtain a wireless spectrum to adopt new technologies. Cognitive radio technology is introduced to improve such spectrum utilization. Reports state that most of the licensed spectrums are underutilized, and few spectrums are overutilized. Cognitive radio networks help to exploit the licensed spectrum and access the spectrum without any interference to the licensed user.Through its spectrum sensing and spectrum sharing process, cognitive radio network gains more attention in wireless communication. This research work proposed an efficient optimized spectrum sensing technique for cognitive radio networks through dragonfly optimization algorithm along with the adaptive threshold process. Proposed work performs better in terms of detection accuracy and efficiency when compared to conventional spectrum sensing schemes such as linear support vector machine and particle swarm optimization models.

Spectrum utilization is an important factor in Cognitive radio networks, which is accomplished by analyzing the unused spectrum bands of primary users (PU). The secondary users are allowed to access the resources, when the spectral bands are vacant by sensing the spectrum status and thus it reduces the spectrum scarcity among the users. Researchers have paid more attention towards spectrum sensing along with its security factors in cognitive radio networks. In this process, cooperative spectrum sensing is widely adopted in cognitive radio networks due to its robustness. However, the security concerns in cooperative spectrum sensing against attacks must be addressed. The performance of cooperative spectrum sensing will get affected if the fusion center gets wrong information from malicious user. This leads to wrong decision in the fusion center and results into false observations and affects the decision process. In order to overcome these challenges, this research work proposes a hybrid nature inspired and optimized cooperative spectrum sensing against attacks in cognitive radio networks. The proposed model allows the fusion center to remove the uncharacteristic data in the fusion process, which results from the malicious users. The performance analysis of spectrum sensing process under different attacks are analyzed through simulation and later it is compared against conventional methods such as genetic algorithm, particle swarm optimization and differential evolution schemes to validate the improved performance.

Summary 5G Internet of Things (IoT) networks are characterized by wideband radio frequency spectrum utility and are therefore of primary importance for efficient means of sensing the wideband spectrum characterized by high bandwidth. A cognitive radio network (CRN) intelligently does the sensing of authorized users ideal spectrum and allocates the same to the demanded unauthorized user. Conventional energy detection and k‐means schemes associated with CRN perform well for narrowband applications, whereas they are not quite suitable for wideband applications. Hence, a compressive collaborative sensing scheme together with deep neural network learning model (CCS‐DLNN) has been proposed to sense the information from the compressed and reconstructed information signal. Based on the extracted features, decision on presence or absence of primary user (PU) in the received signal has been observed. This paper proposes a deep learning neural network model for learning the dynamic change in the input spectra. Accordingly, this paper also updates the weights associated with the neurons to converge upon the target objective. The performance of the proposed sensing scheme has been evaluated pertaining to probability of detection, sensing error, and accuracy of detection of idle channels. The proposed work will be very useful for the upcoming generation departing to be implemented with 5G IoT networks.

No abstract