Secure Beamforming in 5G-Based Cognitive Radio Network

Abstract

Cognitive radio network (CRN) and non-orthogonal multiple-access (NOMA) is a significant system in the 5G wireless communication system. However, the system is an exceptional way for the cognitive users to secure a communication from the interferences in multiple-input multiple-output (MIMO)-NOMA-based cognitive radio network. In this article, a new beamforming technique is proposed to secure an information exchange within the same cells and neighboring cells from all intervened users. The interference is caused by an imperfect spectrum sensing of the secondary users (SUs). The SUs are intended to access the primary channels. At the same time, the primary user also returns to the channel before the SUs access ends. This similar way of accessing the primary channel will cause interference between the users. Thus, we predicted that the impact of interferences would be greatly reduced by the proposed technique, and that the proposed technique would maximize the entire secrecy rate in the 5G-based cognitive radio network. The simulation result provides better evidence for the performance of the proposed technique.

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

“…The authors proposed a beamforming approach for secure data transmission is a 5G cognitive radio network [31]. The proposed beamforming approach includes two users: the primary user (PU) and the secondary user (SU).…”

“…• The trust value of the vehicles in the network was computed by the fusion center by incorporating the TOPSIS Beamforming in ultra-dense network Reinforcement learning algorithm Insecure beamforming [31] Secure Beamforming Power allocation policy Insufficient beamforming security [32] Interference less Beamforming Bi-LSTM Inaccurate CSI [33] Multiuser Beamforming Deep Neural Network High Attack Threats Spectrum Sensing and Access [34] Dynamic spectrum access Proactive and reactive techniques Poor spectrum access [35] Spectrum allocation Deep reinforcement algorithm High spectrum allocation latency [36] Dynamic spectrum access and allocation QAM modulation and Lagrange method Low performance of spectrum allocation [37] QoS aware spectrum access ANN algorithm Poor spectrum access efficiency [38] Detection of false sensing Dempster-Shafer theory Partially overcome the malicious SUs method; however, it faced difficulties in maintaining the consistency of the decision.…”

“…This metric is used to analyze the accuracy for beamforming generation between the proposed BiGRU-CapsNet algorithm and other algorithms, which was proposed in several existing approaches, such as DNN [27], DQN [29], and Bi-LSTM [31]. It is computed in terms of percentage.…”

An adaptive learning algorithm for spectrum sensing based on direction of arrival estimation in cognitive radio systems

“…Further considered secondary user direction of arrival (DOA), it contains multiple input single output (MISO) and one receiving antenna for primary user and feedback based adaptive frequency algorithm. Non orthogonal multiple access [1] based cognitive radio network is used for secure beamforming to avoid interference in multiple input multiple output networks. In array sensors, estimation of number of channels is a problem and it is solved by considering direction of arrival in spectrum sensing method.…”