Abstract Cognitive radio networks face challenges from malicious users who aim to disrupt decision-making during spectrum sensing and decrease spectrum efficiency. The proposed model seeks to distinguish between the presence and absence of primary user signals using energy detection and also it introduces a quantum-inspired outlier detection mechanism for cognitive radio networks to enhance security and reliability during spectrum sensing by ignoring the identified outliers. It employs BB84 quantum key distribution (QKD) protocol and one time pad for secure transmission of secondary user (SU) local decisions to the fusion center (FC), ensuring data integrity and confidentiality. These decisions are transmitted securely establishing a shared secret key between SU and FC and arrives at a global decision. To make the model more robust, we proposed another novel model with a modified BB84 protocol and compared the results of both the models, offering a robust solution for enhancing the reliability and performance of cognitive radio networks with proven results from simulation.
Abstract Cognitive radio networks face challenges from malicious users who aim to disrupt decision-making during spectrum sensing and decrease spectrum efficiency. The proposed model seeks to distinguish between the presence and absence of primary user signals using energy detection and also it introduces a quantum-inspired outlier detection mechanism for cognitive radio networks to enhance security and reliability during spectrum sensing by ignoring the identified outliers. It employs BB84 quantum key distribution (QKD) protocol and one time pad for secure transmission of secondary user (SU) local decisions to the fusion center (FC), ensuring data integrity and confidentiality. These decisions are transmitted securely establishing a shared secret key between SU and FC and arrives at a global decision. To make the model more robust, we proposed another novel model with a modified BB84 protocol and compared the results of both the models, offering a robust solution for enhancing the reliability and performance of cognitive radio networks with proven results from simulation.
Cognitive self-organizing networks (CSONs) have emerged as a promising approach to address the spectrum scarcity problem in wireless communications. These networks rely on cognitive radios (CRs) that can dynamically adapt their transmission parameters and spectrum access strategies based on environmental conditions and available resources. However, efficient spectrum-sharing mechanisms are essential to exploit the potential of CSONs and maximize spectrum utilization fully. This research article proposes a novel framework for quantum-assisted spectrum sharing in CSONs. Leveraging the principles of quantum computing and quantum communication, proposed framework aims to overcome the limitations of classical spectrum-sharing approaches and provide enhanced performance in terms of spectrum efficiency, interference mitigation, and security. The proposed framework utilizes quantum superposition and entanglement to enable simultaneous spectrum access by multiple CRs while ensuring interference-free coexistence. By encoding the spectrum occupancy information of primary users (PUs) into quantum states, CRs can efficiently sense and detect available spectrum opportunities, even in dynamic and highly congested scenarios. Quantum-based spectrum allocation and resource management algorithms are developed to facilitate optimal spectrum utilization and fair sharing among CRs. Furthermore, the inherent characteristics of quantum mechanics, such as non-locality and quantum key distribution, are exploited to enhance the security and privacy of spectrum sharing operations in CSONs. Quantum-resistant cryptographic techniques are incorporated to protect sensitive information and prevent unauthorized access. The study conducts extensive simulations to evaluate the performance of the proposed quantum-assisted spectrum sharing framework in various network scenarios. The results demonstrate significant improvements in terms of spectrum utilization, interference management, and security compared to conventional spectrum sharing schemes. The framework shows promise for addressing the challenges associated with spectrum scarcity and enabling efficient spectrum sharing in CSONs. Overall, this research article contributes to the emerging field of quantum-assisted communication systems and provides valuable insights into the application of quantum computing principles in the design of cognitive self-organizing networks.
The study investigates how quantum cryptography can be employed to improve security in smart grid communication networks. Attempting to use the BB84 QKD protocol, it allows secure creation and management of those cryptographic keys that cannot be broken with the help of quantum and traditional hacking techniques. It depends on symmetric encryption mechanisms like AES to provide high data confidentiality during transmission. Regarding the key management, the system provides a key management server that distributes keys to the grid nodes and performs key updating to ensure control with the managing authority. It also presents reliability under practical conditions through performance analogs such as Quantum Bit Error Rates (QBER), and system latency. This approach shows that quantum cryptography can provide a highly secure and efficient communication network for the next-generation smart grid.
The advent of 6G wireless communication promises improvements in signal coverage, data rates, and latency, addressing increased connectivity demands due to the proliferation of 5G, IoT, and augmented reality. This paper introduces a Quantum-Secured IoT Communication Framework designed for 6G Cognitive Radio Networks (CRNs) in order to cater to the growing need for dependable and protected connectivity. Noteworthy aspects of this framework encompass dual-layer authentication utilizing Quantum Key Distribution (QKD) and Public Key Infrastructure (PKI), secured spectrum access regulations, and efficient beamforming strategies. Moreover, the framework employs a Reinforcement Learning-based Ensemble Regression (RL-ER) model for spectrum sensing and a Multi-Layer Perceptron with Kalman Filter (MLP-KF) for Channel State Information (CSI) prediction. Simulations demonstrate that the framework significantly improves prediction accuracy, encryption and decryption times, and error rates, thereby enhancing IoT network performance with better signal coverage, reduced latency, and robust security.
In this study, the physical layer security for cognitive radio network (CRN) will be investigated in which a secondary user transmitter (SU-Tx) sends confidential information to a SU receiver (SU-Rx) on the same frequency band of a primary user (PU) in the presence of an eavesdropper receiver. Moreover, relay selection scheme is proposed for the security constrained CRNs with single eavesdropper, multiple eavesdroppers and PUs. The proposed scheme selects a trusted decode and forward relay to assist the SU-Tx and maximise the achievable secrecy rate that is subjected to the interference power constraints at the PUs for the different number of eavesdroppers and PUs under available channel knowledge. The SU cooperates with relays only when a high secrecy rate is achieved. Secrecy rate and secrecy outage probability are the two performance metrics that are used to verify the effectiveness of the proposed scheme although asymptotic approximations of the secrecy outage probability are also derived. Simulation and analytical results demonstrate that the performance improvement of the proposed scheme reaches to the double relative to the conventional scheme for the secrecy capacity.
In this paper, we study the physical layer security for underlay cognitive radio network in the presence of an eavesdropper that tries to intercept the secondary transmission. Specifically, a transmission at each hop uses a same frequency band of a primary user as long as the interference that caused by the secondary transmission at a primary receiver does not excess a maximum tolerable interference level. We investigate the secrecy outage probability (SOP) for a cognitive decode-and-forward (DF) relay network. Moreover, we drive a closed-form expression of the SOP of the considered system. Simulation results validate our theoretical results under three distinct cases as follows: the signal-to-noise ratio (SNR) of the main link between a secondary transmitter and a corresponding receiver is, respectively, smaller, equal to, or larger than the SNR of the eavesdropper link between the same secondary transmitter and the eavesdropper.
The Internet of Things (IoT) has transformed device communication, necessitating reliable information security, especially physical layer security (PLS). Protecting data confidentiality in wireless transfers is critical in IoT environments. Cognitive Radio Networks (CRNs) optimize spectrum utilization, enhancing network efficiency. This study investigates PLS in CRNs, featuring a primary source-destination pair and a secondary user (SU) that performs cooperative jamming to secure the primary user’s (PU) communication. The SU generates artificial interference, with transmit power determined by the primary network’s interference tolerance. We develop mathematical formulas to assess outage probabilities, aiming to enhance security and efficiency in IoT and spectrum management.