Biblio

Communication systems across a variety of applications are increasingly using the angular domain to improve spectrum management. They require new sensing architectures to perform energy-efficient measurements of the electromagnetic environment that can be deployed in a variety of use cases. This paper presents the Directional Spectrum Sensor (DSS), a compressive sampling (CS) based analog-to-information converter (CS-AIC) that performs spectrum scanning in a focused beam. The DSS offers increased spectrum sensing sensitivity and interferer tolerance compared to omnidirectional sensors. The DSS implementation uses a multi-antenna beamforming architecture with local oscillators that are modulated with pseudo random waveforms to obtain CS measurements. The overall operation, limitations, and the influence of wideband angular effects on the spectrum scanning performance are discussed. Measurements on an experimental prototype are presented and highlight improvements over single antenna, omnidirectional sensing systems.

Cognitive Radio Network makes intelligent use of the spectrum resources. However, spectrum sensing is vulnerable to numerous harmful assaults. To lower the network's performance, hackers attempt to alter the sensed result. In the fusion centre, blockchain technology is used to make broad judgments on spectrum sensing in order to detect and thwart hostile activities. The sensed local results are hashed using the SHA 3 technique. This improves spectrum sensing precision and effectively thwarts harmful attacks. In comparison to other established techniques like equal gain combining, the simulation results demonstrate higher detection probability and sensing precision. Thus, employing Blockchain technology, cognitive radio network security can be significantly enhanced.

The strategy of permanently allocating a frequency band in a wireless communication network to one application has led to exceptionally low utilization of the vacant spectrum. By utilizing the unused licensed spectrum along with the unlicensed spectrum, Cognitive Radio Sensor Network (CRSNs) ensures the efficiency of spectrum management. To utilize the spectrum dynamically it is important to safeguard the spectrum sensing. Cooperative Spectrum Sensing (CSS) is recommended for this task. CSS aims to provide reliable spectrum sensing. However, there are various vulnerabilities experienced in CSS which can influence the performance of the network. In this work, the focus is on the Byzantine attack in CSS and current security solutions available to avoid the Byzantines in CRSN.

In this paper, we introduce cooperative spectrum sensing (CSS) scheme for detection of primary user (PU) in a cognitive radio network. Our scheme is based on a separating-hyperplane that discriminates between ellipsoids corresponding to two hypotheses. Additionally, we present a method to eliminate malicious cognitive radio users (MCRUs) that send false sensing data to the fusion center (FC) and degrade the system's detection performance. Simulation results verify the outperformance of the proposed method for the elimination of MCRUs and detection of PU.

In recent years, with the rapid update of wireless communication technologies such as 5G and the Internet of Things, as well as the explosive growth of wireless intelligent devices, people's demand for radio spectrum resources is increasing, which leads spectrum scarcity is becoming more serious. To address the scarcity of spectrum, the Internet of Things based on cognitive radio (CR-IoT) has become an effective technique to enable IoT devices to reuse the spectrum that has been fully utilized. The frequency band information is transmitted through wireless communication in the CR-IoT network, so the node is easily to be eavesdropped or tampered with by attackers in the process of transmitting data, which leads to information leakage and wrong perception results. To deal with the security problem of channel data transmission, this paper proposes a chaotic compressed spectrum sensing algorithm. In this algorithm, the chaotic parameter package is utilized to generate the measurement matrix, which makes good use of the sensitivity of the initial value of chaotic system to improve the transmission security. And the introduction of the semi-tensor theory significantly reduces the dimension of the matrix that the secondary user needs to store. In addition, the semi-tensor compressed sensing is used in the fusion center for parallel reconstruction process, which effectively reduces the sensing time delay. The simulation results show that the chaotic compressed spectrum sensing algorithm can achieve faster, high-quality, and low-energy channel energy transmission.

Wireless networks are very significant in the present world owing to their widespread use and its application in domains like disaster management, smart cities, IoT etc. A wireless network is made up of a group of wireless nodes that communicate with each other without using any formal infrastructure. The topology of the wireless network is not fixed and it can vary. The huge increase in the number of wireless devices is a challenge owing to the limited availability of wireless spectrum. Opportunistic spectrum access by Cognitive radio enables the efficient usage of limited spectrum resources. The unused channels assigned to the primary users may go waste in idle time. Cognitive radio systems will sense the unused channel space and assigns it temporarily for secondary users. This paper discusses about the recent trends in the two most important aspects of Cognitive radio namely spectrum sensing and security.

Cognitive radio is development of wireless communication and mobile computing. Spectrum is a limited source. The licensed spectrum is proposed to be used only by the spectrum owners. Cognitive radio is a new view of the recycle licensed spectrum in an unlicensed manner. The main condition of the cognitive radio network is sensing the spectrum hole. Cognitive radio can be detect unused spectrum. It shares this with no interference to the licensed spectrum. It can be a sense signals. It makes viable communication in the middle of multiple users through co-operation in a self-organized manner. The energy detector method is unseen signal detector because it reject the data of the signal.In this paper, has implemented Simulink Energy Detection of spectrum sensing cognitive radio in a MATLAB Simulink to Exploit spectrum holes and avoid damaging interference to licensed spectrum and unlicensed spectrum. The hidden primary user problem will happened because fading or shadowing. Ithappens when cognitive radio could not be detected by primer users because of its location. Cooperative sensing spectrum sensing is the best-proposed method to solve the hidden problem.

Cognitive radio is a promising technology that intends on solving the spectrum scarcity problem by allocating free spectrum dynamically to the unlicensed Secondary Users (SUs) in order to establish coexistence between the licensed Primary User (PU) & SUs, without causing any interference to the incumbent transmission. Primary user emulation attack (PUEA) is one such major threat posed on spectrum sensing, which decreases the spectrum access probability. Detection and defense against PUEA is realized using Yardstick based Threshold Allocation technique (YTA), by assigning threshold level to the base station thereby efficiently enhancing the spectrum sensing ability in a dynamic CR network. The simulation is performed using NS2 and analysis by using X-graph. The results shows minimum interference to primary transmissions by letting SUs spontaneously predict the prospective spectrum availability and aiding in effective prevention of potential emulation attacks along with proficient improvement of throughput in a dynamic cognitive radio environment.

With the improvement in technology and with the increase in the use of wireless devices there is deficiency of radio spectrum. Cognitive radio is considered as the solution for this problem. Cognitive radio is capable to detect which communication channels are in use and which are free, and immediately move into free channels while avoiding the used ones. This increases the usage of radio frequency spectrum. Any wireless system is prone to attack. Likewise, the main two attacks in the physical layer of cognitive radio are Primary User Emulation Attack (PUEA) and replay attack. This paper focusses on mitigating these two attacks with the aid of authentication tag and distance calculation. Mitigation of these attacks results in error free transmission which in turn fallouts in efficient dynamic spectrum access.

This paper investigates closed-form expressions to evaluate the performance of the Compressive Sensing (CS) based Energy Detector (ED). The conventional way to approximate the probability density function of the ED test statistic invokes the central limit theorem and considers the decision variable as Gaussian. This approach, however, provides good approximation only if the number of samples is large enough. This is not usually the case in CS framework, where the goal is to keep the sample size low. Moreover, working with a reduced number of measurements is of practical interest for general spectrum sensing in cognitive radio applications, where the sensing time should be sufficiently short since any time spent for sensing cannot be used for data transmission on the detected idle channels. In this paper, we make use of low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. More precisely, this paper provides new closed-form expressions for accurate evaluation of the CS-based ED performance as a function of the compressive ratio and the Signal-to-Noise Ratio (SNR). Simulation results demonstrate the increased accuracy of the proposed equations compared to existing works.

Spectrum sensing (signal detection) under low signal to noise ratio is a fundamental problem in cognitive radio networks. In this paper, we have analyzed maximum eigenvalue detection (MED) and energy detection (ED) techniques known as semi-blind spectrum sensing techniques. Simulations are performed by using independent and identically distributed (iid) signals to verify the results. Maximum eigenvalue detection algorithm exploits correlation in received signal samples and hence, performs same as energy detection algorithm under high signal to noise ratio. Energy detection performs well under low signal to noise ratio for iid signals and its performance reaches maximum eigenvalue detection under high signal to noise ratio. Both algorithms don't need any prior knowledge of primary user signal for detection and hence can be used in various applications.

As smart grid becomes more popular and emergent, the need for reliable communication technology becomes crucial to ensure the proper and efficient operation of the grid. Therefore, cognitive radio has been recently utilized to provide a scalable and reliable communication infrastructure for smart grid. However, accurate spectrum sensing is the core of this infrastructure. In this paper, we propose an architecture, utilizing Role-Based Delegation to manage spectrum sensing within the cognitive-radio-based communication infrastructure for smart grid and ensure its reliability and security.

Cognitive radio (CR) networks are becoming an increasingly important part of the wireless networking landscape due to the ever-increasing scarcity of spectrum resources throughout the world. Nowadays CR media is becoming popular wireless communication media for disaster recovery communication network. Although the operational aspects of CR are being explored vigorously, its security aspects have gained less attention to the research community. The existing research on CR network mainly focuses on the spectrum sensing and allocation, energy efficiency, high throughput, end-to-end delay and other aspect of the network technology. But, very few focuses on the security aspect and almost none focus on the secure anonymous communication in CR networks (CRNs). In this research article we would focus on secure anonymous communication in CR ad hoc networks (CRANs). We would propose a secure anonymous routing for CRANs based on pairing based cryptography which would provide source node, destination node and the location anonymity. Furthermore, the proposed research would protect different attacks those are feasible on CRANs.