Technology In Cognitive Radio Networks Research Articles

SSO Based Energy Efficient Power Allocation with Optimal Power Constraints for Underlay Cognitive Radio Networks

ABSTRACT The most desired scheme in wireless communication (WC) technology is a cognitive radio network (CRN), which enhanced spectral efficiency. The main challenging problem for CRN is the power allocation (PA) scheme since it requires deliberating the quality of service (QoS) parameters for secondary users (SUs) through sustaining the power inference (PI) to Primary users (PUs) under the threshold of Interference Temperature (IT). The spectral insufficiency issues and interferences among the users possess a challenging task in CRN technology. To solve this issue, this paper statements shark smell optimisation (SSO) PA complications with optimal power constraints to diminishes the interference power and maximise capacity by permitting a large number of secondary users in CRN technology. In addition to that, multi-objective optimisation problems have been considered and targeted to maximise the minimum signal to noise ratio (SNR), network throughput utility and energy efficiency (EE). The proposed method is executed in MATLAB software, and the simulation results evaluate that the proposed strategy maintain ethical balance when compared to other existing approaches also it contributes to spectral scarcity issues resourcefully.

An Authentication Based Approach for Prevention of Spectrum Sensing Data Falsification Attacks in Cognitive Radio Network

This is the era of Intelligent Cognitive Radio Network (CRN) technology that provides the available spectrum with efficient utilization. Cognitive Radio (CR) technology must promise to allow interference-free spectrum access by users. The paper discusses the several attacks and motives of attacks. The authentication mechanism role to prevent the attacks for hassle-free spectrum utilization is demonstrated. In this paper, resolving the cognitive network security issues by the authentication mechanism and the methods and need of authentication is discussed. This paper addresses the research challenges in the way of securing the CRN and countermeasures in CRN security strategies. Spectrum sensing data falsification (SSDF) attack is most disruptive in which the malicious users degrade the decision-making process by sending the false sensing reports to data fusion center’s thus preventing honest users from utilizing the spectrum. In this paper, to counter the SSDF attack, the trust-based security mechanism is demonstrated to authenticate the honest users and it is observed that the proposed framework in the MATLAB environment is efficient and able to detect malicious users. CR technology is the strategy applied to the spectrum to make it efficient for wireless communication. The strategy is an intelligent way to access the spectrum as it can learn its environment and make decisions by easy adaptation of operating parameters. The initial step is to sense the spectrum available further steps are spectrum decision making, spectrum management, and spectrum mobility. The network is vulnerable to various attacks on spectrum sensing and policy protocols which lead to disturbing functionality of CR technology. The defence mechanism based on public-key cryptography is proposed in which the Primary User (PU) is authenticated by appending signature provided to PU signal. Authentication with a tag to the primary users is another perspective proposed. CRN technology should provide integrity, confidentiality and authenticity to the users.

A Survey on Prevention of the Falsification Attacks on Cognitive Radio Networks

Today is the era of intelligent cognitive radio network technology that allows the utilization of available spectrum efficiently by allocation of the spectrum dynamically to unlicensed users. The technology utilizes the free spectrum bands which are not being used by the authorized users without causing any obstruction to the existing transmission. Cognitive radio technology promises interference free spectrum access by users. However, there exist many security attacks on spectrum impacting on spectrum access probability and network services. This will result in significant degradation of the performance of a cognitive radio network. This paper discusses the several attacks on different layers, their motives and various mechanisms to prevent them. The specific threats based on issues with configurability are explored on the basis of the related work. In this paper the role of authentication mechanism to prevent the attacks for hassle free spectrum utilization and its performance in resolving the cognitive network security issues are discussed. The need for effective sensing and decision mechanisms to enhance the security of cognitive networks is demonstrated in the paper. Hence, the presented paper gives an overview of existing research efforts at first and secondly, the research challenges are identified that can address the ways to secure the cognitive radio network and lastly the countermeasures in cognitive radio network security strategies are demonstrated.

Genetic Algorithm Dependent Qos Aware Adaptive Subcarrier Allocation Scheme in Cognitive Radio Networks

Cognitive Radio Network technology is an efficient and intelligent system with the intention of detecting automatically about the availability of channels in a wireless spectrum and changing transmission parameters to facilitate more communication.In Quality of Service aware spectrum decision making system is locating the spectrum bands all the way through in view of the application necessities by means of the self-motivated nature of the spectrum bands.Here, an innovative adaptive sub-carrier allocation mechanism is proposed for Cognitive Radio Networks by rearranging the functional parameters anchored in the Proportional Resource Algorithm and Genetic Algorithm.This proposed scheme is designed to achieve a divergent performance standard for subcarrier allocation and power allocation system of the Cognitive Radio Network users. The suggested scheme is proven to be effective by maximizing the allocation of data rate for each subcarrier. Moreover, by increasing the overall network throughput, the numbers of satisfied users within bandwidth limits are maintained with the quality of service. In fact, it locates the maximum channel availability.

A General Framework for Spectrum Sensing Using Dedicated Spectrum Sensor Networks

Efficient spectrum sensing is essential for the successful application of the Dynamic Spectrum Assignment (DSA) technology in Cognitive Radio Networks (CRNs). In conventional spectrum sensing schemes, secondary users (SUs) have to intelligently schedule their sensing and accessing so that the spectrum opportunities are thoroughly exploited while the primary users are not harmed. In this article, we propose a new sensing service model in which a Spectrum Sensor Network (SSN) is employed for spectrum sensing tasks. We will describe the general framework for this SSN-enabled CRN and present the major challenges in such an architecture. We will address one of these challenges and formulate it as a boundary detection problem with unknown erroneous inputs. A novel cooperative boundary detection algorithm is designed which explores recent advances in Support Vector Machines (SVM) and computational geometry. We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. Real testbed as well as comprehensive simulation experiments are conducted, and the results show that, compared with the traditional schemes, cooperative boundary detection can dramatically reduce the spectrum sensing overhead and improve the effectiveness of DSA.

A Multi-Antenna Spectrum Sensing Scheme Based on Main Information Extraction and Genetic Algorithm Clustering

Spectrum sensing is an indispensable technology for cognitive radio networks, which enables secondary users (SUs) to discover spectrum holes and to opportunistically use under-utilized channels without causing interference to primary users. Aim at improving the sensing performance, a multi-antenna spectrum sensing scheme based on main information extraction and genetic algorithm clustering (MIEGAC) is proposed in this paper. Specifically, in order to reduce the amount of signal that is transferred to the fusion center, an information pre-processing scheme based on principal component analysis (PCA) is presented. Main information from the sensing signal is extracted via PCA, which reduces the cost of the reporting channel and the impact of interfering information on detection result. Furthermore, an information fusion method is described in this paper, which takes the place of complicated matrix decomposition algorithms. Moreover, inspired by machine learning, a clustering scheme based on genetic algorithm is introduced to classify signal features, which implements the spectrum sensing decision and avoids calculating the decision threshold. Simulation results illustrate that the MIEGAC can considerably improve the sensing performance for spectrum sensing. Significantly, this paper provides a novel approach for the design of centralized spectrum sensing algorithms in cognitive radio technologies.

Unleashing the Power of Multi-Server PIR for Enabling Private Access to Spectrum Databases

The emergence of IoT is driving rapid growth in the use of wireless devices around the world. In addition, 5G, through its support for large numbers of devices, is expected to unleash the full potential of IoT globally. The growing number of wireless devices and the massive traffic stemming from the emergence of these technologies will result in a dramatic increase in the demand for spectrum resources. Dynamic spectrum sharing, enabled through cognitive radio network technology, emerges as a key solution for coping with these rising spectrum demands. One important approach that is currently being adopted as a potential solution for promoting dynamic spectrum sharing is the deployment and reliance on white space geo-location spectrum databases for locating spectrum availability in the TV white space. Despite the great benefits these databases offer in terms of their ability to help locate spectrum opportunities for secondary usage, they suffer from location privacy issues, as users need to reveal their location in the process of querying these databases for spectrum availability. Knowing that their whereabouts may be exposed, users can be discouraged from querying the databases, thereby hindering the adoption and deployment of this technology in future generation networks. In this article, we focus on the location privacy problem in database-driven dynamic spectrum access. Specifically, we present and compare key approaches that aim to protect the location information of secondary users in database-driven spectrum sharing and discuss some key research challenges that remain unaddressed.

Spectrum Prediction Based on Taguchi Method in Deep Learning With Long Short-Term Memory

Spectrum prediction is a promising technology in cognitive radio networks, since it can reduce considerable time and energy consumed in spectrum sensing process. Many spectrum prediction algorithms have achieved good performance, but majority of them with shallow architecture cannot capture the inherent correlations of spectrum data very well. Long short-term memory (LSTM) neural network in deep learning has been validated to have strong capability of solving time series problems. In this paper, we develop a spectrum prediction framework with a deep learning approach on two real-world spectrum datasets. For the first dataset to predict channel occupancy states, we firstly employ the taguchi method to determine the best optimized configuration of neural network for certain spectrum point and then analyze the effect of each design hyper-parameter. Next, we build LSTM neural networks with two perspectives of regression and classification for spectrum prediction. For the second dataset to predict channel quality, we compare the prediction performance of the LSTM neural network and conventional multilayer perceptron (MLP) neural network. For both of our datasets, results show that the prediction performance varies with frequency bands. From the point of statistics, the LSTM neural network has better prediction performance than the MLP neural network and is more stable as well. Furthermore, we find that the performance of the LSTM neural network with classification perspective is slightly better than that with regression perspective in our first dataset.

Optimal Mode Selection Policies in Cognitive Radio Networks with RF Energy Harvesting

Applying energy harvesting technology in cognitive radio networks (CRNs) leads to a tradeoff between the time allocated for spectrum sensing followed by spectrum accessing and that for energy harvesting. This tradeoff can be formulated as a mode selection problem for the secondary users. In this paper, we consider a CRN working in the time-slotted manner. The secondary users powered by radio frequency energy harvesting can perform overlay transmission or cooperate with the primary users. To maximize the long-term throughput of the secondary network, we propose two optimal mode selection policies by formulating this problem under a partially observable Markov decision process framework. Numerical simulations show that both of our proposed policies achieve more throughput than the overlay-only policy. Finally, we also evaluate the effect of the cooperative threshold and the energy harvesting process on the optimal policies.

Detection of PUE Attack in CRN with Reduced Error in Location Estimation Using Novel Bat Algorithm

Cognitive Radio Network Technology makes the efficient utilization of scarce spectrum resources by allowing the unlicensed users to opportunistically use the licensed spectrum. Cognitive Radio Network due to its flexible and open nature is vulnerable to a number of security attacks. This paper is mainly concerned with one of the physical layer attack called Primary User Emulation Attack and its detection. This paper solves the problem of PUE attack by localization technique based on TDOA measurements with reduced error in location estimation using a Novel Bat Algorithm (NBA). A number of cooperative secondary users are used for detecting the PUEA by comparing its estimated position with the known position of incumbent. The main goal of NBA is to minimize two fitness functions namely non-linear least square and the maximum likelihood in order to optimize the estimation error. After evaluation, simulation results clearly demonstrates that NBA results in reduced estimation error as compared to Taylor Series Estimation and Particle Swarm Optimization.

Location Privacy in Cognitive Radio Networks: A Survey

Cognitive radio networks (CRNs) have emerged as an essential technology to enable dynamic and opportunistic spectrum access which aims to exploit underutilized licensed channels to solve the spectrum scarcity problem. Despite the great benefits that CRNs offer in terms of their ability to improve spectrum utilization efficiency, they suffer from user location privacy issues. Knowing that their whereabouts may be exposed can discourage users from joining and participating in the CRNs, thereby potentially hindering the adoption and deployment of this technology in future generation networks. The location information leakage issue in the CRN context has recently started to gain attention from the research community due to its importance, and several research efforts have been made to tackle it. However, to the best of our knowledge, none of these works have tried to identify the vulnerabilities that are behind this issue or discuss the approaches that could be deployed to prevent it. In this paper, we try to fill this gap by providing a comprehensive survey that investigates the various location privacy risks and threats that may arise from the different components of this CRN technology, and explores the different privacy attacks and countermeasure solutions that have been proposed in the literature to cope with this location privacy issue. We also discuss some open research problems, related to this issue, that need to be overcome by the research community to take advantage of the benefits of this key CRN technology without having to sacrifice the users' privacy.

ON THE LOCATION-AWARE COOPERATIVE SPECTRUM SENSING IN URBAN ENVIRONMENT

Spectrum sensing is a key enabling technology for cognitive radio networks (CRNs). The main objective of spectrum sensing is to provide more spectrum access opportunities to cognitive radio users without interfering with the operations of the licensed network. Spectrum sensing decisions can lead to erroneous sensing with low performance due to fading, shadowing and other interferences caused by either terrain inconsistency or dense urban structure. In order to improve spectrum sensing decisions, in this paper a cooperative spectrum sensing scheme is proposed. The propagation conditions such as the variance and intensity of terrain and urban structure between two points with respect to signal propagation are taken into consideration. We have also derived the optimum fusion rule which accounts for location reliability of secondary users (SUs). The analytical results show that the proposed scheme slightly outperforms the conventional cooperative spectrum sensing approaches.

Single-User Cyclic Spectrum Detector Design in Cognitive Radio

Cognitive radio can improve the spectrum utilization efficiency of spectrum band. So spectrum sensing is the key technology in cognitive radio network. Our main task is to design a cyclic detector with better detection performance than energy detector. This paper studies single-user spectrum detector in cognitive radio networks, including cyclic spectrum detector and energy detector. We firstly analyzed the energy spectrum sensing mathematic model for comparison, then we proposed the new simulation model and detailed simulation steps of this cyclic spectrum sensing detector. Simulation results demonstrate that the cyclic spectrum detector has better performance than energy detector while the SNR of channel condition is low.

An Improved SNR-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is a new technology in cognitive radio network, whose main purpose is to design an optimal detector. This paper studies a soft linear cooperative spectrum sensing method. We propose a SNR-based algorithm for weight setting at fusion center to improve the detector performance. Simulation results shows that the SNR-based method has better detector performance than the others.

Research on Spectrum Allocation Based on the Network Weights of Internet of Things

To solve the shortage defects of wireless spectrum source of Internet of things, an idle spectrum resources and immune sorting selection method based on the subjective and objective weight is proposed by using cognitive radio network technology. Firstly, the radio cognitive idle spectrum system is relied on to make the spectrum system modeling of Internet of things for the generation of objective and subjective weight perception, graph coloring of allocation of Internet of things and rectangular arrays; Then, the immune weight solving process is optimized and the weight setting rationality is improved; And idle spectrum performance of the spectrum resources is analyzed again, and it has the stability; Lastly, MATLAB is used to make simulation experiment. The result shows: the algorithm can not only make effective allocation to spectrum resources, greatly improving the spectrum utilization, but also meet radio transmission demand of Internet of things with high network throughput capacity and improve the transmission rate of Internet node with the stability.

Trusted Collaborative Spectrum Sensing for Mobile Cognitive Radio Networks

Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). Although mobility is an inherent property of wireless networks, there has been no prior work studying the performance of collaborative spectrum sensing under attacks in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at bad locations with severe path-loss. In this paper, we propose to use two trust parameters, location reliability and malicious intention (LRMI), to improve both malicious user detection and primary user detection in mobile CRNs under attack. Location reliability reflects path-loss characteristics of the wireless channel and malicious intention captures the true intention of secondary users, respectively. We propose a primary user detection method based on location reliability (LR) and a malicious user detection method based on LR and Dempster-Shafer (D-S) theory. Simulations show that mobility helps train location reliability and detect malicious users based on our methods. Our proposed detection mechanisms based on LRMI significantly outperforms existing solutions. In comparison to the existing solutions, we show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20% at false alarm rate of 5%, respectively.

A Novel DSA-Driven MAC Protocol for Cognitive Radio Networks

With the deployment of more wireless applications, spectrum scarcity becomes an issue in many countries. Recent reports show that the reason for this spectrum shortage is the underutilization of some spectrum re-sources. Fortunately, the emergence of open spectrum and dynamic spectrum assess (DSA) technology in cognitive radio networks relieves this problem. In this paper, we propose a novel DSA-driven cognitive MAC protocol to achieve highly efficient spectrum usage and QoS provisioning. In the proposed protocol, secondary users are divided into several non-overlapping groups, and all leftover channels are allocated among groups taking the groups’ bandwidth requirements into consideration. Moreover, the allocation of vacant channels can be adjusted dynamically when members join/leave groups or primary users return/leave the current network. Simulations show that the proposed MAC protocol greatly improves the quality of ser-vice for secondary users and maximizes the utilization ratio of spectrum resources.

Optimal spectrum sensing framework for cognitive radio networks

Spectrum sensing is the key enabling technology for cognitive radio networks. The main objective of spectrum sensing is to provide more spectrum access opportunities to cognitive radio users without interfering with the operations of the licensed network. Hence, recent research has been focused on the interference avoidance problem. Moreover, current radio frequency (RF) front-ends cannot perform sensing and transmission at the same time, which inevitably decreases their transmission opportunities, leading to the so-called sensing efficiency problem. In this paper, in order to solve both the interference avoidance and the spectrum efficiency problem, an optimal spectrum sensing framework is developed. More specifically, first a theoretical framework is developed to optimize the sensing parameters in such a way as to maximize the sensing efficiency subject to interference avoidance constraints. Second, in order to exploit multiple spectrum bands, spectrum selection and scheduling methods are proposed where the best spectrum bands for sensing are selected to maximize the sensing capacity. Finally, an adaptive and cooperative spectrum sensing method is proposed where the sensing parameters are optimized adaptively to the number of cooperating users. Simulation results show that the proposed sensing framework can achieve maximum sensing efficiency and opportunities in multi-user/multi-spectrum environments, satisfying interference constraints.