Presence Of Primary User Research Articles

Cognisseum: Cognitive radios on Colosseum facing adversaries

Cognitive radio technology brings a lot of interesting features which affect the transmission and reception properties of modern communication devices. Dynamic spectrum sensing, channel hopping and allocation, and software-based control are among the many. The new features allow strategic defense mechanisms while also enabling more capable adversarial attacks. In this work, we study coalitions of secondary users (SUs) against adversaries. In the presence of primary users (PUs), we inspect the behavior of SU pairs in cognitive radio networks, before and after adversarial attacks. We propose algorithms for forming coalitions among SU pairs. We consider two attack strategies for the adversaries: smart or naïve. We study how the channels are allocated if there is an attack and how the payoffs of those SU pairs vary with varying number of channels. We also show the effects of attack from the attackers’ point-of-view and how the attack strategy changes if the adversaries act smart vs. naïve. Using Colosseum, a large-scale wireless channel emulator, we construct a functional cognitive radio network and use its software-defined radio (SDR) hardware as SU and adversarial nodes. Using this setup, we run experiments and record data by running network performance measurement tool iPerf3 for various coalitional setups.

Hybrid Cognition for Target Tracking in Cognitive Radar Networks

This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. This is a difficult problem since the network must seek the optimal assignment from nodes to channels, rather than just seek the best overall channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. We define hybrid cognition as the condition where both the independent radar nodes as well as the central coordinator are modeled as cognitive agents, with restrictions on the amount of information that can be exchanged between the radars and the coordinator. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel. We provide several reward functions which capture different aspects of the dynamic radar scenario and describe the online machine learning algorithms which are applicable to this structure. In addition, we study varying levels of feedback, where central coordinator update rates vary. We compare our algorithms against baselines and demonstrate dramatic improvements in convergence time over the prior art. A network using hybrid cognition is able to use a minimal amount of feedback to achieve much faster convergence times and therefore lower tracking error.

Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

AbstractCognitive radio (CR) technology is beneficial for unmanned aerial vehicles (UAVs) to access the spectrum resources needed for communication. Then, in order to integrate CR technology into UAVs without causing excessive interference to the primary users (PUs), cooperative spectrum sensing (CSS) is used as a key function of CR technology to detect the PUs for the UAV's available spectrum. However, the flexible locations of UAVs may significantly degrade the performance and efficiency of CSS within them. Hence, on the basis of a cognitive UAV networks (CUAVN) framework, we propose an internal cooperation paradigm to achieve the cooperative gain of CSS within an UAV. Furthermore, we convert the decision rule as an optimal stopping problem to optimize the decision rule for CSS. The optimization problem is carried out by the error probability regarding the PU presence of the phenomenon and the stopping time required to reach a global decision about the PU status. By optimizing the error probability respectively under the condition of a fixed sensing time, the optimization problem is transformed into an optimal stopping problem and solved by Markov stopping theory. Following this, we further determine a pair of thresholds in the optimal sequential decision rule from the minimal cost function. At last, numerical simulation results verify the correctness and effectiveness of the optimal sequential detection rule, and corroborate that in contrast to the external cooperative paradigm, the superiority of the internal cooperative paradigm under various network environments and parameters, in terms of the CSS performance and the stopping time.

A variational Bayesian approach to multiantenna spectrum sensing under correlated noise

In this paper, we address the problem of spectrum sensing when the secondary user (SU) is equipped with a multiantenna receiver and noise across antenna elements are correlated. Covariance matrix of correlated noise is non-diagonal and its standard-condition-number (SCN) is much greater than one, which results in significantly degraded detection performance. Many of the existing techniques either whiten the covariance matrix or utilize geometrical properties of noise covariance matrix for the design of decision statistic. However, these techniques have low detection performance in uncorrelated noise or when the correlation is low. To address this problem and further improve the detection performance, a novel spectrum sensing algorithm based on variational Bayesian Gaussian mixture model (VBGMM) is proposed. Specifically, a new decision vector is proposed which is amenable to VBGMM and incorporates signal features suitable for spectrum sensing in both correlated and uncorrelated noise. Decision vectors extracted from the received signal are clustered using variational expectation-maximization (VEM) algorithm to detect the presence of primary user (PU). Compared to existing techniques, the proposed method is shown by simulation to achieve higher detection rates for the entire range of correlation.

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks.

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

Multiantenna Spectrum Sensing for Correlated Signal in Spatially Correlated Noise Environments

The multiantenna spectrum sensing (MSS) problem for correlated signal is addressed in this correspondence. First, the zero-lag and non-zero-lag covariance matrices are established, respectively, to capture the correlation feature of primary signals. Then, two scenarios are considered: 1) the noises at different antennas are spatially white; 2) the noises at different antennas are spatially correlated. Afterward, two variants of the covariance detection method are devised to detect the presence of primary user. Also, we derive the approximate closed-form expressions to calculate the decision thresholds of the proposed two methods. Simulation results are presented to demonstrate the superiority of the proposed two methods over the conventional MSS methods.

Implementation of cognitive radio networks for optimum spectrum utilization through feed forward backpropagation artificial neural network

In wireless Radio Frequency (RF) networks, a huge amount of available bandwidth of the RF spectrum is wasted, because the users are not able to fully utilize the frequency band of the spectrum. The Cognitive Radio Networks provide an effective solution for optimum utilization of the frequency band of the RF spectrum. Cognitive Radio Networks consist of two types of users: primary users, which utilizes their allocated license frequency band of the RF spectrum and secondary users, which uses the license frequency band of the primary users when they are not using the RF spectrum. Primary users pre-empt the secondary users when they want to use the RF spectrum. To know when the spread spectrum is not occupied by the primary users, the secondary users must have some mechanism to sense the environment, learn the parameters and take a decision on the basis of it. The users of Cognitive Radio Networks enhance these capabilities by using Artificial Neural Networks (ANN). This paper discusses how the presence of primary users in the particular frequency band of the spectrum could be detected by the secondary users by using Feed Forward Back Propagation ANN. For this a CRN simulation model is designed and developed which uses a Feed forward back propagation ANN for optimum use of the spectrum.

Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network

Cognitive Radio Network (CRN) has turn up to solve the issue of spectrum congestion occurred due to the wide spread usage of wireless applications for 6G based Internet of Things (IoT) network. The Secondary Users (SUs) are allowed to access dynamically the frequency channels owned by the Primary Users (PUs). In this paper, we focus the matter of contention of routing in multi hops setup by the SUs for a known destination in the presence of PUs. The traffic model for routing is generated on the basis of Poison Process of Markov Model. Every SU requires to reduce the end-to-end delay and packet loss of its transmission simultaneously to improve the data rate for the Quality of Service (QoS) of the Secondary Users. The issue of routing is formulated as stochastic learning process of non-cooperative games for the transformation of routing decisions of SUs. We propose a distributed non-cooperated reinforcement learning based solution for solving the issue of dynamic routing that can avert user interferences and channel interferences between the competing Sus in 6G-IoT network. The proposed solution combines and simulate the results to show the effectiveness and working of the proposed solution in decreasing the end-to-end delay, packet loss while meeting the average data rate requirement of QoS for SUs.

Spectrum Monitoring Based on Error Vector Magnitude and Received Signal Strength Indicator

Wireless communication systems may suffer from interferences caused by other emitters. Modern research and development on these systems is focusing on a more dynamic spectrum usage in order to mitigate or avoid such interferences. Nevertheless, this adaptability requires detecting them by either spectrum sensing or spectrum monitoring. While spectrum sensing observes the electromagnetic spectrum regarding the presence of signals, spectrum monitoring exploits the reception metrics during a communication for detecting interferences. This paper proposes a spectrum-monitoring algorithm that is making use of the error vector magnitude of the received signals. As a high error vector magnitude may indicate both an interference and a low signal power, the received signal strength indicator is used to distinguish these cases. The primary user presence is identified based on linear classification by the help of support-vector machines, which take the error vector magnitude, the received signal strength indicator, and the modulation order as input. The success of the spectrum-monitoring algorithm is demonstrated based on simulations of quadrature amplitude modulated signals.

Joint optimisation techniques for trade-off aware spectrum sensing in cognitive radio network

Cognitive radio (CR) network is considered a promising domain to enhance spectrum efficiency to access underutilised frequency bands. However, due to the influence of channel fading and shadowing, accuracy in primary user (PU) detection by CR gets hampered. This paper designs a joint optimisation technique for spectrum sensing in CR network to optimise energy, delay, and throughput with increased sensing accuracy. Initially, simple energy detection is exhibited for sensing the presence of PU in band. Further, the algorithm is developed to achieve an energy-throughput trade-off, and delay-throughput trade-off. Hence, the optimisation algorithm for detecting energy, reducing delay, and enhancing throughput are developed to optimise complete sensing performance. Furthermore, the joint optimisation model assists in acquiring trade-offs amongst energy, delay, and throughput. The assessment of the technique is performed using delay, energy, and throughput. Moreover, the software-defined radio (SDR) configuration is performed for validating the result.

Gini Index Test Metric Based Collaborative Sensing of Spectrum for Cognitive Networks

The key confront for a cognitive radio network is to identify the presence of primary users consistently so as to curtail the intrusion to license approved communications. Consequently, sensing of spectrum is a main significant requisite of a cognitive network. Nevertheless, because of the ambiguity in the channel, local interpretations don’t provide a trustworthy solution and therefore collaboration is needed between the users. Collaborative spectrum sensing is a competent scheme to enhance the spectrum utility in wireless networks, which utilizes cooperation between multiple nodes to prevail over the inadequacy of single-node to improve the performance of detection. In this paper, a detailed investigation, simulation and comparative analysis of collaborative scheme for sensing the spectrum of cognitive networks based on Gini Index test metrics is done and the results are compared with various other detecting schemes. The simulation is accomplished by means of Matlab R2014 software.

Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

Analysis of energy‐efficient cooperative spectrum sensing with improved energy detectors and multiple antennas over Nakagami‐q/n fading channels

SummaryCognitive radio (CR) is an advanced technique used for the utilization of radio spectrum. A CR node uses the radio spectrum of a licensed user (called as primary user, PU) deviously. In this work, a cooperative sensing network (CSN) with improved energy detection (IED)‐based CR nodes is proposed. Each CR node consists of multiple antennas and perform themselves selection diversity (SC) technique. Each CR node senses a PU through erroneous sensing channel and reports its sensing data (1‐bit binary‐decision) to the fusion center (FC) through erroneous reporting channels. At FC, existence of PU (presence or absence) is identified using k‐out‐of‐N fusion rule. The novel expressions for detection probability over noise plus Nakagami‐q/n fading channels are derived and the mathematical frameworks for analysis of throughput and energy‐efficiency of the proposed network are developed. The simulation testbed is also developed and presented in this work to validate the analytically obtained performance characteristics. For several network parameters values, the investigation on comparison between IED and traditional energy detection method for various decision fusion rules is studied. Further, performance characteristics are analytically illustrated for throughput and energy‐efficiency over noise plus Nakagami‐q/n fading channels. The impact of diversity technique and effect of fading severity parameters on the throughput and energy‐efficiency are also investigated. The effect of channel error on total error performance for both proposed and conventional networks is shown. Finally, optimal fusion rule and optimal network parameters values correspond to maximum throughput and maximum energy‐efficiency are also identified.

HCR-WSN: Hybrid MIMO cognitive radio system for wireless sensor network

Multi Input Multi Output (MIMO) based Cognitive Radio (CR) provisions an efficient utilization of the scarce electromagnetic spectrum. Traditional CR includes Interweave and Underlay techniques. We propose a Hybrid MIMO CR system for Wireless Sensor Network, HCR-WSN, that utilizes both underlay and interweave CR techniques and provides higher spectral efficiency. The wireless sensor network consisting of CR nodes achieves higher throughput using the proposed framework.HCR-WSN improves the spectral efficiency gain of an Uplink Hybrid Cognitive Radio MIMO system in wireless sensor network (WSN), by implementing Narrowband and Wideband (Multiple Carrier Direct Sequence Code Division Multiple Access, i.e., MC-DS-CDMA) technology adhering to the Interference Threshold limit. The WSN is consisting of nodes acting as primary users (PU) and CR secondary users (SU). The proposed Hybrid CR system ensures efficient spectral utilization under circumstances resulting in near zero throughput for SU. In the presence of PU, CR-SU transmits in either Narrowband or MC-DS-CDMA based on the proposed transmission selection algorithm. In the absence of PU, SU uses Interweave technique thereby allowing other competing SUs to transmit in Interference Alignment underlay mode in a multi-user scenario. The achievable throughput is enhanced using the proposed technique in a multi-user hybrid MIMO cognitive radio wireless sensor network.

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

Dynamic Spectrum Sensing Under Crash and Byzantine Failure Environments for Distributed Convergence in Cognitive Radio Networks

In cognitive networks, efficient spectrum sensing is of great importance for communication of unlicensed secondary users (SU) without interfering with the communication of licensed primary users (PU). Such spectrum sensing requires robust and reliable communication between the SUs to sense the spectrum efficiently under different network circumstances and to make a quick decision for the data transmission. In this paper, we are proposing a decentralized cooperative algorithm for efficient sensing of spectrum in the networked cognitive radios. The proposed algorithm is investigated under crash and Byzantine failure environments to study their behavior and efficiency for consensus. Energy detector module is modeled for each cooperating SU in cognitive radio network for sensing the presence of PU in a dedicated spectrum. Moreover, SU is modeled as agents connected through undirected graphs to simulate communication among them related to the spectrum availability. Multiple simulation scenarios, based on autonomous SU using the proposed distributed consensus algorithm are presented to demonstrate the theoretical development of proposed algorithm to be visualized in real scenarios. The simulation results reveal that the proposed method provides a significant improvement in convergence rate, reliability, and in terms of various key performance indicators.

Error rate detection due to primary user emulation attack in cognitive radio networks

Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.

Future migration: Key environmental indicators of Pu accumulation in terrestrial sediments of Queensland, Australia

Plutonium (Pu) interactions in the environment are highly complex. Site-specific variables play an integral role in determining the chemical and physical form of Pu, and its migration, bioavailability, and immobility. This paper aims to identify the key variables that can be used to highlight regions of radioecological sensitivity and guide remediation strategies in Australia. Plutonium is present in the Australian environment as a result of global fallout and the British nuclear testing program of 1952–1958 in central and west Australia (Maralinga and Montebello islands). We report the first systematic measurements of 239+240Pu and 238Pu activity concentrations in distal (≥1000 km from test sites) catchment outlet sediments from Queensland, Australia. The average 239+240Pu activity concentration was 0.29 mBq.g −1 (n = 73 samples) with a maximum of 4.88 mBq.g −1.238Pu/239+240Pu isotope ratios identified a large range (0.02–0.29 (RSD: 74%)) which is congruent with the heterogeneous nuclear material used for the British nuclear testing programme at Maralinga and Montebello Islands. The use of a modified PCA relying on non-linear distance correlation (dCorr) provided broader insight into the impact of environmental variables on the transport and migration of Pu in this soil system. Primary key environmental indicators of Pu presence were determined to be actinide/lanthanide/heavier transition metals, elevation, electrical conductivity (EC), CaO, SiO2, SO3, landform, geomorphology, land use, and climate explaining 81.7% of the variance of the system. Overall this highlighted that trace level Pu accumulations are associated with the coarse, refractive components of Australian soils, and are more likely regulated by the climate of the region and overall soil type.

Mitigation of the spectrum sensing data falsifying attack in cognitive radio networks

ABSTRACT Cognitive radio networks (CRNs), which offer novel network architecture for utilising spectrums, have attracted significant attention in recent years. CRN users use spectrums opportunistically, which means they sense a channel, and if it is free, they start transmitting in that channel. In cooperative spectrum sensing, a secondary user (SU) decides about the presence of the primary user (PU) based on information from other SUs. Malicious SUs (MSUs) send false sensing information to other SUs so that they make wrong decisions about the spectrum status. As a result, an SU may transmit during the presence of the PU or may keep starving for the spectrum. In this paper, we propose a reputation-based mechanism which can minimise the effects of MSUs on decision making in cooperative spectrum sensing. Some of the SUs are selected as distributed fusion centres (DFCs), that are responsible for making decisions about the presence of PU and informing the reporting SUs. A DFC uses weighted majority voting among the reporting SUs, where weights are normalised reputation. The DFC updates reputations of SUs based on confidence of an election. If the majority wins by a significant margin, the confidence of the election is high. In this case, SUs that belong to the majority gain high reputations. We conduct extensive simulations to validate our proposed model.

Machine Learning-Enabled Cooperative Spectrum Sensing for Non-Orthogonal Multiple Access

In this paper, multiple machine learning-enabled solutions are adopted to tackle the challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms (K-Means clustering and Gaussian mixture model) as well as supervised learning algorithms (directed acyclic graph-support vector machine, K-nearest-neighbor and back-propagation neural network). In these solutions, multiple secondary users (SUs) collaborate to perceive the presence of primary users (PUs), and the state of each PU need to be detected precisely. Furthermore, the sensing accuracy is analyzed in detail from the aspects of the number of SUs, the training data volume, the average signal-to-noise ratio of receivers, the ratio of PUs' power coefficients, as well as the training time and test time. Numerical results illustrate the effectiveness of our proposed solutions.