Primary User Signal Research Articles

GWOA: a hybrid genetic whale optimization algorithm for combating attacks in cognitive radio network

With the rapidly growing number of wireless users and systems more frequency bands are needed, which led to the spectrum scarcity problems. Cognitive radio (CR) is an emerging technology that enables the dynamic spectrum access by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users’ (PU) usage. Security threats is one of the substantial problems issues that must be addressed in CR networks. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. In this paper, a hybrid Genetic Whale Optimization Algorithm (GWOA) is proposed to optimize the spectrum utilization and to combat the PUE attacks. GWOA integrates the crossover and mutation operations with WOA algorithm to reach the balance between exploitation and exploration phases, to find the optimal solution based on a fitness function. The simulations results show promising performance of GWOA, when compared with recent detection algorithms.

Allocation of optimal energy in an energy-harvesting cooperative multi-band cognitive radio network

This paper studies the achievable total throughput of an energy harvesting cooperative cognitive radio (CR) network. A CR transmitter cooperates with a primary user (PU) transmission if PU is found to be present in the given channel while it transmits its own data in the absence of PU. The CR transmitter is an energy harvesting node which harvests simultaneously from non-RF signal as well as RF signal of PU. The CR transmitter uses the harvested energy on shared basis for cooperation and transmission. The same study is also extended for cooperation of multiple CRs in multiple PU band scenario. In cooperation in muti-band scenario, all CRs sense all PU channels and sensing informations are fused at fusion centre to know about the status of PU. Performance is investigated in terms of total network throughput for several parameters such as sensing time, energy splitting parameter, and energy allocation ratio etc. Novel analytical expressions for total useful throughput and optimal energy allocation ratio parameter under the considered network scenario are developed. Useful throughput and optimal energy allocation ratio parameter are also estimated for a target secondary network (CR network) throughput under a quality of service constraint of PU such as collision probability. It is observed that the value of optimal energy allocation parameter gets reduced if the number of CRs in cooperation increases or the number of PU channels increases.

Online Learning With Randomized Feedback Graphs for Optimal PUE Attacks in Cognitive Radio Networks

In a cognitive radio network, a secondary user learns the spectrum environment and dynamically accesses the channel, where the primary user is inactive. At the same time, a primary user emulation (PUE) attacker can send falsified primary user signals and prevent the secondary user from utilizing the available channel. The best attacking strategies that an attacker can apply have not been well studied. In this paper, for the first time, we study optimal PUE attack strategies by formulating an online learning problem, where the attacker needs to dynamically decide the attacking channel in each time slot based on its attacking experience. The challenge in our problem is that since the PUE attack happens in the spectrum sensing phase, the attacker cannot observe the reward on the attacked channel. To address this challenge, we utilize the attacker’s observation capability. We propose online learning-based attacking strategies based on the attacker’s observation capabilities. Through our analysis, we show that with no observation within the attacking slot, the attacker loses on the regret order, and with the observation of at least one channel, there is a significant improvement on the attacking performance. Observation of multiple channels does not give additional benefit to the attacker (only a constant scaling) though it gives insight on the number of observations required to achieve the minimum constant factor. Our proposed algorithms are optimal in the sense that their regret upper bounds match their corresponding regret lower bounds. We show consistency between simulation and analytical results under various system parameters.

Adaptive Full-Duplex Communications in Cognitive Radio Networks

In this paper, we propose a novel adaptive scheme for full-duplex communication of secondary users (SUs) in a cognitive radio network. The secondary network operates adaptively in three modes; cooperative sensing (CS), full duplex transmit and sensing (FDTS), and in-band bidirectional full duplex transmit and receive (FDTR). In the CS mode, the secondary nodes will detect the signal of a primary user (PU) through a novel cooperative medium access control (MAC) protocol and will decide the system's mode of operation in the subsequent spectrum hole. This adaptive decision is based on dual-threshold detection introduced for the first time. When the PU's signal at SU's receivers is weak, the system switches to the FDTS mode to avoid higher collisions probability and long or endless collision durations. In the FDTS mode, one of SUs senses the PU activity continuously while transmitting to another node. When the channel conditions allow, the system switches to the FDTR mode, in which the SUs would communicate bidirectionally in an asynchronous full duplex manner. The novel idea of asynchronous transmission in this mode will result in decreased maximum and average collision durations. Analytical closed forms for probability of collision, average collision duration, and cumulative collision duration, as well as throughput of the SU network are derived, and performance of the proposed scheme in terms of the above-mentioned metrics, effectiveness, and advantages over conventional methods of sensing and transmission are verified via simulations.

Security in Cognitive Radio Network: Defense against Primary User Emulation attacks using Genetic Artificial Bee Colony (GABC) algorithm

Due to the exponential increase in the number of wireless devices and in data rates demands, the shortage of the free frequency spectrum resources becomes a critical problem. Cognitive Radio Network (CRN) is considered as an emerging technology that eases off the problem of spectrum shortage by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users’ (PU) usage. Securing the CRN is a crucial problem that must be addressed. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. Many efforts had been made for securing the spectrum sensing process. In this paper, a hybrid Genetic Artificial Bee Colony (GABC) algorithm is proposed to optimize the spectrum utilization by detecting the PUE attacks and enhancing the probability of detection. GABC integrates the Genetic operators with ABC algorithm to reach the balance between exploitation and exploration to find the optimal solution. The simulations results show promising performance of GABC in optimizing spectrum sensing, when compared with recent detection algorithms.

Sources and scavenging of plutonium in the East China Sea

The 240Pu/239Pu atom ratio and 239+240Pu activity of seawater in the East China Sea (ECS) was measured in order to examine the Pu sources and elaborate Pu scavenging process. High 240Pu/239Pu atom ratios (0.187–0.243, average = 0.221 ± 0.017) in the surface water and water column were observed during 2011, implying of non-global fallout Pu sources. The distribution of 240Pu/239Pu atom ratio in the ECS was in agreement with the introduction pathway of the Kuroshio, showing a decreasing trend away from the outer shelf. An even higher 240Pu/239Pu atom ratios (0.243–0.263, average = 0.253 ± 0.007) were observed in the Kuroshio, indicating the non-global fallout Pu signal from the Pacific Proving Grounds (PPG). Using a two end-member mixing model, the Pu source from the PPG contribution was calculated to be 36 ± 9% in the ECS seawater. The 239+240Pu activities of surface seawater were in the range of 2.00–2.95 mBq m−3 in the ECS. The spatial distribution of 239+240Pu activity in the surface seawater showed an increasing trend from the outer shelf to the nearshore. Moreover, 239+240Pu inventory of water column at the station DH23 in the ECS was calculated to be ~0.29 Bq m−2, which was 1–3 orders of magnitude lower than the estimates of sediment cores in the ECS shelf (9–407 Bq m−2). Such differences were determined by the high degree Pu scavenging efficiency in the ECS and high Pu input carried by terrestrial sediments from the Yangtze River. Finally, both 240Pu/239Pu atom ratios and 239+240Pu activities were identical before and after the Fukushima nuclear accident (FNA), suggesting that the impact of the FNA on the ECS was negligible.

On Channel Hopping Sequences With Full Rendezvous Diversity for Cognitive Radio Networks

In cognitive radio networks, establishing a communication link between a pair of secondary users (SUs) requires them to rendezvous on a commonly available channel. Asynchronous channel hopping sequences with full rendezvous diversity (AFCHS) enable a pair of SUs to meet on every channel without requiring clock synchronization, and hence minimize rendezvous failures due to the appearance of primary user signals. Maximum conditional time-to-rendezvous (MCTTR) and maximum time-to-rendezvous (MTTR) are two primary metrics for evaluating the worst-case time-to-rendezvous of AFCHS under two extreme scenarios. In this letter, we derive lower bounds on the MCTTR and MTTR of AFCHS, and then propose a scheme with the minimum MCTTR and small MTTR. When the number of channels is odd, the proposed scheme has the minimum MTTR.

Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter

Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results.

Sliding-Window-Based Detection for Spectrum Sensing in Radar Bands

Recently, radar frequency bands have been drawing much attention as possible candidates for cognitive radio, since their utilization was revealed to be relatively low. One important functionality of a cognitive radio system is the spectrum sensing, which detects the presence of a primary user (PU) signal. The pulse radar signal, which is the PU signal in radar bands, is characterized by its sparsity in the time domain. Exploiting this sparsity, we present a spectrum sensing technique that combines the intermediate detections from multiple small sliding sensing windows of the received radar signal into a final decision. We also show by computer simulation that it can achieve improvement of the sensing performance in comparison with the conventional single-window-based spectrum sensing approach.

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to the PU, while the CR system should have enough data transmission time to maximize the transmission opportunity of the SU. Therefore, the sensing time and data transmission time of the SU are generally considered as main optimization parameters to maximize the throughput of the CR system. In this paper, a separate sensing node is designated and the sensing is continuously performed using the interference alignment (IA) technique. In this paper, the designated sensing node estimates the interference ratio and transmission opportunity loss ratio. To satisfy the primary user’s interference requirement and maximize secondary throughput, we proposed dynamic adjustment mechanism for sensing slot time and sensing report interval using reinforcement learning in time-varying communication environment. The experimental results show that the proposed approach can minimize the interference on PU and enhance the transmission opportunity of SUs.

A Sensing Contribution-Based Two-Layer Game for Channel Selection and Spectrum Access in Cognitive Radio Ad-hoc Networks

In cognitive radio (CR) networks, the secondary users (SUs) sense the spectrum licensed to the primary users (PUs) to identify and possibly transmit over temporarily unoccupied channels. Cooperative sensing was proposed to improve the sensing accuracy, but in heterogeneous scenarios, SUs do not contribute equally to the cooperative sensing result because they experience different received PU signal quality at their sensors. In this paper, a two-layer cooperative game is developed for distributed sensing and access in multichannel CR ad hoc networks, where the SUs’ transmission opportunities are commensurate with their sensing contributions, thus fostering cooperation and eliminating free-riders. Numerical results show that the proposed two-layer game is computationally efficient and outperforms previously investigated collaborative sensing and spectrum access approaches in heterogeneous multichannel CR scenarios in terms of energy efficiency, throughput, SU fairness, and complexity. Moreover, it is demonstrated that this game is robust to changes in the network topology and the number of SUs. Finally, a new physical-layer approach is proposed to distribute the network-level miss-detection constraints fairly among the interfering SUs for guaranteed PU protection and demonstrate the performance advantages of the AND-rule combining of spectrum sensing results for heterogeneous SUs.

Performance optimization for energy harvesting cognitive cooperative networks with imperfect spectrum sensing

In this paper, considering imperfect spectrum sensing in a cognitive cooperative system, we study the performance optimization for throughput maximization of secondary user (SU) and average delay minimization under maximum delay constraint of primary user (PU) with harvested energy from radio frequency signal of active PU by cooperative SU. We use a one-dimension linear search method to decompose the two optimization problems due to the non-convexity of the original optimization. We prove that the above maximization and minimization have same forms of solution under the same constraints. Simulation results indicate that the throughput performance of SU is higher than that of traditional cognitive system.

Simultaneous cooperative spectrum sensing and wireless power transfer in multi-antenna cognitive radio

In multi-antenna cognitive radio (CR), the sensing performance of detecting the presence of primary user (PU) in fading channel can be improved through multi-antenna cooperative spectrum sensing. However, the CR may consume more stored energy due to the cooperative spectrum sensing and thus decrease its transmission performance. In this paper, to guarantee the transmission performance, a simultaneous cooperative spectrum sensing and wireless power transfer (SCSSWPT) scheme has been proposed, which can harvest the radio frequency (RF) energy of the PU signal to supply the consumed energy of spectrum sensing. Time splitting model, power splitting and antenna splitting model are proposed to implement cooperative spectrum sensing, energy harvesting and data transmission simultaneously. Three optimization problems have been formulated to maximize spectrum efficiency of the CR in the three SCSSWPT models, respectively, subject to the constraints of detection probability and harvested energy. The simulation results have shown that there is an optimal number of sensing antennas to maximize the spectrum efficiency of the CR, and the time splitting model can achieve higher spectrum efficiency.

Secure Non-Consensus Based Spectrum Sensing in Non-Centralized Cognitive Radio Networks

In a non-centralized cognitive radio network, secondary users (SUs) cooperate in order to determine the presence of the primary user (PU) signal. This can be accomplished by each node sharing its spectrum sensing result with its neighboring SUs. In this paper, a non-consensus based distributed spectrum sensing (NCSS) scheme is presented, in which a node uses the raw energy level of the PU signal from its $h$ -hop neighbors for determining the status of the PU. A message passing scheme is used rather than the conventional consensus scheme. A comparison with an existing consensus algorithm is also shown. Further, two types of spectrum sensing data falsification attacks are considered in which the attackers inject very high or very low energy values. To catch such abnormal behavior of a node, an approach called secure NCSS algorithm is proposed, which isolates a node that generates extreme energy values so that it is removed from future computation. For this, an outlier detection technique is adopted. The approach is based on the idea that a malicious node’s energy level deviates significantly from those of genuine nodes, whereas all genuine nodes share nearby estimated energy levels.

Spectrum Sensing in Cognitive Radio by Use of Volume-Based Method

Spectrum sensing is the mission of finding the licensed user signal situation, i.e. to determine the existence and deficiency of primary (licensed) user signal, the recent publications random matrix theory algorithms performs better-quality in spectrum sensing. The RMT fundamental nature is to make use of the distributed extremal eigenvalues of the arrived signal sample covariance matrix (SMC), specifically, Tracy-Widom (TW) distribution which is useful to certain extent in spectrum sensing but demanding for numerical evaluations because there is absence of closed-form expression in it. The sample covariance matrix determinant is designed for two novel volume-based detectors or signal existence and deficiency cases are differentiated by using volume. Under the Gaussian noise postulation one of the detectors theoretical decision thresholds is perfectly calculated by using Random matrix theory. The volume-based detectors efficiency is shown in simulation results.

Secondary users selection and sparse narrow-band interference mitigation in cognitive radio networks

Spectrum scarcity is a critical problem that may reduce the effectiveness of wireless technologies and services. To address this problem, different spectrum management techniques have been proposed in the literature such as overlay cognitive radio (CR) where the unlicensed users can share the same spectrum with the licensed users. The main challenges in overlay CR networks are the identification and detection of the Primary User (PU) signals in a multi-source narrow-band interference (NBI) scenario. Therefore, in this paper, we investigate the performance of an orthogonal frequency division multiplexing (OFDM) overlay CR network with Secondary Users (SUs) and subcarriers selection schemes. Three approaches for SUs and subcarriers Selection named Direct, Distributed and Incremental selection techniques are proposed in this paper to increase the expected signal to interference and noise ratio based on full or partial knowledge of the channel state information (CSI). We also show that Distributed selection techniques provide all the SUs equal chances to be selected without affecting the selection diversity gain. General as well as simplified outage probability expressions are derived and extensive simulations are conducted to evaluate the performance of the proposed techniques and support the theoretical derivations. To accommodate more SUs, a new approach for asynchronous NBI estimation and mitigation in CR networks is investigated. Without any prior knowledge of the NBI characteristics and based on sparse signal recovery theory, the proposed approach allows the PU to exploit the sparsity of the SUs interference to recover it and approach the interference-free limit over practical ranges of NBI power levels.

Energy Efficient Statistical Cooperative Spectrum Sensing in Cognitive Radio Networks

Cooperative spectrum sensing (CSS) alleviates the problem of imperfect detection of primary users (PU)s in cognitive radio (CR) networks by exploiting spatial diversity of the different secondary users (SUs). The efficiency of CSS depends on the accuracy of the SUs in detecting the PU and accurate decision making at the fusion center (FC). This work exploits the higher order statistical (HOS) tests of the PU signal for blind detection by the SUs and combination of their decision statistics to make a global decision at the FC. To minimize energy, a two stage optimization paradigm is carried out, firstly by optimal iterative selection of SUs in the network using Lagrange criterion and secondly optimized fusion techniques achieved by Neyman Pearson. The probability of detecting the PU based on HOS and hard fusion schemes is investigated. The results indicate that the Omnibus HOS test based detection and optimized majority fusion rule greatly increases the probability of detecting the PU and reduces the overall system energy consumption.

Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements

The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map using field measurements obtained by cyclostationary-based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to the widely used energy detectors, such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting, ordinary kriging, and universal kriging. The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum sensors measuring eight digital video broadcasting-terrestrial (DVB-T) channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross-validation approach with the widely used root mean square error as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator. Comparison results clearly show the performance improvement and robustness obtained using cyclostationary based detectors instead of energy detectors.

Performance of an Energy harvesting Cooperative Cognitive Radio Network with Hybrid Spectrum Access Scheme

In this paper, a hybrid spectrum access scheme is investigated for a relay based energy harvesting cooperative cognitive radio (CR) network. In the considered network model, a relay harvests energy from RF signal of primary user (PU) as well as from RF signal of CR also. The relay node forwards the CR data to the respective destination and also cooperates in PU transmission as well. The CR follows a hybrid spectrum access scheme, i.e., it employs an underlay scheme of co-existence if PU is found to be present in the given channel else it switches to the overlay scheme of transmission. In overlay scheme, a CR transmits with maximum transmission power while in underlay scheme, it controls its transmission power based on the knowledge of channel state information of the link between CR and PU receiver, to limit the interference at PU receiver. Three new ratio parameters namely, PU–CR time allocation parameter, PU two hop transmission parameter and CR two hop transmission parameter are considered to model the cooperative transmission of PU and CR network where the parameter β takes care of the allocated time for PU and CR transmission and the other two parameters θ and ϕ take care of two hop transmission for PU and CR respectively. Analytical expressions of combined throughput of a CR and PU are developed under the considered network scenario. The performance is investigated in terms of combined throughput of CR, combined throughput of PU, and the overall network throughput for several sensing parameter and new ratio parameters.

A Recursive Algorithm for Wideband Temporal Spectrum Sensing

Wideband spectrum sensing techniques determine which portions of a given spectrum band are occupied or idle in the frequency domain. The idle portions represent spectrum holes that can potentially be exploited by secondary or unlicensed users. Existing methods for wideband sensing, however, do not take into account the temporal activity of the primary or licensed users within the spectrum band. We propose an algorithm that identifies primary user activity over a wide spectrum band and provides a statistical characterization of the primary user signals in the band. The algorithm applies hidden Markov modeling to a hierarchically partitioned representation of the spectrum band, together with a recursive tree search. Different from existing wideband sensing algorithms, the proposed wideband temporal sensing method is able to accurately detect spectrum holes even in the presence of bursting primary user signals. Moreover, the hidden Markov modeling of the primary user signals enables the accurate detection and the prediction of primary user activity over time. Numerical results demonstrate the significant performance gain of the proposed algorithm over existing wideband spectrum sensing algorithms, particularly in the presence of low duty-cycle primary user signals.