Detection Performance Of Spectrum Research Articles

A Collaborative Spectrum Sensing Algorithm Based on Reputation Update against Malicious User Attacks

In cognitive radio networks, collaborative spectrum sensing (CSS) algorithms could improve spectrum detection performance; however, most explorations are based on reliable network environments. In the real network environment, there may be malicious users that bring wrong spectrum sensing results and attacks designed by them remarkably reduce the spectrum efficiency. In order to resist the attacks of malicious users, this paper proposes a CSS method based on the reputation update. By setting an appropriate reputation threshold, the user fusion center selects the sensing user with a higher reputation to participate in the CSS. Each user’s reputation value is then updated according to whether its local sensing result matches the final judgment result. This article chiefly discusses scenarios for application of three information fusion rules. The simulation results reveal that the proposed approach with reputation update outperforms the conventional CSS algorithm for a variety of judgment rules. The proposed algorithm is capable of preventing lower reputation users from participating in the CSS, filtering out malicious users, and eliminating the impact of malicious users’ attacks.

Fusion Schemes Based on IRS-Enhanced Cooperative Spectrum Sensing for Cognitive Radio Networks

The detection performance of cooperative spectrum sensing (CSS) is poor when there are obstacles blocking. Therefore, fusion schemes based on intelligent reflecting surface (IRS)-enhanced CSS are investigated. Existing fusion schemes can be divided into the soft combination and the hard combination. In the soft combination, each secondary user (SU) uploads the decision statistic to a fusion center (FC). Comparatively, during the hard combination, each SU reports the local 0/1 decision result to FC instead of decision statistic. In this paper, the equal gain combination (EGC) and the selection combination (SC) schemes are studied for the soft combination. The weighted hard combination (WHC) scheme is studied for the hard combination, and an optimal set combination (OSC) scheme is proposed based on signal-to-noise ratio (SNR). By using the definition of the non-centrality chi-square distribution and the moment-matching method, the closed-form expressions for the average probability of detection of all the schemes are derived. Simulation results reveal that the detection performance of IRS-enhanced CSS obviously outperforms the decode and forward (DF) relay case and the no IRS case. In addition, our proposed OSC are superior to the K-rank in terms of the detection performance.

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

A line spectrum detector based on improved coherent power spectrum estimation

The line spectrum detection method based on coherent power spectrum estimation is one of the important research directions of weak signal line spectrum detection. For the purpose of resolving the problem of phase compensation inaccurate introduced by limited frequency search accuracy with the existing coherent power spectral estimate method, a modified line spectrum detector is proposed. The zero padding is used to improve the line spectrum frequency search accuracy in discrete Fourier transform (DFT) for phase compensation calculations, thereby obtaining more accurate line spectrum frequencies and phase compensation, which further improves Line spectrum detection performance. The detection performances of three detections based on Welch’s method, coherently averaged power spectral estimate(CAPSE) and modified method are compared each other by computer simulations. The results show modified line spectrum detection can obtain a gain of 2-3dB, which verifies the effectiveness of the modified method.

Antenna Selection and Detection Performance on Correlation Based Detection Systems

The increase data size transmitted in digital communication systems necessitates the use of high-capacity communication channels for the transmission of these data. To increase the channel capacity, MIMO systems are widely used. Antenna selection in MIMO systems is very important for efficient use of communication sources. Cognitive Radio systems generally aim to increase spectrum efficiency by using MIMO systems. Therefore, Cognitive Radio must find empty spectrum regions with spectrum detection methods. Spectrum detection performance is related to the antenna numbers in MIMO systems. Therefore, this study examines the effect of antenna numbers on detection performance in correlation-based detection method. Thanks to this study, the relationship between channel capacity, detection performance and antenna number for MIMO systems was investigated.

Dynamic dual threshold cooperative spectrum sensing for cognitive radio under noise power uncertainty

The spectrum sensing performance depends on the accuracy of the detection about whether primary users are busy or idle. Previous studies on cognitive radio spectrum sensing have shown that the cooperation between secondary users can improve their spectrum detection performance in real cognitive networks. Aiming at the problem of threshold mismatch of energy detectors under noise power uncertainty, a cooperative spectrum sensing method with dynamic dual threshold is proposed. Firstly, the utility function is defined with the objective of minimizing the error probability of spectrum sensing, and the optimum threshold of energy detector is derived. Secondly, in order to mitigate the influence derived from noise uncertainty, an effective dynamic dual threshold adjustment mechanism is presented, and the optimizing combinative fusion rule is discussed with the prerequisite of the minimum global error probability. In addition, in view of insufficient number of cognitive users whose sensing results lie in decision zones, the parameter of credibility is defined to choose the secondary users with reliable local detection for final fusion. Simulation results show that our proposed method can mitigate the influence of noise uncertainty and increase the spectrum sensing accuracy compared with other existing methods.

An improved method for blind detection of multi-node cooperative spectrum based on correlation coefficient

According to the problem that the detection performance of the whole cooperative spectrum detection system is reduced due to the low Signal to noise ratio(SNR) of some perceived nodes in complex electromagnetic environment, an improved method of multi-node cooperative spectrum blind detection based on correlation coefficient is proposed. This method uses the characteristics of the received signal correlation function, combines the square rate combination to construct a new test statistic and deduces the threshold formula. This method effectively improves the problem of degraded cooperative spectrum detection performance due to the different SNR of each sensor node, improves the spectrum detection probability, and the computer simulation results verify the effectiveness of the method.

A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel

Spectrum sensing is the prerequisite of the realization of cognitive radio. So it is a significant part of cognitive radio. In order to stimulate the SUs to sense the spectrum, we combine the incentive mechanism of crowd-sensing with cooperative spectrum sensing effectively, and put forward a crowd cooperative spectrum sensing algorithm with optimal utility of secondary users (SUs) under non-ideal channel which we define SUs’ utility expectation functions related to rewards, sensing time and transmission power. Then, we construct the optimization problem of maximizing the utilities of SUs by optimizing the sensing time and the transmission power, and prove that this problem is a convex optimization problem. The optimal sensing time and transmission power are obtained by using the Karush-Kuhn-Tucker (KKT) conditions. The numerical simulation results show that the spectrum detection performance of algorithm, which we put forward, is improved.

Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks.

An explosive growth in vehicular wireless applications gives rise to spectrum resource starvation. Cognitive radio has been used in vehicular networks to mitigate the impending spectrum starvation problem by allowing vehicles to fully exploit spectrum opportunities unoccupied by licensed users. Efficient and effective detection of licensed user is a critical issue to realize cognitive radio applications. However, spectrum sensing in vehicular environments is a very challenging task due to vehicle mobility. For instance, vehicle mobility has a large effect on the wireless channel, thereby impacting the detection performance of spectrum sensing. Thus, gargantuan efforts have been made in order to analyze the fading properties of mobile radio channel in vehicular environments. Indeed, numerous studies have demonstrated that the wireless channel in vehicular environments can be characterized by a temporally correlated Rayleigh fading. In this paper, we focus on energy detection for spectrum sensing and a counting rule for cooperative sensing based on Neyman-Pearson criteria. Further, we go into the effect of the sensing and reporting channel conditions on the sensing performance under the temporally correlated Rayleigh channel. For local and cooperative sensing, we derive some alternative expressions for the average probability of misdetection. The pertinent numerical and simulating results are provided to further validate our theoretical analyses under a variety of scenarios.

Scalable and Robust ANN Based Cooperative Spectrum Sensing for Cognitive Radio Networks

Cognitive radio network (CRN) supports dynamic spectrum access addressing spectrum scarcity issue experienced by today’s wireless communication network. Sensing is an important task and cooperative spectrum sensing is used for improving detection performance of spectrum. The sensing information from individual secondary users is sent to fusion center to infer a common global decision regarding primary user’s presence. Various fusion schemes for decision making are proposed in the literature but they lack scalability and robustness. We have introduced artificial neural network (ANN) at fusion center thereby achieving significant improvement in detection performance and reduction in false alarm rate as compared to conventional schemes. The proposed ANN scheme is found capable to deal with scalability of CRN with consistent performance. Further, SNR of individual Secondary user is taken into consideration in decision making at fusion center. Moreover the proposed scheme is tested against security attack (malicious users) and inadvertent errors occurring at SUs are found to be robust.

Spectrum Sensing Optimization in an UAV-Based Cognitive Radio

In cognitive radio (CR), the detection performance of traditional spectrum sensing may be decreased due to the multipath fading and shadowing effect. However, unmanned aerial vehicle (UAV) can receive a higher strength signal without suffering severe fading and shadowing. In this paper, an UAV-based CR is proposed to improve spectrum sensing performance and access the idle spectrum. The UAV performs circular flight with the PU locating at the center, and the flight period is divided into sensing radian and transmission radian. Only when the absence of the PU has been detected within sensing radian can the UAV access the spectrum. The spectrum sensing optimization is formulated as an optimization problem, which seeks to maximize the effective throughput of the UAV by optimizing the sensing radian subject to the constraint of the interference throughput. Then, virtual cooperative spectrum sensing is proposed to improve sensing performance in fading channel, and the effective throughput is maximized by jointly optimizing local sensing radian and the number of sensing slots. The simulation results show that better UAV transmission performance can be obtained by optimizing spectrum sensing, and the virtual cooperative spectrum sensing outperforms the single spectrum sensing especially in deep fading channel.

Jettison the Defectives: A Robust Cooperative Spectrum Sensing Scheme in a Cognitive Radio Network

In this paper, we extenuate the effect of malicious cognitive sensors (MCSs) from the optimal combining of cooperative spectrum sensing scheme. We consider the cognitive radio network (CRN) that includes honest cognitive sensors (HCS) and MCSs; the former one, report accurate sensing data to the combining centre (CC), while the latter, due to its malfunctioning, reports CC a false data of dissimilar statistical distribution. Our sensing scheme includes the combined two-stage test at CC, namely Shapiro–Wilk’s normality test and log-likelihood ratio test. The former is used to identify MCS and to exclude them from the latter, which optimally combines the sensing information of HCS. Simulation results show that detection performance of proposed method bests fault-tolerant cooperative spectrum sensing (FTCSS) method by 44.9%, when 4 MCS exist in a CRN. Further, the receiver operating characteristics show that, the proposed method does not increase the event of false alarm, unlike equal gain combining scheme and FTCSS, when MCS increases the false alarm.

Detection performance analysis of spectrum sensing in cognitive radio networks with mobile secondary users

The majority of existing papers about spectrum sensing have the assumption that secondary users (SUs) are stationary. However, mobility is an essential feature of mobile communications networks. In this paper, the detection performance of spectrum sensing by mobile SUs was analyzed. Three performance metrics, i.e., detection probability, miss detection probability and false alarm probability, were thoroughly investigated. In our analysis, a critical variable was the real-time received primary user signal power by a mobile SU. Its probability distribution and mathematical expectation were analytically derived. Moreover, the three performance metrics in single-node spectrum sensing and multi-node collaborative spectrum sensing systems were also derived. Extensive simulations were performed. The results are consistent with the theoretical analysis. And it is concluded that SU mobility has a significant impact on the detection probability and the miss detection probability, but not on the false alarm probability.

Multiantenna GLR Detection of Rank-One Signals With Known Power Spectral Shape Under Spatially Uncorrelated Noise

We establish the generalized likelihood ratio (GLR) test for a Gaussian signal of known power spectral shape and unknown rank-one spatial signature in additive white Gaussian noise with an unknown diagonal spatial correlation matrix. This is motivated by spectrum sensing problems in dynamic spectrum access, in which the temporal correlation of the primary signal can be assumed known up to a scaling, and where the noise is due to an uncalibrated receive array. For spatially independent identically distributed (i.i.d.) noise, the corresponding GLR test reduces to a scalar optimization problem, whereas the GLR detector in the general non-i.i.d. case yields a more involved expression, which can be computed via alternating optimization methods. Low signal-to-noise ratio (SNR) approximations to the detectors are given, together with an asymptotic analysis showing the influence on detection performance of the signal power spectrum and SNR distribution across antennas. Under spatial rank- $P$ conditions, we show that the rank-one GLR detectors are consistent with a statistical criterion that maximizes the output energy of a beamformer operating on filtered data. Simulation results support our theoretical findings in that exploiting prior knowledge on the signal power spectrum can result in significant performance improvement.

Optimization algorithm of cognitive radio spectrum sensing based on quantum neural network

In recent years, with the development of mobile communications and Wireless Local Area Network (WLAN) as well as restrictions on limited spectrum resources, wireless spectrum resources are increasingly strained. Cognitive radio, put forward as a concept of dynamic use of spectrum, solved the problem of low spectrum utilization rate brought by the current static spectrum allocation scheme and greatly improved the utilization of the existing spectrum resources. In order to overcome the shortcomings of traditional spectrum sensing and improve spectrum detection performance under low signal-noise rate, this paper proposed a spectrum perception algorithm based on quantum neural network (QNN) and carried out an optimization study on the spectrum sensing algorithm of cognitive radio. Through the simulation experiment, we found that the improved QNN algorithm showed more excellent convergence performance and detection capability.

Line spectrum detection algorithm based on the phase feature of target radiated noise

In order to improve the performance of line spectrum detection, according to the feature that the underwater target radiated noise containing stable line spectrum, the differences of the phase difference between line spectrum and background noise, a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain. After phase difference alignment, the phase variance of line spectrum and the phase of background noise, respectively, are small and big in frequency domain, this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance, which can restrain the background noise disturbance, and enhance the signal to noise ratio (SNR). The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum, restrain the energy of background noise, and have better detection performance under lower SNR.

An EM Approach for Cooperative Spectrum Sensing in Multiantenna CR Networks

In this paper, a cooperative wideband spectrum sensing scheme based on the expectation–maximization (EM) algorithm is proposed for the detection of a primary user (PU) system in multiantenna cognitive radio (CR) networks. Given noisy signal observations from $N$ secondary users (SUs) over multiple subbands at a fusion center (FC), prior works on cooperative spectrum sensing often use the set of received subband energy as decision statistics over the sensing interval. However, to achieve satisfactory performance, knowledge of the channel state information (CSI) and the noise variances at all the SUs is required by these algorithms. To overcome this limitation, our proposed method, which is referred to as joint detection and estimation (JDE), adopts the EM algorithm to jointly detect the PU signal and estimate the unknown channel frequency responses and noise variances over multiple subbands in an iterative manner. Various aspects of this proposed EM-JDE scheme are investigated, including a reliable initialization strategy to ensure convergence under practical conditions and a distributed implementation to reduce communication overhead. Under the assumption of perfect estimation for the channel frequency responses and noise variances, we further show that the proposed EM-JDE converges to the maximum-likelihood (ML) solution, which serves as an upper bound on its performance. Monte Carlo simulations over Rayleigh fading channels show that the proposed scheme significantly improves the performance of spectrum detection by exploiting the diversity of the spatially distributed SUs with multiple antennas.

A Novel Approach to Wideband Spectrum Compressive Sensing Based on DST for Frequency Availability in LEO Mobile Satellite Systems

In LEO mobile satellite network, the L/S frequency availability is an essential task for global communication but entails several major technical challenges: high sampling rate required for wideband sensing, limited power and computing resources for processing load, and frequency-selective wireless fading. This paper investigates the issue of frequency availability in LEO mobile satellite system, and a novel wideband spectrum compressed signal detection approach is proposed to obtain active primary users (PUs) subbands and their locations that should be avoided during frequency allocation. We define the novel wideband spectrum compressed sensing method based on discrete sine transform (DST-WSCS), which significantly improves the performance of spectrum detection and recovery accuracy compared with conventional discrete Fourier transform based wideband spectrum compressed sensing scheme (DFT-WSCS). Additionally, with the help of intersatellite links (ISL), the scheme of multiple satellites cooperative sensing according to OR and MAJ decision fusion rules is presented to achieve spatial diversity against wireless fading. Finally, in-depth numerical simulations are performed to demonstrate the performance of the proposed scheme in aspect of signal detection probability, reconstruction precision, processing time, and so forth.

Cooperative Interference Game in cognitive radio hidden terminal scenario

Hidden terminal problem in spectrum sensing is one of the most challenging problems in cognitive radio network (CRN). To tackle this problem, we propose a novel Cooperative Interference Game scheme in this paper. The scheme adopted full duplex (FD) mode to sense over multiple sub-bands in an iterative manner without extra sensing devices. The implementation algorithm of the proposed scheme is consisted of three modules: the formulation of the maximum transmit power limitation of second user (SU); the self-interference cancellation coefficient; and the optimal location of SU for an optimized low collision probability. Monte Carlo simulation proved that compared with cooperative spectrum sensing, the proposed scheme significantly improves the performance of spectrum detection and mitigates hidden terminal problem to a large extent with less energy consumption.