基于改进混合蛙跳算法的认知无线电协作频谱感知

提出了一种改进的混合蛙跳算法（shuffled frog leaping algorithm,SFLA）,并提出了基于改进SFLA的认知无线电协作频谱感知方法,通过仿真对改进SFLA算法性能与传统SFLA算法性能进行了比较,并对本文提出的基于改进SFLA的协作感知方法与已有的基于修正偏差因子（modified deflection coefficient,MDC）的协作感知方法性能进行了比较.结果表明改进SFLA算法性能优于传统SFLA;基于改进SFLA的协作感知方法比MDC方法能获得更大的检测概率,验证 关键词： 认知无线电 频谱感知 混合蛙跳算法     

用于认知无线电协作频谱感知的混合蛙跳算法群体初始化技术

提出了一种用于认知无线电线性加权协作频谱感知的改进混合蛙跳算法(shuffled frog leaping algorithm, SFLA) 的群体初始化技术, 提出在SFLA初始群体中包含基于修正偏差因子所得的解, 从而改进算法初期性能. 仿真结果表明相比于传统群体初始化技术, 本文所提出的群体初始化技术能够以更快的速率得到期望解, 从而节约计算时间, 更有利于实时应用 关键词： 认知无线电 频谱感知 混合蛙跳算法 群体初始化     

Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.     

基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知

针对在实际宽带压缩频谱感知中难以预先获知宽带频谱稀疏度的问题,提出一种改进的稀疏度自适应匹配追踪(modified sparsity adaptive matching pursuit, MSAMP)算法,该算法在支撑集选择过程中对稀疏度进行了预估计。结合序贯压缩检测技术,给出了一种基于该算法的多认知用户合作场景下的宽带压缩频谱感知方法,理论分析和实验仿真结果表明,该方法可在频谱稀疏度先验知识缺少的情况下,有效提高宽带频谱感知性能。     

认知无线电网络中利用FEC和DE的多节点频谱感知算法

针对认知无线电网络(CRN)中空闲频谱感知困难的问题,本文提出了基于前向纠错和差分进化算法的多节点频谱感知算法。首先,利用基于差分进化算法的协同检测完成信号感知;然后,研究了信道噪声对频谱感知性能的影响;最后,分析了前向纠错技术在信道存在噪声时对频谱感知性能的影响。仿真实验将纠错和无纠错控制信道的不同信噪比作为依据,采用三种不同的检测方法评估了本文算法。仿真实验结果表明,在存在噪声的认知无线电网络中,本文算法提高了系统的性能和检测概率,且协同感知算法的性能随着节点数目的增加而提高,该算法适合应用于实时性要求较高的应用程序。     

A tree-structured DFT filter bank based spectrum detector for estimation of radio channel edge frequencies in cognitive radios

Cognitive Radio (CR) aims to provide efficient spectrum utilization in spectrum scarce wireless environments. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor the electromagnetic spectrum and detect unused bands of spectrum. Wideband spectrum sensing needs to be employed for better spectrum opportunity detection and interference avoidance both in the case of commercial and military applications. Accurate sensing needs to be employed for blocker detection in commercial systems such as LTE for the design of transmit/receive path. In military radios, the challenge lies in the robust detection of the location of the center frequencies and bandwidths of individual radio channels in the wideband input signal. In this paper, an energy detector based on tree-structured discrete Fourier transform based filter bank (TDFTFB) is proposed for detecting the edges of the channels in the spectrum. The proposed method is compared with the conventional wavelets based method for complexity and performance. The design example and simulations show that the gate count resource utilization of the proposed detection scheme is 22.9% lesser than the wavelets method at the cost of a slight degradation (0.5%) in detection accuracy. Over-the-air tests performed using Universal Software Radio Peripheral 2 (USRP2) and MATLAB/SIMULINK showed that the present method is not input specific whereas the conventional wavelet based approach depends on the spectral location of the input.     

Quadratic covariance matrix-based cooperative spectrum sensing method by using an evolutionary algorithm

Cognitive radio (CR) is a practical technology to solve the current low utilization of spectrum resources, and spectrum sensing is the most critical technique in a CR network. In this paper, a genetic simulated annealing algorithm based on quadratic covariance matrix and information geometry is proposed for cooperative spectrum sensing (CSS) to enhance the performance in the low signal-noise ratio (SNR). Firstly, the quadratic covariance matrix of cooperative secondary users (SUs) is used as the characteristic matrix to perform feature extraction. Secondly, based on the information geometry, the characteristic matrix is mapped on the statistical manifold to avoid information loss. Furthermore, the genetic simulated annealing algorithm is used to obtain a classifier on the statistical manifold, and the mutation process is improved by a new mutation operator to accelerate the convergence speed of the whole algorithm. Finally, the classifier is employed to implement spectrum sensing. In the simulation analysis, the proposed method has better spectrum sensing performance than the popular various methods under low SNR and faster convergence speed.     

基于随机矩阵最大特征值分布的频谱感知算法

现有频谱感知算法在低信噪比时检测性能较低且受虚警概率影响大,针对此问题,提出了一种基于wishart矩阵样本协方差矩阵最大特征值的分布特性的频谱感知算法。该算法利用最大特征值与几何平均特征值的比值,不需要主用户的先验知识,不敏感于噪声,对相关信号和独立同分布信号均具有较高的检测性能。仿真结果表明,所提算法受虚警概率的影响较小,检测性能高,并且在采样点数、协作用户数、信噪比及虚警概率较小的情况下,也能获得较好的检测性能。     

On spectrum sensing in cognitive radio CDMA networks with beamforming

In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%).     

Cooperative wideband spectrum sensing under reporting channel errors: Analysis and algorithm

This paper focuses on the performance analysis and the algorithm development for cooperative wideband spectrum sensing (CWSS) under imperfect reporting channels for cognitive radio (CR). The centralized approach with multiple distributed cooperating secondary users (CSUs) and a fusion center (FC) is considered for CWSS. Nakagami fading is used to model the channel between PUs and the CSUs. The CSUs equipped with multiple diversity antennas are considered to take advantage of both space and antenna diversity. In this work, the reporting channel model under erroneous reporting is proposed. The repetition code-based CWSS (RC-CWSS) algorithm is proposed for performance improvement. First, the modified, improved theoretical analysis of an existing algorithm called partial band Nyquist sampling-based CWSS (PBNS-CWSS) under no reporting errors is given. The theoretical analysis under an imperfect reporting channel is then carried out for the same algorithm. Then, the complete theoretical analysis for the proposed RC-CWSS algorithm is carried out. The theoretical analyses are verified using Monte-Carlo simulations. The analysis shows that the performance of CWSS is greatly affected by the reporting channel errors. Also, the RC-CWSS outperforms recently proposed state-of-the-art algorithms. Finally, the effects of different parameters on the performance of the proposed algorithm are also studied.     

Single decision reporting for cooperative spectrum sensing under erroneous feedback channels with Byzantine attack

Spectrum sensing based on a single user suffers from low detection performance due to fading, shadowing, and hidden node problems. Cooperative spectrum sensing (CSS) is thought to be a potential method to overcome these issues and improve detection performance in determining the available spectrum in cognitive radio (CR). However, CSS suffers in case of erroneous reporting channels, and it is also susceptible to Byzantine attacks by malicious users (MUs). In this paper, we first analyze the traditional CSS under erroneous feedback channels. And then, we extend the analysis to include erroneous reporting channels in the presence of the Byzantine attack. We propose a single decision reporting (SD-R) algorithm immune to erroneous reporting channels. The proposed algorithm also improves the performance under the Byzantine attack. With the proposed algorithm, MUs can attempt only false alarm attacks, whereas the miss detection attack is not possible. An extensive analysis is carried out, and the plots are shown to prove the superiority of the proposed algorithm.     

5G-based wideband cognitive radio system design with cooperative spectrum sensing

Since the 5G bandwidth is very large, there are a large number of non-continuous idle spectrum in 5G communication. In this paper, we have designed transmitter and receiver of a 5G-based wideband cognitive radio (CR) system with cooperative spectrum sensing, in order to improve transmission performance and avoid interference signals. Each CR user marks the spectrum availability for getting the sub-basis function through doing Inverse Fast Fourier Transform (IFFT) with the product of spectrum marker vector and random phase vector. The cooperative spectrum sensing can be realized by cascading the sub-basis functions of all the users. Multiple access of the CR system is also proposed to access much non-continuous idle spectrum. The simulation results have shown that the proposed CR system can avoid the interference effectively and outperform the spread spectrum system obviously.     

Performance evaluation of improved double-threshold energy detector over Rayleigh-faded sensing and imperfect reporting channels

Cognitive radio (CR) has been viewed as a promising solution to spectrum scarcity. In order to design a reliable CR system, many improvements have been proposed to enhance spectrum sensing performance of secondary users (SUs) in a CR network (CRN). Sensing reliability and transmission throughput of SUs are two important performance criteria, which should be optimized to enhance signal protection of primary user (PU) as well as spectrum utilization rate. In this paper, we consider Rayleigh-faded sensing channels and SUs use improved energy detector (IED) to make their local decisions. The final decision is made in a fusion center (FC) through the cooperative spectrum sensing (CSS) scheme with erroneous reporting channels. We show that the improved double-threshold energy detector (IDED) outperforms the conventional energy detector (CED) in terms of the total error rate. Furthermore, we evaluate the transmission throughput of the CRN through various ED schemes with detection constraints over both perfect and imperfect reporting channels. We show that the IDED has the highest achievable throughput among different ED schemes over imperfect reporting channels.     

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.     

一种面向中继协作频谱感知系统的自适应全局最优化算法

该文研究了冗余中继, 次用户及中继用户数目, 检测门限, 信道传输错误率等因素对中继协作频谱感知系统性能的影响, 并提出一种新的自适应全局最优化算法.该算法基于获得最大无干扰功率的自适应中继选择方法, 确定备选认知中继集合;单个次用户以信道传输错误率最小为准则, 从备选认知中继集合中自适应选择最佳中继, 使总体检测率最大;在给定目标检测率的条件下, 以系统吞吐量最大为准则, 给出了自适应全局最优化算法.仿真实验结果表明新算法信道传输精度高, 信道吞吐量大, 节约带宽资源.     

Design of a highly sensitive photonic crystal waveguide platform for refractive index based biosensing

In this paper, a photonic crystal waveguide platform on silicon-on-insulator substrate is proposed in order to realize a highly sensitive refractive index based biosensor. Following the design, the analysis of the sensor structure are made by using the three dimensional Finite Difference Time Domain method. The principle of sensing is based on the change in refractive index, which in turn changes the output spectrum of the waveguide. Results show that the sensitivity of the sensor depends mainly on the geometrical properties of the defect region of the photonic crystal structure. The phenomenon is verified for various samples having refractive index ranging from 1 (air) to 1.57 (Bovine serum albumin). Further, the structure is compared with few other conventional photonic crystal waveguide designs to analyze the sensing performance. The estimated value of sensitivity of the sensor is found to be 260 nm/RIU with a detection limit of 0.001 RIU. This high sensitivity can enhance the performance of low-concentration analytes detection.     

Kurtosis based spectrum sensing in cognitive radio

In this paper, we consider spectrum sensing in cognitive radio networks based on higher order statistics. The kurtosis, a fourth order statistic, which is a measure of deviation from Gaussianity, is used as a detection statistic. An optimum threshold is set up based on the Neyman–Pearson criterion and an analytical expression for upper bound on probability of miss is derived for a single pair of primary and secondary users. Further, we also propose a collaborative spectrum sensing scheme for more than one secondary user and it is shown by simulations that the proposed kurtosis based method outperforms the energy based spectrum hole detection method significantly.     

Outlier-aware cooperative spectrum sensing in cognitive radio networks

This paper considers the problem of cooperative spectrum sensing in cognitive radio networks (CRN). Communication in CRNs may be disrupted due to the presence of malicious secondary users (SU) or channel impairments such as shadowing. This paper proposes a spatio-frequency framework that can detect and track malicious users and anomalous measurements in CRNs. The joint problem of spectrum sensing and malicious user identification is posed as an optimization problem that aims to exploit the sparsity inherent to both, spectrum occupancy and malicious user occurrence. Proposed scheme obtains improved performance by utilizing node location information, and can handle missing or inaccurate location information, and noisy SU reports. A distributed block-coordinate descent-based algorithm is proposed that is shown to outperform the state-of-the-art PCA-based approach, and is flexible enough to defeat a variety of attacks encountered in SU networks. An online algorithm, that can handle incorporate multiple SU readings sequentially and adapt to time-varying channels, primary user, and malicious user activity, is also proposed and shown to be consistent. Simulation results demonstrate the efficacy of the proposed algorithms.     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques

The spectrum mobility during data transmission is an integral part of the cognitive radio network (CRN) which is conventionally two types for instance reactive and proactive. In the reactive approach, the cognitive user (CU) switches its communication after the emergence of the primary user (PU), where the detection of emergence of PU relies either on spectrum sensing and/or monitoring. Due to certain limitations of the reactive approach such as: (1) loss at least one packet on the emergence of PU and (2) resource (bandwidth) wastage if the periodic sensing is used for mobility, the researchers have introduced the concept of proactive spectrum mobility. In this approach, the emergence of PU is predicted on the bases of pre-available spectrum information, and switching is performed before true emergence of the PU, in order to avoid even the single packet loss. However, the imperfect spectrum prediction is a major milestone for the proactive spectrum mobility. Recently, due to introduction of the spectrum monitoring simultaneous to the data transmission, the reactive approach has come into lime-light again, however, it suffers from the ‘single packet loss’ and ‘imperfect spectrum monitoring’ issues. Therefore in this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of cognitive radio network (CRN). In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of PU during the data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed approaches as well as for the prediction and monitoring approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction and monitoring approach. In a special case, when the prediction error is zero, the graphs of all metric values overlies the spectrum monitoring approach, which further validates the proposed approach.