Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Spectrum sensing is an important function in radio frequency spectrum management and cognitive radio networks. Spectrum sensing is used by one wireless system (e.g., a secondary user) to detect the presence of a wireless service with higher priority (e.g., a primary user) with which it has to coexist in the radio frequency spectrum. If the wireless signal is detected, the second user system releases the given frequency to maintain the principle of not interfering. This paper proposes a machine learning implementation of spectrum sensing using the entropy measure as a feature vector. In the training phase, the information about the activity of the wireless service with higher priority is gathered, and the model is formed. In the classification phase, the wireless system compares the current sensing report to the created model to calculate the posterior probability and classify the sensing report into either the presence or absence of wireless service with higher priority. This paper proposes the novel application of the Fluctuation Dispersion Entropy (FDE) measure recently introduced in the research community as a feature vector to build the model and implement the classification. An improved implementation of the FDE (IFDE) is used to enhance the robustness to noise. IFDE is further enhanced with an adaptive method (AIFDE) to automatically select the hyper-parameter introduced in IFDE. Then, this paper combines the machine learning approach with the entropy measure approach, which are both recent developments in spectrum sensing research. The approach is compared to similar approaches in literature and the classical energy detection method using a generated radar signal data set with different conditions of SNR(dB) and fading conditions. The results show that the proposed approach is able to outperform the approaches from literature based on other entropy measures or the Energy Detector (ED) in a consistent way across different levels of SNR and fading conditions.     

TCVQ-SVM algorithm for narrowband spectrum sensing

Spectrum sensing is viewed as the basic and crucial technology for cognitive radio. To improve the accuracy of spectrum sensing in low signal to noise ratio (SNR), this paper presents an efficient TCVQ-SVM method based on machine learning for narrowband spectrum sensing. Firstly, trace of covariance matrix and variance of quadratic covariance matrix (TCVQ) is extracted as feature vectors and combined as training samples of spectrum sensing. Then, the classification model can be achieved by training samples based on support vector machine (SVM), which can avoid setting threshold and adjusting classification hyperplane by its self-learning ability. Lastly, the result of spectrum sensing can be obtained. By utilizing trace and variance as input features of SVM, the algorithm can make full use of the eigenvalue difference and structure characteristic of the received signal, and at the same time, achieve good performance in low SNR. Theoretical analysis reveals that the proposed method has low computational complexity. Simulation results and experiments on the hardware platform illustrate that the proposed algorithm is effective and robust.     

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.     

A comparative study of spectrum awareness techniques for cognitive radio oriented wireless networks

Spectrum scarcity is impeding practical implementations of emerging wireless multimedia applications requiring significantly more frequency spectrum. Cognitive radio (CR) has emerged as a promising solution to the current spectral congestion problem by imparting intelligence to the conventional software defined radio that allows spectrum sharing through opportunistic spectrum access. The principal objective of CR is to optimize the use of under-utilized spectrum through robust and efficient spectrum sensing (SS). This paper introduces cognitive functionality and provides an in-depth comparative survey of various spectrum awareness techniques in terms of their sensing accuracy and computational complexities along with their merits and demerits. Specifically, key challenges in SS are highlighted and possible solutions are discussed. A classification of SS is presented to address the sensing method selection criterion. Both non-cooperative and cooperative sensing schemes are reviewed and open research problems are highlighted to identify future research directions.     

The concise fractional Fourier transform and its application in detection and parameter estimation of the linear frequency-modulated signal

A concise fractional Fourier transform(CFRFT) is proposed to detect the linear frequency-modulated(LFM) signal with low signal to noise ratio(SNR).The frequency axis in time-frequency plane of the CFRFT is rotated to get the spectrum of the signal in different angles using chirp multiplication and Fourier transform(FT).For LFM signal which distributes as a straight line in time-frequency plane,the CFRFT can gather the energy in the corresponding angle as a peak and improve the detection SNR,thus the LFM signal of low SNR can be detected.Meanwhile,the location of the peak value relates to the parameters of the LFM signal.Numerical simulations and experimental results show that,the proposed method can be used to efficiently detect the LFM signal masked by noise and to estimate the signal's parameters accurately.Compared with the conventional fractional Fourier transform(FRFT),the CFRFT reduces the transform complexity and improves the real-time detection performance of LFM signal.     

基于统计特征和显著度的光谱信号提取算法

各类光谱信号都会受到噪声和基线畸变的影响,在提取光谱信号过程中若不考虑基线畸变和噪声的影响,将会严重影响信号提取的精度和准确性,所以需要在信号提取前消除噪声和基线畸变的影响。大多数信号提取算法的步骤是先提取整体基线,再提取信号,这样难以保证基线的提取精度。为了降低信号提取过程中背景噪声、基线畸变等不利因素的影响,根据信号的存在总是会导致该区域的统计特征不同于背景的特点,提出了一种基于显著度和统计特征的光谱信号检测与提取算法（SSD算法）。首先,在待测数据的不同尺度空间中计算出信号在各尺度下的显著度,将检测出的显著信号点作为候选信号点;其次,利用信号特征去除候选信号点中的伪信号点;最后,对候选信号点所在区域采用二次多项式进行基线拟合以剔除伪信号区域并实现最终的信号提取。为验证SSD算法的综合性能,首先,通过仿真的方法对高斯信号和矩形信号在不同基线类型、不同信噪比下进行实验;然后将该算法与AirPLS算法、Wavelet算法以及DoG算法对两种信号在不同信噪比,不同基线类型下的提取结果进行比较。仿真实验结果表明：与其他算法相比,SSD算法信号提取结果基本不受信号类型和基线畸变类型的影响,且当信噪比大于40时基本不受信噪比的影响;在不同基线畸变类型下,SSD算法对两种信号提取结果的准确度、稳定性、离散度均较好,其他算法则只适用于某种基线畸变类型。从总体提取结果上看,SSD算法提取结果的绝对误差的均值仅为AirPLS算法的8.71%、Wavelet算法的3.52%、DoG算法的2.01%;绝对误差的均方根也仅为AirPLS算法的13.08%、Wavelet算法的5.45%、DoG算法的3.11%。因此,所提出的SSD算法在提取信号时具有良好的综合性能,能够在不同的信噪比及基线畸变情况下准确的提取出信号。     

一种基于新型间歇混沌振子的舰船线谱检测方法

为了实现低信噪比下未知频率的舰船辐射线谱的检测,对常规型间歇混沌振子列检测方法进行了改进,提出了一种基于适应步长型间歇混沌振子的信号检测方法.该方法可以只用一个Duffing振子,通过设定一组能够覆盖待测信号所在频段的求解步长序列,实现对未知频率、具有任意初相位的微弱周期信号的搜索检测.为进一步提高系统的弱信号检测性能,分析了Holmes型Duffing方程在不同频率内置策动力下对弱信号灵敏度的差异.综合理论分析和仿真研究结果给出了Duffing振子在内置策动力角频率为0.4 rad/s时对弱信号检测性能最佳,并据此对所采用的Duffing振子进行了优化;仿真结果表明,改进后的Duffing振子的弱信号检测性能提高了12 dB.最后将此方法应用于一组含有舰船辐射线谱的实船数据,结果表明此方法可以实现低信噪比下的未知频率微弱线谱检测.     

进行子阵加权波束形成的波达方向估计

针对水声信道的多径效应以及海底散射信号信噪比低导致方位估计性能较差的问题,提出了一种基于子阵加权波束形成的UESPRIT算法(Weighted Beamspace UESPRIT Based on Subarrays,BS-BUESPRIT)。首先利用密集波束域转换矩阵估计回波信号的方位谱,进而估计同一时刻到达阵列的回波数目;之后将均匀线阵分为多个尺寸相同、相互重叠的子阵,利用加权波束形成对各子阵接收信号做指定方向的空域滤波;最后基于各子阵波束形成后的输出结果,利用UESPRIT算法实现回波方向的估计。仿真和湖试、海试试验结果表明,与UESPRIT算法相比,BS-BUESPRIT算法提高了信号波达方向估计性能,在多径和较低信噪比条件下有着更高的估计精度,应用于高分辨率测深侧扫声呐时有效地提高了声呐的测深性能。      

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.     

MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM

Spectral pre-coding is a capable method to restrain Out-Of-Band Emission (OOBE) and act in accordance with leaking parameters over neighboring frequency channels while masking unnecessary emissions. Nevertheless, spectral pre-coding might deform the real data vector that is articulated as the Error Vector Magnitude (EVM), which shows a harmful effect on the performance of Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM)-oriented schemes. In this research, a new Mapper Reducer for spectral pre-coded signal (MaReSPS) for energy-constrained signal receiver is proposed for energy efficient spectral precoding in the MIMO-OFDM system. This model involves Mapper Reducer (MR) framework for detecting the received signal, which renders an error rate, and graceful degradation is observed in the throughput under channel uncertainty. The proposed scheme alleviates the resultant Transmit EVM (TxEVM) observed at the receiver by capitalizing on the massive MIMO system, and as a result the throughput is improved. The comparison is done with respect to Block Error Rate (BLER), throughput, and Power Spectral Density (PSD) for proving the betterment of the proposed precoding model for MIMO-OFDM. In particular, the normalized throughput for conventional No OOBE Reduction (OOBER), Mask Compliant Spectral Pre-coder (MSP), Notching Spectral Pre-coder + Zero Forcing (NSP + ZF), P1/P2: CVX and P1/P2: Top- Alternating Direction Method of Multipliers (ADMM) models, as well as proposed MaReSPS model, is lower at a Signal to Noise Ratio (SNR) from 0 dB to 15 dB. With an increase in SNR, the normalized throughput increases and when SNR =40 dB, the normalized throughput values reach their peak values. However, compared to existing models, the proposed MaReSPS model showed high normalized throughput.     

Method of improved scatterer size estimation and application to parametric imaging using ultrasound

The frequency dependence of RF signals backscattered from random media (tissues) has been used to describe the microstructure of the media. The frequency dependence of the backscattered RF signal is seen in the power spectrum. Estimates of scatterer properties (average scatterer size) from an interrogated medium are made by minimizing the average squared deviation (MASD) between the measured power spectrum and a theoretical power spectrum over an analysis bandwidth. Estimates of the scatterer properties become increasingly inaccurate as the average signal to noise ratio (SNR) over the analysis bandwidth becomes smaller. Some frequency components in the analysis bandwidth of the measured power spectrum will have smaller SNR than other frequency components. The accuracy of estimates can be improved by weighting the frequency components that have the smallest SNR less than the frequencies with the largest SNR in the MASD. A weighting function is devised that minimizes the noise effects on the estimates of the average scatterer sizes. Simulations and phantom experiments are conducted that show the weighting function gives improved estimates in an attenuating medium. The weighting function is applied to parametric images using scatterer size estimates of a rat that had developed a spontaneous mammary tumor.     

高速运动目标的自适应线谱增强算法研究

本文研究了低信噪比、目标高速运动情况下,多普勒频移较大时针对线谱信号的自适应增强算法。对海试数据的分析表明,常规自适应线谱增强算法对多普勒频移下的线谱增强效果明显,但要求信噪比较高,且实时性较差;基于高阶累积量对角切片的自适应线谱增强算法对非线性高斯色噪声下的线谱增强效果显著,但对低信噪比环境下变化线谱的跟踪性能较差;频域批处理自适应线谱增强算法对低信噪比下的线谱信号具有很好的增强效果,降低了运算量。     

低信噪比下PSK信号的载频估计（英）

针对电子侦察中PSK信号的载频估计问题,提出一种低信噪比下载频估计新方法。该方法将PSK信号划分为等长度的交叠区间,提取各个区间内信号频谱的聚集性测度作为特征参数,然后将此特征参数导入网格密度聚类算法,以聚类结果作为载频估计特征类,最后将特征类对应的频谱叠加后提取峰值得出信号载频估计值。该方法避免了传统PSK信号载频估计的非线性运算,显著降低了PSK信号载频估计的信噪比门限,且无需先验知识,适合于电子侦察场合。仿真实验结果证实了该方法在低信噪比下PSK信号载频估计的有效性。     

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.     

低信噪比线性调频信号目标的方位估计

线性调频(LFM)信号目标的方位估计是水声探测研究的重要内容,在进行方位估计时,若存在强干扰信号源与强背景噪声,阵元接收信号的信噪比会显著降低,严重影响LFM信号目标方位估计结果的准确性.针对该问题,提出了一种简明分数阶滤波方法,并将其与常规波束形成方法(CBF)相结合来实现低信噪比条件下LFM信号目标的方位估计.简明分数阶傅里叶变换能在正交角度上将LFM信号的能量聚集在特定频点处并形成明显的能量峰,利用该特性,可对阵列各阵元接收的低信噪比LFM信号在简明分数阶域聚集的能量峰进行最佳滤波,以滤除干扰信息及背景噪声.对滤波输出进行逆简明分数阶傅里叶变换可得到增强信干比和信噪比的阵元域信号,进一步用于目标方位估计,就能获得更加准确的目标方位。数值仿真结果和海试实验数据处理结果验证表明,本文所提出的方法可有效抑制干扰和背景噪声,并对低信噪比LFM信号进行准确、稳健的方位估计。      

简明分数阶傅里叶变换及其对线性调频信号的检测和参数估计

针对低信噪比下线性调频信号的检测问题,提出了一种简明分数阶傅里叶变换方法。该变换借助chirp相乘和傅里叶变换对时频平面上的频率轴进行旋转,以获取信号在各个角度下频率轴上的频谱分布。对时频分布呈直线状的线性调频信号,简明分数阶傅里叶变换能在特定角度上将信号能量聚集成尖锐的强能量峰,从而提高信噪比,实现对线性调频信号的可靠检测和参数估计。数值仿真和实验验证结果表明,简明分数阶傅里叶变换可对较低信噪比的线性调频信号实现有效检测,并由变换域峰值的位置对信号参数进行准确估计。相比于传统的分数阶傅里叶变换方法,简明分数阶傅里叶变换的复杂度更低,离散计算效率更高,在对噪声掩盖下的线性调频信号进行检测和参数估计时能更好地满足实时处理的要求。      

Laser Doppler velocimeter using a single longitudinal mode solid-state laser source

An experimental demonstration of using a single longitudinal mode solid-state laser source in laser Doppler velocimeter (LDV) is presented. The technology of frequency spectrum correction is used in processing Doppler signal. The results of the experiments show that: the magnitude and signal-to-noise ratio (SNR) of Doppler signal are both enhanced by the solid-state laser; the measurement accuracy of LDV is improved by the technology of frequency spectrum correction, and the variance of the measured Doppler frequency is larger than the Cramer-Rao low bound (CRLB) of Doppler frequency about one order of magnitude.     

基于FPGA的激光测速仪信号处理方案设计与实现

为满足高动态环境下的激光多普勒测速仪信号处理需要,提出了一种基于现场可编程门阵列(FPGA)的激光测速仪信号处理方案。在FPGA内部完成全部信号处理的内容,利用快速傅里叶变换(FFT)算法得到信号的频谱,利用能量重心法对离散频谱进行校正,开发采样频率自适应算法,兼顾测量准确度与测量范围的要求,最后将结果通过通用串行总线上传个人计算机显示。程序采用流水线方式设计,提高信号处理速度。经过实验验证,数据更新率达到2.4~24kHz,数据延迟时间为123~1230μs,测量准确度优于8×10-4,测量稳定度优于2.5×10-7。     

利用先验估计自适应加窗的准静态超声弹性成像位移估计算法

准静态弹性成像技术是基于组织压缩前和压缩后超声回波射频信号进行组织运动重构的弹性成像技术。提出了一种基于先验估计的自适应窗函数算法,在位移估计过程中,使用已估计的临近窗的时延值作为先验信息,自动调整截取压缩后射频信号段的截取窗函数,提高了互相关运算所需的压缩前和压缩后信号段之间的相关性。仿真实验结果表明,该算法不仅大大提高了成像速度,而且提高了信噪比较低时的成像质量,同时该算法具有更宽的应变通带。