Cyclic Spectrum Research Articles

Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing

For cognitive radio networks, efficient and robust spectrum sensing is a crucial enabling step for dynamic spectrum access. Cognitive radios need to not only rapidly identify spectrum opportunities over very wide bandwidth, but also make reliable decisions in noise-uncertain environments. Cyclic spectrum sensing techniques work well under noise uncertainty, but require high-rate sampling which is very costly in the wideband regime. This paper develops robust and compressive wideband spectrum sensing techniques by exploiting the unique sparsity property of the two-dimensional cyclic spectra of communications signals. To do so, a new compressed sensing framework is proposed for extracting useful second-order statistics of wideband random signals from digital samples taken at sub-Nyquist rates. The time-varying cross-correlation functions of these compressive samples are formulated to reveal the cyclic spectrum, which is then used to simultaneously detect multiple signal sources over the entire wide band. Because the proposed wideband cyclic spectrum estimator utilizes all the cross-correlation terms of compressive samples to extract second-order statistics, it is also able to recover the power spectra of stationary signals as a special case, permitting lossless rate compression even for non-sparse signals. Simulation results demonstrate the robustness of the proposed spectrum sensing algorithms against both sampling rate reduction and noise uncertainty in wireless networks.

Construction of dsDNA biosensor based on dendritic SiO2 nanoparticle and investigation on its interaction with benzo[a]pyrene

In this work, a new dsDNA biosensor was constructed to monitor the interaction of DNA and benzo[a]pyrene (BaP). Firstly, dendritic SiO2 nanoparticles were synthesized by silanization of SiO2 nanoparticles with (γ-(methacryloyloxy)propyl) trimethoxysilane and polymerization with acrylic acid. Then, due to the rich carboxyl groups of these nanoparticles, they were associated with the amino groups of a self-assembly membrane formed on the gold electrode by a sulfur-containing compound, 5-amino-3-mercapto-1,2,4-triazole. Finally, dsDNA was immobilized on the electrode surface by static adsorption with the aid of metallic ion. The whole immobilization steps were characterized by cyclic voltammogram and electrochemical impedance spectrum. After that, using methylene blue as a probe, the interaction of BaP with dsDNA was investigated. A linear relationship between the percentages of current decrease with the logarithm of BaP concentrations was found in the range from 0.33 to 133 μM.

Blind Estimation of Carrier Frequency and Symbol Rate Based on Cyclic Spectrum Density

A blind estimation method based on cyclic spectrum density is proposed to estimate carrier frequency and symbol rate with respect to non-cooperative communication. The cyclostationarity of modulated signals is used to estimate carrier frequency and symbol rate within frequency domain and cyclic frequency domain separately. Both theoretical analysis and simulation results show that the presented method is noise-insensitive and robust.

The Cyclostationary Characteristic Analysis of the Time-Frequency Overlapped Signal in Single-Channel

The characteristic analysis of the time-frequency overlapped signal (TFOS) in single-channel is a powerful challenge in electronic countermeasures. So, in this paper, the time-frequency overlapped degrees (Pt, Pf) of signals are proposed to measure the level overlapped of in time domain and frequency domain. Afterwards, based on cyclostationary principle, the proposition about the cyclostationary characteristic of TFOS is proved. Moreover, the cyclic spectrum of TFOS is deduced. Theoretical analysis and experimental results show that the linear sum of multicyclostationary signals is still cyclostationary signal.

Sensitivity Analysis of Cyclostationarity-Based and Radiometric Detectors for Single-Sensor Receivers

The purpose of the work presented here is to determine which signal model, stationary or cyclostationary, is more suitable for signal detection in different representative cases. We consider different detectors depending on the available signal knowledge. As cyclostationarity-based detectors, the multi-cycle, single-cycle, and a new detector for unknown signals, denoted maximum spectral correlation amplitude (MSCA) detector, are analyzed. The MSCA detector performs an efficient search on the whole cyclic spectrum based on the fast Fourier transform (FFT) accumulation method (FAM) for cyclic-correlation estimation. Analytical approximations of the false alarm and detection probabilities of the MSCA detector are derived. Additionally, the optimum radiometer and a channelized radiometer are included in the work as radiometric detectors. All these detectors are designed for stationary white Gaussian noise (WGN), their sensitivity is evaluated also for different nonideal environments, which comprise nonstationary or non-Gaussian noise. Our results show that, in general, cyclostationarity-based detectors only find application when the noise power is unknown and it is not possible to use a CFAR (constant false alarm rate) strategy for the radiometer.

Cyclic spectral analysis of radio pulsars

Cyclic spectral analysis is a signal-processing technique designed to deal with stochastic signals whose statistics vary periodically with time. Pulsar radio emission is a textbook example of this signal class, known as cyclostationary signals. In this paper, we discuss the application of cyclic spectral analysis methods to pulsar data and compare the results with the traditional filterbank approaches used for almost all pulsar observations to date. In contrast to standard methods, the cyclic spectrum preserves phase information of the radio signal. This feature allows us to determine the impulse response of the interstellar medium and the intrinsic, unscattered pulse profile directly from a single observation. We illustrate these new analysis techniques using real data from an observation of the millisecond pulsar B1937+21.

Improved Fast Cyclic Spectral Estimation Algorithm and Performance Analysis

An improved fast algorithm is proposed for cyclic spectral estimation,which decreases the requirement of data quantity,without reducing the performance.The windowed overlapped data processing,which is used in time smooth method,is introduced into the frequency smooth algorithm to cut down the original cyclic spectral estimation variance and improve the estimation quality.The paper deduces the estimator's asymptotically expressions of mean,variance,resolution and computational complexity.The theoretical analysis and simulation results prove that the improved algorithm displays better performance than DFSM under the same conditions.The new method is an efficient estimator of cyclic spectrum in the low SNR,high resolution demanding and small data quantity environment.

Fatigue crack growth rate in acrylic resins under variable amplitude loading

Dental materials made of resin composite offer many advantages over those made of amalgam. They are easy to use, durable and esthetically pleasing. These materials in the oral cavity are subject to different environments and fatigue loadings. The objective of this manuscript was to evaluate the fatigue behavior of acrylic resins under variable amplitude loading conditions. Two types of acrylic resin were used for this analysis (heat-polymerizing resins – Meliodent and Royaldent). These materials are usually used as acrylic bases for total dentures and have been prepared in accordance to the manufacturer’s recommendations. For fatigue fracture analysis, were performed crack propagation tests on CT-type specimens, according to ASTM E647 standard. The tests were carried out on a servo-hydraulic testing machine, for cyclic loadings with variable amplitude and different stress ratio, R. The compliance method was used for fatigue crack length measurements. The characteristics of fatigue crack propagation were expressed as the correlation between the fatigue crack growth rate (d a/d N) and crack length ( a) and the stress ratio ( R). Based on experimental results, was analyzed the notch and loading effect on fatigue crack growth rate in acrylic resins. Conclusions One of the conclusions of this study is that the deformed area near the notch has a significant effect on fatigue crack propagation. As we increase the stress ratio of the cyclic spectrum, the loading effect is still smaller and fatigue fracture is controlled by the crack length. Acrylic resins have a brittle fracture behavior and crack length plays an important role in fatigue crack growth.

Cyclic Spectrum Analysis on Rolling-Element Bearing with Inner-Race Point Defect

Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.

Application of Cyclic Spectrum Feature Detection to Cognitive UWB Wireless Communication

摘要: 认知超宽带无线通信技术结合了超宽带无线通信技术和认知无线电技术，信号频谱检测是认知超宽带无线通信系统的核心模块之一。循环平稳特性普遍存在于各种调制信号中，该文研究了一种新的检测算法循环功率谱特征检测算法，并以OFDM信号为例给出了软件仿真和性能分析。该检测算法能够区分有用信号，噪声信号和干扰信号，是最适合认知超宽带无线通信系统的。 关键词: 无线通信 / 认知超宽带 / 循环功率谱特征检测 / 循环功率谱估计

Cyclic Spectrum Analysis of OFDM Signals

Cyclic Spectrum Analysis of OFDM Signals

Spectrum Sensor Hardware Implementation Based on Cyclostationary Feature Detector

Cognitive radios utilize spectrum sensors to provide information about the surrounding radio environment. This enables cognitive radios to communicate at the same frequency bands with existing (primary) radio systems, and thereby improve the utilization of spectral resources. Furthermore, the spectrum sensor must be able to guarantee that the cognitive radio devices do not interfere with the primary system transmissions. This paper describes a hardware implementation of a spectrum sensor based on cyclostationary feature detector, which has an improved detection performance achieved by decimation of the cyclic spectrum. Decimation also provides a simple way to control detection time and, therefore, allows trading the detection time to better probability of detection and vice versa. Implementation complexity in terms of power consumption and silicon area for a 65 nm CMOS process is evaluated. Measured detection performance is presented and detection of a 802.11g WLAN signal through air interface is demonstrated. Cognitive radios utilize spectrum sensors to provide information about the surrounding radio environment. This enables cognitive radios to communicate at the same frequency bands with existing (primary) radio systems, and thereby improve the utilization of spectral resources. Furthermore, the spectrum sensor must be able to guarantee that the cognitive radio devices do not interfere with the primary system transmissions. This paper describes a hardware implementation of a spectrum sensor based on cyclostationary feature detector, which has an improved detection performance achieved by decimation of the cyclic spectrum. Decimation also provides a simple way to control detection time and, therefore, allows trading the detection time to better probability of detection and vice versa. Implementation complexity in terms of power consumption and silicon area for a 65 nm CMOS process is evaluated. Measured detection performance is presented and detection of a 802.11g WLAN signal through air interface is demonstrated.

Multi-segmental Smoothing Cyclic Spectrum Estimation based on Welch-Bartlett Method

A multi-segmental smoothing cyclic spectrum estimation(CSE) based on Welch-Bartlett method is proposed in this paper for basic CSE is the high computation and not convenient in practice. The proposed algorithm, using multisegmental smoothing and Fast Fourier Transform(FFT), estimate signal frequency spectrum accurately as possible, and then calculates any section cyclic spectrum by frequency spectrum shift and correction calculating. The method can not obtain all alpha-plane cyclic spectrum, so reduce calculating times. The method has easier flexibility and apply further cross-spectrum and multi-spectrum etc.

Fatigue Crack Growth Prediction of Thick Wall Cylinder under Variable Amplitude Loading

The life prediction information is useful for improving the component design methodology at the early developing stage. Many thick cylinders are subjected to complex cyclic loading spectrum ranging from small vibration to large load induction. This paper presents modeling of fatigue crack growth behavior in thick wall cylinder for outer surface cracked pipe subjected to internal pressure. There are many factors affecting fatigue crack growth such as; crack length, orientation of crack, thickens of the cylinder and the load ratio. Fatigue crack growth as consequence of service loads depends on many different contributing factors. With the help of a simulation fatigue crack growth in three-dimensional structures can either be predicted or explain for already existing failures. The simulation results showed that, more studies on the thick wall cylinder structure need to be performed in order to obtain more accurate fatigue life.

Radar emitter recognition based on cyclostationary signatures and sequential iterative least-square estimation

Radar emitter recognition plays an important role in electronic warfare (EW). Specific radar emitter recognition is the state-of-art technology of emitter recognition, which can recognize the different radar devices of the same type. It is a composite task that involves radar signal interception, modulation recognition, features extraction and classification. In this paper, first we study the unintentional modulation on pulse (UMOP) features of radar emitter. Then the iterative least-square method is introduced for estimation of the UMOP features. Because of the discriminatory capability and abundant information of cyclostationary signatures, the zero frequency slice of cyclic spectrum is used for specific radar emitter recognition. Based on these, the sequential iterative least-square (SILS) algorithm is proposed for the online recognition of radar emitters. Finally experiments on three simulation radars and eight actual intercepted radars with the same type verify the correctness and validity of the proposed method.

Affordable Cyclostationarity-Based Spectrum Sensing for Cognitive Radio With Smart Antennas

Cognitive radio (CR) technology is a promising way to improve the bandwidth efficiency of underutilized radio spectra. For practical CR systems with limited <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the primary users' signal characteristics, spectrum sensing is mainly based on energy detection and cyclostationary feature detection. Energy detection is simple and practical but becomes ineffective at a low signal-to-interference-and-noise ratio (SINR). Conventional cyclostationary feature detection based on cyclic spectrum estimation can robustly detect weak signals from primary users by only exploiting the cyclostationarity property of communication signals. However, the high implementation complexity it requires limits its widespread usage. In the literature, the use of smart-antenna technology is suggested to track the locations of the primary users and apply transmit beamforming to avoid spatial interference with their signals. The objective of this paper is to establish adaptive cyclostationary (receive) beamforming as an effective spectrum-sensing method with affordable complexity for multiple-antenna cognitive radio. Specifically, we introduce a new spectrum-sensing method that exploits a recently proposed beamforming algorithm, called the adaptive cross-self-coherent-restoral (ACS) algorithm. The complexity of the resultant algorithm is higher than that of the energy detector but is at least an order of magnitude smaller than that of the previous cyclostationary feature detectors, such as the Fourier spectrum cyclic density analysis method and its multitaper-Loe¿ve version. Their performances for spectrum sensing are empirically evaluated and compared in detail in an example.

True-Power Measurement in Digital Communication Systems Affected by In-Channel Interference

A new cyclostationarity-based method for power measurement in digital communication systems in the presence of in-channel interference is proposed. The new method takes advantage of the signal-selective properties typical of algorithms based on cyclic spectral analysis. The true-power measure is achieved from the noisy data by estimating the cyclic spectrum of the desired signal at a cycle frequency not shared with noise and interference. The performance of the method is evaluated through several experiments on spread-spectrum and pulse-amplitude-modulated signals and with reference to different types of interfering signals. The method exhibits very good performance even when the desired and interfering signals completely overlap in both temporal and spectral domains.

Cyclic Spectral Analysis of UWB-IR Signals

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A unified, matrix-based, stochastic method for cyclic spectral analysis of ultra-wideband impulse radio signals, based on their Markov chain representation, is proposed. Explicit expressions are obtained for the cyclic spectrum of pulse position modulation and pulse amplitude modulation with time-hopping and direct sequence coding, respectively. Some theoretical and graphed results for the spectral correlation characterization of these signals are presented, as well. </para>

Theorical and experimental research on the HELS method in the cyclostationary acoustic field

Helmholtz equation least square method (HELS) is based on the assumption that the acoustic field can be approximated by the superposition of a series of spherical waves. According to the principle of least square error between the reconstruction and measurement, the expansion number of the spherical wave equation and the related coefficients can be determined by the least-square method. The HELS method is applied in the practice due to its efficient computing and less measurements comparing with other acoustical holography algorithms. HELS and other near-field acoustic holography methods are all based on the stationary acoustic field. As a special example of the non-stationary acoustic field, the cyclostationary acoustic field is usually analyzed by dealing with signals in a single channel and its nth order statistics are often utilized to detect mechanical faults. In the present paper, HELS method is modified to analyze the cyclostationary acoustic field with the variance of cyclic spectrum density. The reconstruction in the appropriate cyclic frequency will supply visible acoustic field information, especially that hidden under the cyclostationary condition. A numerical simulation is done to verify the validity of the modified HELS, in which the effects of the ways of measurement are discussed. At last, a spherical sound box driven by cyclostationary signal is set in a semi-anechoic chamber and the acoustic field is reconstructed by modified HELS method.

The Application of Wave Superposition Algorithm to Identify Cyclostationary Sound Source

The cyclostationary characteristics can not be extracted clearly in reconstruction of the acoustic field radiated from the cyclostationary sources by the conventional near-field acoustic holography procedures. The cyclostationary near-field acoustic holography method uses the cyclic spectrum density function as the reconstruction variance instead of the spectrum or the power spectrum density, which decomposes the carrier wave and modulating wave in the appropriate cyclic frequency. The reconstructed CSD shows the distribution of the modulating wave of cyclostationary sources and supplies more cyclostationary acoustical information than the conventional NAH. In the present work, the theory of the cyclostationary NAH combined with the wave superposition is introduced, which can be used for the arbitrarily shaped cyclostationary sources. A numerical simulation and a speaker experiment are employed to validate this WSA-based cyclostationay NAH method. The results of the simulation show great agreement between the reconstruction and the analytical value. The distribution of the CSD of the cyclostationary acoustical source on the surface coincides with the speaker box accurately.