Cyclostationary Feature Detection Based Spectrum Sensing Algorithm under Complicated Electromagnetic Environment in Cognitive Radio Networks

This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.

NONPARAMETRIC POLYSPECTRAL ESTIMATORS FOR k-TH-ORDER (ALMOST) CYCLOSTATIONARY COMPLEX PROCESSES

Higher-order almost cyclostationary complex processes are complex random signals with almost periodically time-varying statistics, which is important to the research of non-Gaussian signals in information system. In tins paper, smoothed polyperiodograms are proposed for related to cyclic polyspectral estimation and are shown to be consistent and asymptotically complex normal. Asymptotic covariance expressions are derived along with their computable forms.

A Blind Separation Approach of Low Order Cyclostationary Signals

This paper presents a new blind separation approach of the low order cyclostationary signals based on the cyclic periodicity of the cyclostationary signal.The goal of the method is extracting the hidden periodicity and reducing the randomicity of cyclostationary signal and it is particularly applicable to the separation of low order cyclostationary signals.The method also demonstrates the importance of extraction of cyclostationary signals from low order to high order in turn.The effectiveness of the proposed method is finally demonstrated by computer simulation and experiment.

PERFORMANCE ANALYSIS OF SECOND-ORDER STATISTICS FOR CYCLOSTATIONARY SIGNALS

The second order statistics for cyclostationary signals were introduced, and their performance were discussed. It especially researched the time lag characteristic of the cyclic autocorrelation function and spectral correlation characteristic of spectral correlation density function. It was pointed out that those functions can be available to extract the time vary information of the kind of non stationary signals. Using the relations of time lag cyclic frequency and frequency cyclic frequency independently, vibration signals of a rolling element bearing measured on test bed were analyzed. The results indicate that the second order cyclostationary statistics might provide a powerful tool for the feature extracting and fault diagnosis of rolling element bearing.

NONPARAMETRIC SPECTRAL ESTIMATORS FORSECOND-ORDER (ALMOST) CYCLOSTATIONARYCOMPLEX PROCESSES

Second-order almost cycloststionary complex processes are complex random signals with almost periodically time-varying statistics. Smoothed periodograms are proposed for related to cyclic spectral estimation and are shown to be consistent. Asymptotic covariance expressions are derived along with their computable forms.

Cyclostationary Model Based Multiple-Tone Interference Rejection in DSSS Communication Systems

Multiple-tone interference is a kind of narrow band interference appears in Direct Sequence (DS) Spread Spectrum (SS) communication system frequently that degrades the system performance. Most open literature studies on narrowband interference rejection assumed stationary SS signal and interference. SS signal and interference are modeled as cyclostationary and based on cyclostationary model, an interference rejection algorithm is proposed. Through computer simulation, it is shown that the performance of the SS system can be improved considerably after adopting this algorithm.

CYCLOSTATIONARY MODEL IN DS SPREAD SPECTRUM COMMUNICATION SYSTEM

During the past research, Direct Sequence (DS) Spread Spectrum (SS) signal is modeled as stationary in most cases. Cyclostationary model for SS signal is introduced in this letter. Compared with stationary model, cyclostationary model can preserve more information,and its advantages are shown through example.

BLIND IDENTIFICATION OF A CLASS OF NONLINEAR SYSTEMS WITH CYCLOSTATIONARY INPUT

This letter deals with blind identification of nonlinear discrete Hammerstein system under the input signal that is cyclostationary. The first-order moment of the specific input as well as the inverse nonlinear mapping of the Hammerstein model are combined to establish a relationship between the system output and the system parameters, which implies an approach to identifying the system blindly. Simulation results demonstrate the effectiveness of this approach to blind identification of a class of nonlinear systems.

Throughput Estimation with Noise Uncertainty for Cyclostationary Feature Detector in Cognitive Radio Network

Cognitive Radio Networks (CRNs) are recognized as the enabling technology for improving the future bandwidth utilization. In CRNs secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. The secondary users are required to sense the radio frequency environment. The lower the probability of false alarm, the more chances the channel can be reused and the higher the achievable throughput for the secondary network. The main contribution of this paper is to formulate the sensing-throughput-noise uncertainty tradeoff for cyclostationary feature detection. Computer simulations have shown that for a 1 MHz channel, when the sensing duration is 2% of total time, the spectrum will get 99% probability of detection regardless of 50% noise uncertainty.

A New Method for Rolling Element Bearing Fault Diagnosis Based on Cyclostationary Theory

The theory of cyclostationary and its application are very important for the analysis and processing of a non stationary signal. The paper introduces second order cyclostationary statistics, with emphass on discussion of cyclic periodogram arithmetic. Comparing the time smoothed cyclic periodogram with the frequency smoothed cyclic periodogram, we found that the former is more useful to extract the feature of cyclostationary signals. The method has been applied to analyze the vibration signal of a rolling element bearing measured on a test bench, and proved to be effective. Meanwhile, we have compared it with traditional power spectral density analysis, and the results prove that the time smoothed cyclic periodogram is more available to diagnose the fault of a rolling element bearing.

Combined energy detection and one-order cyclostationary feature detection techniques in cognitive radio systems

One of the main requirements of cognitive radio systems is the ability to detect the presence of the primary user with fast speed and precise accuracy. To achieve that, a possible two-stage spectrum sensing scheme is suggested in this paper. More specifically, a fast spectrum sensing algorithm based on the energy detection is introduced focusing on the coarse detection. A complementary fine spectrum sensing algorithm adopts one-order cyclostationary properties of primary user＇s signals in time domain. Since the one-order feature detection is performed in time domain, the real-time operation and low-computational complexity can be achieved. Also, it drastically reduces hardware burdens and power consumption as opposed to two-order feature detection. The sensing performance of the proposed method is studied and the analytical performance results are given. The results indicate that better performance can be achieved in proposed two-stage sensing detection compared to the conventional energy detector.

Cyclostationary signature design for common control channel of cognitive radio

Embedding specific signatures in transmitted signals for identifying common control channels of cognitive radio are addressed in research laboratories because availability of the spectrum occupied by the common control channel might change in time. A novel solution to embed a unique cyclostationary signature for the common control channel of cognitive radio is proposed in this article. Based on linear periodically time-variant transformation （LPTV） model, the cyclic autocorrelation expression of the proposed signature is derived, which characterizes its cyclostationarity. Analysis of the cyclostationary signature is presented considering effects of additive white Gaussian noise （AWGN） and multiplath channels. Simulation results illustrating the reliability of signatures are given.

Cyclostationary Property Based Spectrum Sensing Algorithms for Primary Detection in Cognitive Radio Systems

To implement the primary signal without interference in cognitive radio systems, cognitive radios can detect the presence of the primary user in low SNR. Currently, energy detector is the most common way of spectrum sensing because of its low computational complexity. However, performunce of the method will be possibly degraded due to the uncertainty noise. This paper illustrates the benefits of one-order and two-order cyclostationary properties of primary user＇s signals in time domain. These feature detection techniques in time domain possess the advantages of simple structure and low computational complexity comparing with spectral feature detection methods. Furthermore, performance of the one-order and two-order feature detection is studied and the analytical results are given. Our analysis and numerical results show that the sensing performance of the one-order feature detection is improved significantly comparing with conventional energy detector since it is robust to noise. Meanwhile, numerical results show that the two-order feature detection technique is better than the one-order feature detection. However, this benefit comes at the cost of hardware burdens and power consumption due to the additional multiplying algorithm.

Cyclostationary Modeling of Surface Electromyography Signal During Gait Cycles and Its Application for Cerebral Palsy Diagnosis

Cerebral palsy(CP) is a group of permanent movement disorders that appear in early childhood.The electromyography(EMG) signal analysis and the gait analysis are two most commonly used methods in the clinic. In this paper, a cyclostationary model of the EMG signal is proposed. The model can combine the aforementioned two methods. The EMG signal acquired during the gait cycles is assumed to be cyclostationary due to the physiological characteristics of the EMG signal production. Then, the spectral correlation density is used to analyze the cyclic frequency(corresponding to the gait cycles) and spectral frequency(the frequency of EMG signal) in a waterfall representation of the two kinds of frequencies. The experiments show that the asymptomatic(normal) subjects and symptomatic subjects(with CP) can be distinguished from the spectral correlation density in a range of cyclic frequencies.

An Improved Cyclostationary Feature Detection Based on the Selection of Optimal Parameter in Cognitive Radios

Spectrum sensing is an important part of cognitive radio systems to find spectrum hole for transmission which enables cognitive radio systems coexist with the authorized radio systems without harmful interference.In this paper,an improved cyclostationary feature detection method is proposed to reduce computational complexity without loss of good performance based on the optimal parameter selection strategy for choosing detection parameters of cyclic frequency and lag.Taking binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals as examples,the theoretical analyses are presented for choosing the optimal parameters.Simulation results are given to certify the correctness of the proposed parameter selection strategy and show the performance of the proposed method.

Separating cyclostationary signals from spectrally overlapping interference

This paper studies an algorithm about separating spectrally overlapping signals using the cyclostationary properties of signals.On the basis of direct sequence spread system(DSSS),frequency shift filter is added into the re-ceiver of the communication system.Although the structure of frequency shift filter is more complicated than the time-domain filter,it uses both time correlations and fre-quency spectrum correlations so it can achieve better per-formances on separating the overlapping signals.After the analysis of cyclostationary characteristic and frequency spec-trum correlation,the structure of the frequency shift filter can be gained.Then,a self-adaptive algorithm is utilized for the purpose of achieving optimum multidimensional tap weights of frequency shift components.The simulation results indi-cate that this method can efficiently separate overlapping signals,and its error rate is lower than the time-domain filter or DSSS system by two orders of magnitude on the condition that high-power interference is added into the system.

Method of combining space-time block coding with adaptive beamforming

A method of space-time block coding （STBC） system based on adaptive beamforming of cyclostationarity signal algorithm is proposed.The method uses cyclostationarity of signals to achieve adaptive beamforming,then constructs a pair of low correlated transmit beams based on beamform estimation of multiple component signals of uplink.Using these two selected transmit beams,signals encoded by STBC are transmitted to achieve diversity gain and beamforming gain at the same time,and increase the signal to noise ratio （SNR） of downlink.With simple computation and fast convergence performance,the proposed scheme is applicable for time division multiple access （TDMA） wireless communication operated in a complex interference environment.Simulation results show that the proposed scheme has better performance than conventional STBC,and can obtain a gain of about 5 dB when the bit error ratio （BER） is 10-4.

Anti-interference performance analysis of frequency-shift filter in scenario of multiple WPANs accessing HAN

In the scenario of multiple wireless personal area networks （WPANs） accessing the home area network （HAN）, the frequency-shift （FRESH）filter based on the cyclostationary theory is applied for the anti-interference at the 2. 4 GHz spectrum. The main architecture of multiple WPANs accessing the HAN is proposed. The medium access control（ MAC） -level coordination solution applied in the access point （AP）for the coexistence of different communication protocols within WPAN is discussed. The diagram of the adaptive FRESH filter is described. The anti-interference models of the FRESH filter in the scenario of multiple WPANs accessing the HAN are proposed. The minimum mean square error （MMSE）convergence property of the FRESH filter with the change in the data point number is analyzed. The simulation results indicate that the FRESH filter can effectively extract the signal of interest （SOI） from the interference with the partly overlapped spectrum. Thus, the excellent anti-interference performance of the FRESH filter is validated. Moreover, by both theoretical analysis and simulation, the MMSE convergent property of the FRESH filter is also proven.

Fault Feature Extraction of Rolling Bearing Based on an Improved Cyclical Spectrum Density Method

The traditional cyclical spectrum density（CSD） method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis（CSDK） is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.

Application of Array Measurement in Blind Identification

This paper deals with blind identification and deconvolution algorithm for an arbitrary, possibly white or colored, stationary or nonstationary signal, which is observed through array sensors. By using multiple sensors with their individual outputs sampled at a rate 1/T, one can obtain cyclostationary signals. They can be considered as a single-input multiple-output model with an identical but unknown input signal. With the array measurement, an algorithm for estimating the system transfer function model and its parameters is presented.