AN ADAPTIVE MEASUREMENT SCHEME BASED ON COMPRESSED SENSING FOR WIDEBAND SPECTRUM DETECTION IN COGNITIVE WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

Statistics Modeling of Shallow Sea Ambient Noise and Its Applications in Low-frequency Line Spectrum Detection

The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and the results show that the generalized Gaussian distribution is a suitable model for the ambient noise modeling.Thereafter,the optimal detector based on maximum likelihood ratio can be deduced,and the asymptotic detector is also derived under weak signal assumption.The detector's performance is verified by using numerical simulation,and the results showthat the optimal and asymptotic detectors outperform the conventional correlation-integration system due to accuracy modeling of ambient noise.

Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network

In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels,this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks.After adding idle cognitive users for detection,the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time.Both theoretical analysis and simulation results show that using idle cognitive users can reduce service delay and improve the throughput of cognitive networks.After considering the time occupied by cognitive users to report detection information,the optimal participation number of idle cognitive users in joint detection is obtained through the optimization algorithm.

Femtosecond laser processing stainless steel foil and its Fourier spectrum detection

In this paper, we use femtosecond laser pulse to scribe 304 stainless steel foil, detect the Fourier transform infrared spectrum of the sample before and after processing, confirm the "cold processing" and "thermal processing" and their mutual conversion, and determine the "cold processing" parameter window. The ablation threshold and incubation coefficient of 304 stainless steel foil are calculated, and the effects of scanning speed and effective pulse number on the ablation threshold are analyzed. The ANSYS software is used to simulate the radial and axial temperature distributions of the surface on 304 stainless steel foil sample and the heat-affected zone with a femtosecond laser fluence of 10 J/cm2 and an effective number of pulses of 1 200 are obtained. In the aspect of spectral detection, the Fourier transform infrared spectra of the sample before and after processing are measured and two processing mechanisms of "cold processing" and "hot processing" are confirmed, which proves that we can achieve the conversion between "cold processing" and "hot processing" by changing the laser fluence and determine the "cold processing" laser fluence range.

Fuzzy Service Aware Adaptive Cooperative Spectrum Sensing Algorithm

Based on the service characteristics and the sensing ability for secondary users, a joint optimization scheme of spectrum detection and allocation is investigated to expand the available sensing region and allocate the Qo S-specified channels. On the aspect of spectrum detection, due to the available detection index with the global detection metrics, cooperation thresholds are adaptively adjusted to select the cooperative model for maximizing the available sensing region. On the aspect of spectrum allocation, for different service category, the idle channels are efficiently allocated that depend on their stability and available bandwidth. Meanwhile, based on the requested rates defined by fuzzy theory, the secondary users can be divided into two categories, i.e.,delay sensitive service and reliability sensitive service. Finally, the Qo S-specified channels from the targeted spectrum subset are allocated to secondary users. Simulation results show that our proposed algorithm can not only expand the available sensing region,but also decrease the outage probability of delay sensitive services. Additionally, it enables stable power consumption in the time-variation channel.