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Abstract-We propose a novel random triggering based modulated wideband compressive sampling (RT-MWCS) method to facilitate efficient realization of sub-Nyquist rate compressive sampling systems for sparse wideband signals. Under the assumption that the signal is repetitively (not necessarily periodically) triggered, RT-MWCS uses random modulation to obtain measurements of the signal at randomly chosen positions. It uses multiple measurement vector method to estimate the non-zero supports of the signal in the frequency domain. Then, the signal spectrum is solved using least square estimation. The distinct ability of estimating sparse multiband signal is facilitated with the use of level triggering and time to digital converter devices previously used in random equivalent sampling (RES) scheme. Compared to the existing compressive sampling (CS) techniques, such as modulated wideband converter (MWC), RT-MWCS is with simple system architecture and can be implemented with one channel at the cost of more sampling time. Experimental results indicate that, for sparse multiband signal with unknown spectral support, RT-MWCS requires a sampling rate much lower than Nyquist rate, while giving great quality of signal reconstruction.Index Terms-Random triggering, compressive sampling, random demodulation, signal reconstruction, sparse multiband signal.

Vehicle-to-vehicle (V2V) communication empowers vehicles to share information by broadcasting basic and critical safety messages. Dedicated short-range communication (DSRC), the medium access control (MAC) layer of which utilizes the IEEE 802.11p protocol, is a promising candidate technology for vehicular communication. Safety applications usually demand safety message dissemination to be prompt and reliable. To satisfy these strict requirements, the MAC layer of vehicular safety communication tends to adopt Distributed Coordination Function (DCF) or single-class Enhanced Distributed Channel Access (EDCA) without request-to-send/clear-to-send (RTS/CTS), acknowledgment (ACK) and retransmission mechanisms as the access scheme. As far as we know, although many numerical models have been provided to understand the IEEE 802.11 DCF performance, there is no precise model that examines the performance of vehicular safety communication exploiting such an access scheme in imperfect channels with different incoming traffic loads. In this paper, we settle this problem by developing an analytical model where the impacts of various incoming traffic loads, packet length distribution, hidden terminal effects, node mobility, the MAC layer queuing system, and the faulty radio channels are all included which no one has done this before. The experimental and numerical results reveal that the constructed model can exactly forecast the vehicular network performance of packet delay, delivery rate, and reception rate under different traffic and channel circumstances.

Purpose – The purpose of this paper is to present a model calibration technique for modulated wideband converter (MWC) with non-ideal lowpass filter. Without making any change to the system architecture, at the cost of a moderate oversampling, the calibrated system can perform as the system with ideal lowpass filter. Design/methodology/approach – A known test sparse signal is used to approximate the finite impulse response (FIR) of the practical non-ideal lowpass filter. Based on the approximated FIR filter, a digital compensation filter is designed to calibrate the practical filter. The calibrated filter can meet the perfect reconstruction condition. The non-ideal sub-Nyquist samples are filtered by a compensation filter. Findings – Experimental results indicate that, by calibrating the MWC with the proposed algorithm, the impaction of non-ideal lowpass filter could be avoided. The performance of signal reconstruction could be improved significantly. Originality/value – Without making any change to the MWC architecture, the proposed algorithm can calibrated the non-ideal lowpass filter. By filtering the non-ideal sub-Nyquist samples with the designed compensation filter, the original signal could be reconstructed with high accuracy.

The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.