Space-time two dimensional RAKE receiver was introduced into asynchronous and cooperative communication system, and a high-performance RAKE receiving algorithm was proposed. In this algorithm, the transmitting signals were estimated coarsely by traditional space-time rake receiver, then each multipath signal was reconstructed using the estimated result as well as the channel impulse response, and then eliminated successively from reception signals, so it can eliminate Inter Symbol Interference of receiver signals which generate by multipath fading, and the line of sight (LOS) component in the received signal was obtained, and finally, the space-time combination was carried out to the LOS component, and the spatial diversity was achieved. This algorithm can effectively reduce the bit error rate of the asynchronous cooperative communication system. Simulation result shows that the new algorithm can improve the performance of traditional space-time RAKE when SNR is higher than 5dB dramatically.
Abstract With the development of data acquisition ability, the LEO satellites can work with a surveillance camera on board by focusing a particular area for dozens of seconds, so it drives us to develop some applications based on this data. In this paper, a data preparation method named DERS (Density Estimation based Road Segmentation) is proposed to divide the road from background to reduce the worse impact from relative-motion and then improve the precession and recall for vehicles for surveillance satellite video sequence. DERS has three main steps: frame difference, morphology, pyramid iteration, segmentation. Our experiments with the satellite videos from SkySat-1, and compared between Gaussian Mixture Model (GMM) and GMM with Ders shows that the precision rate and recall has improved of the traditional method with Ders, the precision of GMM with Ders has been improved from 64.42% to 88.71, the recall of GMM with Ders is from 76.21% to 74.16%, and the F-Score has been improved from 69.82% to 80.79%.
ASatelliteEarthTargetgeometric model is presented, and the exact Doppler centroid is obtained. On the basis of the simplified model, the yaw steering is found. From the analysis of the two models, a conclusion about the yaw steering's compensation effect is drawn.
The phenomenon of stochastic resonance in a complex nonlinear system which is excited by both complex weak periodic signal and noise is investigated in this paper. The model of complex nonlinear system is given, and the effects of the input periodic signal amplitude and the noise intensity on the response amplitude of the system at the periodic signal frequency are discussed through numerical simulations. It is shown that the response amplitude of the system to the input periodic signal displays a non-monotonic dependence on the noise intensity, and the response peaks at a particular value of the noise intensity, which is known as stochastic resonance. The results in this paper propose a new way for controlling stochastic resonance in a complex nonlinear system.
Short-term load forecasting is very important for power systems. The load is related to many factors which compose tensors. However, tensors cannot be input directly into most traditional forecasting models. This paper proposes a tensor partial least squares-neural network model (TPN) to forecast the power load. The model contains a tensor decomposition outer model and a nonlinear inner model. The outer model extracts common latent variables of tensor input and vector output and makes the residuals less than the threshold by iteration. The inner model determines the relationship between the latent variable matrix and the output by using a neural network. This model structure can preserve the information of tensors and the nonlinear features of the system. Three classical models, partial least squares (PLS), least squares support vector machine (LSSVM) and neural network (NN), are selected to compare the forecasting results. The results show that the proposed model is efficient for short-term load and daily load peak forecasting. Compared to PLS, LSSVM and NN, the TPN has the best forecasting accuracy.
Because of the increasing demand for electrical energy, vibration energy harvesters (VEHs) that convert vibratory energy into electrical energy are a promising technology. In order to improve the efficiency of harvesting energy from environmental vibration, here we investigate a hybrid VEH. Unlike previous studies, this article analyzes the stochastic responses of the hybrid piezoelectric and electromagnetic energy harvesting system with viscoelastic material under narrow-band (colored) noise. Firstly, a mass-spring-damping system model coupled with piezoelectric and electromagnetic circuits under fundamental acceleration excitation is established, and analytical solutions to the dimensionless equations are derived. Then, the formula of the amplitude-frequency responses in the deterministic case and the first-order and second-order steady-state moments of the amplitude in the stochastic case are obtained by using the multi-scales method. The amplitude-frequency analytical solutions are in good agreement with the numerical solutions obtained by the Monte Carlo method. Furthermore, the stochastic bifurcation diagram is plotted for the first-order steady-state moment of the amplitude with respect to the detuning frequency and viscoelastic parameter. Eventually, the influence of system parameters on mean-square electric voltage, mean-square electric current and mean output power is discussed. Results show that the electromechanical coupling coefficients, random excitation and viscoelastic parameter have a positive effect on the output power of the system.
Network selection in the Internet of Vehicles has become a popular topic of research. Unlike existing algorithms for heterogeneous network environments that rarely consider user satisfaction, in this paper, we propose a network selection strategy that takes into account both user satisfaction and transmission efficiency. We employ the effective capacity concept, which describes the maximum throughput a system can achieve under a specific statistical Quality-of-Service (QoS) delay violation probability constraint. This strategy first analyzes the influence of different utility function weight coefficients, transmission power, and time delay on each network utility satisfaction function. It is evident that the weight coefficient is proportional to the value of the utility function. Within a constrained transmission power range, the rate of increase of the function gradually slows down until it approaches a fixed value. When the delay factor value is larger, the function value is smaller, which indicates that the pursuit of lower delay will sacrifice other network performance aspects. In order to determine the maximum value of each network utility satisfaction function, a convex optimization theory is introduced for the joint optimization of user satisfaction and transmission efficiency. Finally, simulation experiments carried out under three representative network environments show that the proposed strategy is efficient and reliable.
Small cells have been regarded as an appealing technique to boost resource reuse ratio. On the other hand, their large-scale and self-organised tendency would complicate the interference environment of mobile networks. Meanwhile, traffic class is booming in recent years, which leads to higher demand for network designers on Quality of Service (QoS) provision, and therefore users' diverse requirements may not be guaranteed in such an interference limited scenario. To maximise the number of users with QoS demands as well as resource reuse ratio, we formulate the resource allocation problem into a multi-objective l 0 norm form. It is shown to be NP hard, and an iterative method is employed to approach the optimal solution. Because of its limit of being not adaptive to large-scale networks, we also design a heuristic method based on chordal graph, which, however, could result in performance loss when the size of networks is small. Finally, by combining these two methods, we devise a hybrid algorithm such that the allocation performs both efficiently and effectively. Simulation results illustrate the performance of our proposed methods in terms of outage probability and resource reuse ratio.
The exponential growth of services demands further increase of spectral efficiency which drives the next generation wireless access networks towards deploying femtocells with frequency reuse. However, the interference will be severe especially in dense deployment scenarios. The fair resource allocation problem by joint consideration of sub‐channel assignment and interference alignment (IA) is more complicated than the traditional problem. First, not all the users are appropriate for IA since the number of participant users is limited by the feasibility constraint and the interference power levels are different for the path loss. Second, IA can increase the degrees‐of‐freedoms while occupy additional signal dimensions of participant users, hence more sub‐channels are needed by IA compared with the non‐participants when each user transmits the same number of streams. This study models the fair resource allocation problem as an optimisation problem, which is a non‐deterministic polynomial‐time (NP)‐hard. To solve it with low complexity, the authors propose a graph‐based scheme to give the approximate solution, where the selection criteria of IA group are based on the influence of IA on the interference graph. Simulation results show that the authors scheme can approximate the optimal solution in a small network and improve the fairness in dense deployment scenarios.
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