Group-Based Successive Interference Cancellation for Multi-Antenna NOMA System with Error Propagation

Non-orthogonal multiple access(NOMA)is viewed as a key technique to improve the spectrum efficiency and solve the issue of massive connectivity.However,for power domain NOMA,the required overall transmit power should be increased rapidly with the increasing number of users in order to ensure that the signal-to-interference-plus-noise ratio reaches a predefined threshold.In addition,since the successive interference cancellation(SIC)is adopted,the error propagation would become more serious as the order of SIC increases.Aiming at minimizing the total transmit power and satisfying each user’s service requirement,this paper proposes a novel framework with group-based SIC for the deep integration between power domain NOMA and multi-antenna technology.Based on the proposed framework,a joint optimization of power control and equalizer design is investigated to minimize transmit power consumption for uplink multi-antenna NOMA system with error propagations.Based on the relationship between the equalizer and the transmit power coefficients,the original problem is transformed to a transmit power optimization problem,which is further addressed by a parallel iteration algorithm.It is shown by simulations that,in terms of the total power consumption,the proposed scheme outperforms the conventional OMA and the existing cluster-based NOMA schemes.

Handling Error Propagation in Sequential Data Assimilation Using an Evolutionary Strategy

An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models （i.e., the Lorenz-63 model and the Lorenz-96 model）. Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects： the initial conditions of the Lorenz model, ensemble sizes, observation covarianee, and the observation intervals. The search for error adjustment factors is usually performed using trial-and-error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy- based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.

A SCR method for uncertainty estimation in geodesy non-linear error propagation: Comparisons and applications

We review three derivative-free methods developed for uncertainty estimation of non-linear error propagation, namely, MC(Monte Carlo), SUT(scaled unscented transformation), and SI(sterling interpolation). In order to avoid preset parameters like as these three methods need, we introduce a new method to uncertainty estimation for the first time, namely, SCR(spherical cubature rule), which is no need for setting parameters. By theoretical derivation, we prove that the precision of uncertainty obtained by SCR can reach second-order. We conduct four synthetic experiments, for the first two experiments, the results obtained by SCR are consistent with the other three methods with optimal setting parameters, but SCR is easier to operate than other three methods, which verifies the superiority of SCR in calculating the uncertainty. For the third experiment, real-time calculation is required, so the MC is hardly feasible. For the forth experiment, the SCR is applied to the inversion of seismic fault parameter which is a common problem in geophysics, and we study the sensitivity of surface displacements to fault parameters with errors. Our results show that the uncertainty of the surface displacements is the magnitude of ±10 mm when the fault length contains a variance of 0.01 km^(2).

MIMO-DFE BASED SPACE-TIME RECEIVER OVER FREQUENCY SELECTIVE CHANNELS WITH LIMITED ERROR PROPAGATION

MIMO-DFE(Multiple-Input-Multiple-Output Decision Feedback Equalizer) based receiver architectures are researched recently to detect signals in BLAST(Bell laboratories LAyered Space-Time) over frequency-selective channels. Due to their recursive structure, these receivers may suffer from error propagation which results in an overall mean square error degradation. An MIMO-DFE based BLAST receiver with limited error propagation to combat frequencyselective channel is proposed, which employs both norm constraint on feedback filter taps and soft decision device. Simulation results show that the proposed receiver outperforms conventional ones in various frequency selective channels.

Total Sensitivity Index Calculation of Tool Requirement Model via Error Propagation Equation

A new and convenient method is presented to calculate the total sensitivity indices defined by variance-based sensitivity analysis. By decomposing the output variance using error propagation equations, this method can transform the ＂double-loop＂ sampling procedure into ＂single-loop＂ one and obviously reduce the computation cost of analysis. In contrast with Sobors and Fourier amplitude sensitivity test （FAST） method, which is limited in non-correlated variables, the new approach is suitable for correlated input variables. An application in semiconductor assembling and test manufacturing （ATM） factory indicates that this approach has a good performance in additive model and simple non-additive model.

Extensions of nonlinear error propagation analysis for explicit pseudodynamic testing

Two important extensions of a technique to perform a nonlinear error propagation analysis for an explicit pseudodynamic algorithm （Chang, 2003） are presented. One extends the stability study from a given time step to a complete step-by-step integration procedure. It is analytically proven that ensuring stability conditions in each time step leads to a stable computation of the entire step-by-step integration procedure. The other extension shows that the nonlinear error propagation results, which are derived for a nonlinear single degree of freedom （SDOF） system, can be applied to a nonlinear multiple degree of freedom （MDOF） system. This application is dependent upon the determination of the natural frequencies of the system in each time step, since all the numerical properties and error propagation properties in the time step are closely related to these frequencies. The results are derived from the step degree of nonlinearity. An instantaneous degree of nonlinearity is introduced to replace the step degree of nonlinearity and is shown to be easier to use in practice. The extensions can be also applied to the results derived from a SDOF system based on the instantaneous degree of nonlinearity, and hence a time step might be appropriately chosen to perform a pseudodynamic test prior to testing.

Error Propagation in Geochemical Calculations of Groundwater-Mineral Interactions:A Case Study from the Pingdingshan Coalfield,Henan

The WATEQ4F-based “multiple step method”, or the method of running WATEQ4F repeatedly, was used to evaluate the error propagation in computations of water-mineral interactions under the circumstances of constant and varied temperatures due to the uncertainties of input variables. The results show the following: the errors of water chemistry analysis can strongly affect the modelling results of water-mineral reactions; different input variables (errors) have different effects on the saturation indices (S.I.) of different minerals; in many cases, the S.I. errors of minerals change with temperatures.

Error propagation determined iterative channel estimation with ICI mitigation for fast time-varying OFDM channels

The intersubcarrier interference(ICI) degrades the performance of the pilot-aided channel estimation in fast time-varying orthogonal frequency division multiplexing(OFDM) systems.To solve the error propagation in joint channel estimation and data detection due to this ICI,a scheme of error propagation determined iterative estimation is proposed,where in the first iteration,Kalman filter based on signal to interference and noise is designed with ICI transformed to be part of the noise,and for the later iterations,a determined iterative estimation algorithm obtains an optimal output from all iterations using the iterative updating strategy.Simulation results present the significant improvement in the performance of the proposed scheme in high-mobility situation in comparison with the existing ones.

MLSE-assisted decision feedback equalizer for error-propagation suppression in high-speed IM-DD transmission systems

We propose a trellis-compressed maximum likelihood sequence estimation(TC-MLSE)-assisted sliding-block decision feedback equalizer(DFE)to suppress the error propagation resulting from the DFE in high-speed systems.We use an out-ofrange detector to detect the end of burst errors from the DFE and activate the optional TC-MLSE to correct burst errors.We conduct experiments to transmit a 201-Gbit/s PAM-8 signal.The results show that the proposed method achieves a bit error rate of 3.65×10^(-3),which is close to that of MLSE.The optional MLSE is only activated when needed and processes 11.4%of the total symbols.Moreover,the proposed method compresses the maximum length of burst errors from 19 to 5.

Locating Error Considering Dimensional Errors Modeling for Multistation Manufacturing System

Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.

Altimetric Algorithm and Errors of Ocean Altimetry Using GNSS Reflection Signals

As a new remote sensing technology, the global navigation satellite system（GNSS） reflection signals can be used to collect the information of ocean surface wind, surface roughness and sea surface height. Ocean altimetry based on GNSS reflection technique is of low cost and it is easy to obtain large amounts of data thanks to the global navigation satellite constellation. We can estimate the sea surface height as well as the position of the specular reflection point. This paper focuses on the study of the algorithm to determine the specular reflection point and altimetry equations to estimate the sea surface height over the reflection region. We derive the error equation of sea surface height based on the error propagation theory. Effects of the Doppler shift and the size of the glistening zone on the altimetry are discussed and analyzed at the same time. Finally, we calculate the sea surface height based on the simulated GNSS data within the whole day and verify the sea surface height errors according to the satellite elevation angles. The results show that the sea surface height can reach the precision of 6 cm for elevation angles of 55° to 90°, and the theoretical error and the calculated error are in good agreement.

Content based error resilience coding using redundant coding, reference frame modification, and mode decision

Quality degradation occurs during transmission of video streaming over the error-prone network. By jointly using redundant slice, reference frame selection, and intra/inters mode decision, a content and end-to-end rate-distortion based error resilience method is proposed. Firstly, the intra/inter mode decision is implemented using macro-block(MB) refresh, and then redundant picture and reference frame selection are utilized together to realize the redundant coding. The estimated error propagation distortion and bit consumption of refresh MB are used for the mode and reference frame decision of refresh MB. Secondly, by analyzing the statistical property in the successive frames, the error propagation distortion and bit consumption are formulated as a function of temporal distance. Encoding parameters of the current frame is determined by the estimated error propagation distortion and bit consumption. Thirdly, by comparing the rate-distortion cost of different combinations, proper selection of error resilience method is performed before the encoding process of the current frame. Finally, the MB mode and bit distribution of the primary picture are analyzed for the derivation of the texture information. The motion information is subsequently incorporated for the calculation of video content complexity to implement the content based redundant coding. Experimental results demonstrate that the proposed algorithm achieves significant performance gains over the LA-RDO and HRP method when video is transmitted over error-prone channel.

Temperature compensation method of silicon microgyroscope based on BP neural network

The temperature characteristics of a silicon microgyroscope are studied, and the temperature compensation method of the silicon microgyroscope is proposed. First, an open-loop circuit is adopted to test the entire microgyroscope＇s resonant frequency and quality factor variations over temperature, and the zero bias changing trend over temperature is measured via a closed-loop circuit. Then, in order to alleviate the temperature effects on the performance of the microgyroscope, a kind of temperature compensated method based on the error back propagation（BP）neural network is proposed. By the Matlab simulation, the optimal temperature compensation model based on the BP neural network is well trained after four steps, and the objective error of the microgyroscope＇s zero bias can achieve 0.001 in full temperature range. By the experiment, the real time operation results of the compensation method demonstrate that the maximum zero bias of the microgyroscope can be decreased from 12.43 to 0.75（°）/s after compensation when the ambient temperature varies from -40 to 80℃, which greatly improves the zero bias stability performance of the microgyroscope.

Wireless location algorithm using digital broadcasting signals based on neural network

In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.

Adaptive Fuzzy Neural Control of Dynamic System

Aim To build an adaptive fuzzy neural controller and simulate it. Methods\ Fuzzy logic and back propagation(BP) algorithm are combined to utilize their advantages while avoiding the disadvantages. Results and Conclusion\ Simulation results of the third order plant with disturbances and dead times show the validity of the presented controller. The presented controller can control cases that preceding controllers were unable to control.

Analytical exploration of γ-function explicit method for pseudodynamic testing of nonlinear systems

It has been well studied that the γ-function explicit method can be effective in providing favorable numerical dissipation for linear elastic systems. However, its performance for nonlinear systems is unclear due to a lack of analytical evaluation techniques. Thus, a novel technique is proposed herein to evaluate its efficiency for application to nonlinear systems by introducing two parameters to describe the stiffness change. As a result, the numerical properties and error propagation characteristics of the γ-function explicit method for the pseudodynamic testing of a nonlinear system are analytically assessed. It is found that the upper stability limit decreases as the step degree of nonlinearity increases; and it increases as the current degree of nonlinearity increases. It is also shown that this integration method provides favorable numerical dissipation not only for linear elastic systems but also for nonlinear systems. Furthermore, error propagation analysis reveals that the numerical dissipation can effectively suppress the severe error propagation of high frequency modes while the low frequency responses are almost unaffected for both linear elastic and nonlinear systems.

New SST correction method from multi-satellite based on the coefficient of variation

In remote sensing sea surface temperature （SST）, the traditional fusion method is used to compute the dot product of a subjective weight vector with a satellite measurement vector, while the result requires validation by field measurement. However, field measurement that relative to the satellite measurement is very sparse, many information may not be verified. A relative objective weight vector is constructed by using the limited field measurement, which is based on coefficient of variation method. And then it make an application of the data fusion by the weighted average method in the SST data. fuse SST data with the weighted average method. In this way, some posteriori information can be added to the fusion process. The model reduces the dependence on verification, and some of the satellite measurement can be handled without corresponding to the field measurement, and the fusion result matches transfer errors theory.

Positioning Precision Analysis of GNSS Multi-frequency Carrier Phase Combinations

GPS positioning precision is affected by various error sources, and traditional combinations of GPS carrier phase observations have their own limitations such as the wide-lane, the narrow-lane and the ionospheric-free combinations. To obtain the optimal positioning precision, a new linear combination method is addressed through the variance-covariance （VCV） of the GPS multi-frequency carrier phase combination equations, and the impact of the positioning precision is analyzed with the changing of the observation errors deduced by the law of error propagation. For the high precision positioning with only one carrier phase combination, the optimal combination method is deduced and further validated by an example of a baseline resolution with 60 km length. The result indicates that this method is the simplest, and the positioning precision is the best. Therefore, it is useful for long baseline quick positioning for different precision requirements in various distances.

Computing mean fields with known Reynolds stresses at steady state

With the rising of modern data science,data-driven turbulence modeling with the aid of machine learning algorithms is becoming a new promising field.Many approaches are able to achieve better Reynolds stress prediction,with much lower modeling error(∈_(M)),than traditional Reynolds-averaged Navier-Stokes(RANS)models,but they still suffer from numerical error and stability issues when the mean velocity fields are estimated by solving RANS equations with the predicted Reynolds stresses.This fact illustrates that the error of solving the RANS equations(∈_(P))is also very important for a RANS simulation.In the present work,the error∈_(P)is studied separately by using the Reynolds stresses obtained from direct numerical simulation(DNS)/highly resolved large-eddy simulation to minimize the modeling error∈_(M),and the sources of∈_(P)are derived mathematically.For the implementations with known Reynolds stresses solely,we suggest to run an auxiliary RANS simulation to make a first guess onν_(t)^(*)and S_(ij)^(0).With around 10 iterations,the error of the streamwise velocity component could be reduced by about one-order of magnitude in flow over periodic hills.The present work is not to develop a new RANS model,but to clarify the facts that obtaining mean field with known Reynolds stresses is nontrivial and that the nonlinear part of the Reynolds stresses is very important in flow problems with separations.The proposed approach to reduce∈_(P)may be very useful for the a posteriori applications of the data-driven turbulence models.

Misclassification error propagation in land cover change categorization

It is important to describe misclassification errors in land cover maps and to quantify their propagation through geo-processing to resultant information products,such as land cover change maps.Geostatistical simulation is widely used in error modeling,as it can generate equal-probable realizations of the fields being considered,which can be summarized to facilitate error propagation analysis.To fix noninvariance in indicator simulation,discriminant space-based methods were proposed to enhance consistency in area-class mapping and replicability in uncertainty modeling,as the former is achieved by imposing means while the latter is ensured by projecting spatio-temporal correlated residuals in discriminant space to geographic space through a mapping process.This paper explores discriminant models for error propagation in land cover change detection,followed by experiments based on bi-temporal remote sensing images.It was found that misclassification error propagation is effectively characterized with discriminant covariate-based stochastic simulation,where spatio-temporal interdependence is taken into account.