Timing synchronization using cross ambiguity function for MIMO OFDM systems with distributed antennas

In this letter, we propose a novel timing offset estimation method for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with distributed antennas using cross ambiguity function (CAF). The proposed algorithm has an impulse-shaped timing metric which allowed it to reduce the uncertainty due to the timing metric plateau. Simulation results show that the mean square error (MSE) performance of the proposed method is significantly improved.     

Cloud security situation prediction method based on grey wolfoptimization and BP neural network

Aiming at the accuracy and error correction of cloud security situation prediction, a cloud security situation prediction method based on grey wolf optimization (GWO) and back propagation (BP) neural network is proposed.Firstly, the adaptive disturbance convergence factor is used to improve the GWO algorithm, so as to improve theconvergence speed and accuracy of the algorithm. The Chebyshev chaotic mapping is introduced into the positionupdate formula of GWO algorithm, which is used to select the features of the cloud security situation prediction dataand optimize the parameters of the BP neural network prediction model to minimize the prediction output error.Then, the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm toincrease the learning efficiency and accuracy of BP neural network. Finally, the real data sets of Tencent cloudplatform are predicted. The simulation results show that the proposed method has lower mean square error (MSE)and mean absolute error (MAE) compared with BP neural network, BP neural network based on genetic algorithm(GA-BP), BP neural network based on particle swarm optimization (PSO-BP) and BP neural network based onGWO algorithm (GWO-BP). The proposed method has better stability, robustness and prediction accuracy.     

Characterization of Code Tracking Error of Coherent DLL Under CW Interference

Characterization of the tracking error under continuous wave (CW) interference is the foundation of the interference detection and mitigation in receiver. It is helpful to select pseudo-random noise (PRN) codes with better code tracking performance under interference during the design of signal structure. Considering the effect of discrete spectral line of PRN code, we present a detailed analysis of tracking error of coherent delay locked loop (DLL) for the both situations that when the interference overlaps the spectral line and that when the interference is around the spectral line. In the first situation, the expression of the maximum tracking error for binary phase shift key (BPSK) modulated signal is obtained. The comparison of the tracking errors between finite and infinite frontend bandwidth cases in the first situation shows that the tracking error in the finite case is not like that in the infinite case and cannot be always reduced by narrowing the correlator spacing. The reason of this phenomenon is explained in detail. Further, we obtain the expression of the tracking error limitation as the correlator spacing tends to zero. It is more reasonable to take this limitation as the criterion of evaluating the code tracking performance for different PRN codes under interference. In the second situation, with the assumption of the infinite frontend bandwidth, we obtain the analytic expression of the tracking error. The analysis results show that the impact of the interference on tracking performance decays quickly when the interference leaves far away from the spectral line, and the diminution of correlator spacing and loop bandwidth both can reduce the tracking error efficiently.     

基于改进贝叶斯正则化BP神经网络模型的网络安全态势预测方法研究

随着互联网的迅速发展,网络安全问题越来越严重,分析及预测网络安网络安全态势,对于网络安全具有重要意义。本文在网络安全态势量化的的基础上,改进贝叶斯算法,提出一种改进型贝叶斯正则化BP神经网络模型的网络安全态势预测方法,通过模拟网络环境进行数据分析,验证了该预测方法可以减小了训练误差和预测误差,提高了对网络安全态势预测精度,证明了该方法的可行性。     

Analysis of stability and performance of adaptation algorithms with time-invariant gains

Adaptation laws that track parameters of linear regression models are investigated. The considered class of algorithms apply linear time-invariant filtering on the instantaneous gradient vector and includes least mean squares (LMS) as its simplest member. The asymptotic stability and steady-state tracking performance for prediction and smoothing estimators is analyzed for parameter variations described by stochastic processes with time-invariant statistics. The analysis is based on a novel technique that decomposes the inherent feedback of adaptation algorithms into one time-invariant loop and one time-varying loop. The impact of the time-varying feedback on the tracking error covariance can be neglected under certain conditions, and the performance analysis then becomes straightforward. Performance analysis in the presence of a non-negligible time-varying feedback is performed for algorithms that use scalar measurements. Convergence in mean square error (MSE) and the MSE tracking performance is investigated, assuming independent consecutive regression vectors. Closed-form expressions for the tracking MSE are thereafter derived without this independence assumption for a subclass of algorithms applied to finite impulse response (FIR) models with white inputs. This class includes Wiener LMS adaptation.     

Poisson models and mean-squared error for correlator estimator oftime delay

A method for modeling large errors in correlation-based time-delay estimation is developed in terms of level-crossing probabilities. The level-crossing interpretation for peak ambiguity leads directly to an exact expression for the probability of large error involving the hazard function associated with the level-crossing process. Two models for the distribution of the error over the level-crossing time yield approximations to the mean-square error (MSE) that involve the low-order (<4) finite-dimensional distributions of the associated level-crossing process. Application of an inhomogeneous Poisson model for the level crossings reduces the form of the approximations to a weighted sum of the Cramer-Rao lower bound and the second moment of a function of the level-crossing intensity over time. Explicit expressions for the large error probability and the MSE approximations are obtained under a Gaussian model for the correlator statistics. Results of computer simulation are presented that indicate the accuracy of the approximations     

On 2-D Direction-of-Arrival Estimation Performance for Rank Reduction Estimator in Presence of Unexpected Modeling Errors

The rank reduction estimator (RARE) is one kind of autocalibration method used in the presence of sensor errors. It demonstrates high accuracy of direction-of-arrival (DOA) estimation in the absence of multidimensional search or iteration. However, its estimation performance is affected by “unexpected modeling errors.” There is a lack of research regarding the performance of 2-D RARE estimation, although 2-D RARE estimation is extensively employed in applications. This paper presents a theoretical derivation for the closed-form expression of the mean square error (MSE) of 2-D RARE estimation under the influence of small unexpected modeling errors in the first order analysis. First, three definitions of 2-D joint direction-finding success are introduced, in order to establish the criterion for estimate performance. Then corresponding theoretical formulas for three probabilities of direction-finding success are given with the circularly Gaussian assumption of unexpected modeling errors, and their relations are discussed. Finally, the results of simulations utilizing our analysis method are demonstrated, verifying the effectiveness of the MSE expression and the formulas for probabilities of success. Therefore, our first order approximation provides a good prediction of the necessary calibration accuracy in the presence of unexpected modeling errors in order to help RARE meet an expected performance specification.     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.     

天线方向性误差对DOA估计影响及其校正

天线阵元的方向性误差会导致基于特征值分解的DOA估计算法性能的下降,因此有必要对阵元方向性误差进行校正.首先建立了存在方向性误差情况下DOA估计的数学分析模型,提出利用空间平滑法和改进空间平滑法对方向性误差进行校正.仿真实验表明,方向性误差对DOA估计的影响主要表现在谱峰峰值的降低.空间平滑法和改进空间平滑法均可以有效地对方向性误差进行校正.     

Mean-squared error and threshold SNR prediction of maximum-likelihood signal parameter estimation with estimated colored noise covariances

An interval error-based method (MIE) of predicting mean squared error (MSE) performance of maximum-likelihood estimators (MLEs) is extended to the case of signal parameter estimation requiring intermediate estimation of an unknown colored noise covariance matrix; an intermediate step central to adaptive array detection and parameter estimation. The successful application of MIE requires good approximations of two quantities: 1) interval error probabilities and 2) asymptotic (SNRrarrinfin) local MSE performance of the MLE. Exact general expressions for the pairwise error probabilities that include the effects of signal model mismatch are derived herein, that in conjunction with the Union Bound provide accurate prediction of the required interval error probabilities. The Crameacuter-Rao Bound (CRB) often provides adequate prediction of the asymptotic local MSE performance of MLE. The signal parameters, however, are decoupled from the colored noise parameters in the Fisher Information Matrix for the deterministic signal model, rendering the CRB incapable of reflecting loss due to colored noise covariance estimation. A new modification of the CRB involving a complex central beta random variable different from, but analogous to the Reed, Mallett, and Brennan beta loss factor provides a working solution to this problem, facilitating MSE prediction well into the threshold region with remarkable accuracy     

Robust MSE equalizer design for MIMO communication systems in the presence of model uncertainties

This paper considers a robust mean-square-error (MSE) equalizer design problem for multiple-input multiple-output (MIMO) communication systems with imperfect channel and noise information at the receiver. When the channel state information (CSI) and the noise covariance are known exactly at the receiver, a minimum-mean-square-error (MMSE) equalizer can be employed to estimate the transmitted signal. However, in actual systems, it is necessary to take into account channel and noise estimation errors. We consider here a worst-case equalizer design problem where the goal is to find the equalizer minimizing the equalization MSE for the least favorable channel model within a neighborhood of the estimated model. The neighborhood is formed by placing a bound on the Kullback-Leibler (KL) divergence between the actual and estimated channel models. Lagrangian optimization is used to convert this min-max problem into a convex min-min problem over a convex domain, which is solved by interchanging the minimization order. The robust MSE equalizer and associated least favorable channel model can then be obtained by solving numerically a scalar convex minimization problem. Simulation results are presented to demonstrate the MSE and bit error rate (BER) performance of robust equalizers when applied to the least favorable channel model.     

一种基于结构相似性的H．264／AVC运动预测方法

在H．264编码过程中,帧间预测的最佳匹配块的选择和编码模式的判决由率失真代价函数决定,在该函数中,通常对失真采用的衡量方法是绝对（变换）误差和（SATD）。相应地,其他类似的衡量方法,如MSE和PSNR也被用在质量评估当中。然而,SA（T）D和PSNR已经被证明不能反映人眼视觉对失真的真实的敏感程度。最近,人们提出了一种称为HSSIM（基于人眼视觉系统的结构相似性）的新的图像质量测度,由于更好地考虑了图像的结构信息,HSSIM与人类视觉的一致性优于PSNR和MSE。文中提出了一种用于帧间编码的新的运动预测方法（MEHSSIM）,它建立在基于人眼视觉系统的结构相似性的基础之上。实验表明,在由HSSIM测量的主观视频质量基本保持不变的情况下,新方法可以使码率降低平均13．8％。     

Error-sensitivity assessment of vision algorithms

A methodology is proposed for the computation of output-data errors from given input-data errors for a given computational precision for vision algorithms. The approach is based on replacement of the arithmetic types of the object-oriented language used for coding of the tested algorithm by structures describing the values and error models of the numbers represented. The method is useful both for the initial testing of any vision algorithm and for the definitive testing of algorithms for which explicit error models are not known. It is also shown how two error models, namely that of the min/max error propagation and that of local-function linearisation, can be used for assessing the errors introduced by the input data and by the hardware architecture adopted     

Rate-distortion analysis of motion-compensated rate scalable video.

Generally speaking, rate scalable video systems today are evaluated operationally, meaning that the algorithm is implemented and the rate-distortion performance is evaluated for an example set of inputs. However, in these cases it is difficult to separate the artifacts caused by the compression algorithm and data set with general trends associated with scalability. In this paper, we derive and evaluate theoretical rate-distortion performance bounds for both layered and continuously rate scalable video compression algorithms which use a single motion-compensated prediction (MCP) loop. These bounds are derived using rate-distortion theory based on an optimum mean-square error (MSE) quantizer, and are thus applicable to all methods of intraframe encoding which use MSE as a distortion measure. By specifying translatory motion and using an approximation of the predicted error frame power spectral density, it is possible to derive parametric versions of the rate-distortion functions which are based solely on the input power spectral density and the accuracy of the motion-compensated prediction. The theory is applicable to systems which allow prediction drift, such as the data-partitioning and SNR-scalability schemes in MPEG-2, as well as those with zero prediction drift such as fine granularity scalability MPEG-4. For systems which allow prediction drift we show that optimum motion compensation is a sufficient condition for stability of the decoding system.     

Covering properties of convolutional codes and associated lattices

The paper describes Markov methods for analyzing the expected and worst case performance of sequence-based methods of quantization. We suppose that the quantization algorithm is dynamic programming, where the current step depends on a vector of path metrics, which we call a metric function. Our principal objective is a concise representation of these metric functions and the possible trajectories of the dynamic programming algorithm. We shall consider quantization of equiprobable binary data using a convolutional code. Here the additive group of the code splits the set of metric functions into a finite collection of subsets. The subsets form the vertices of a directed graph, where edges are labeled by aggregate incremental increases in mean squared error (MSE). Paths in this graph correspond both to trajectories of the Viterbi algorithm and to cosets of the code. For the rate 1/2 convolutional code [1+D², 1+D+D²], this graph has only nine vertices. In this case it is particularly simple to calculate per dimension expected and worst case MSE, and performance is slightly better than the binary [24, 12] Golay code. Our methods also apply to quantization of arbitrary symmetric probability distributions on [0, 1] using convolutional codes. For the uniform distribution on [0, 1], the expected MSE is the second moment of the “Voronoi region” of an infinite-dimensional lattice determined by the convolutional code. It may also be interpreted as an increase in the reliability of a transmission scheme obtained by nonequiprobable signaling. For certain convolutional codes we obtain a formula for expected MSE that depends only on the distribution of differences for a single pair of path metrics     

Predicting software development errors using software complexitymetrics

Predictive models that incorporate a functional relationship of program error measures with software complexity metrics and metrics based on factor analysis of empirical data are developed. Specific techniques for assessing regression models are presented for analyzing these models. Within the framework of regression analysis, the authors examine two separate means of exploring the connection between complexity and errors. First, the regression models are formed from the raw complexity metrics. Essentially, these models confirm a known relationship between program lines of code and program errors. The second methodology involves the regression of complexity factor measures and measures of errors. These complexity factors are orthogonal measures of complexity from an underlying complexity domain model. From this more global perspective, it is believed that there is a relationship between program errors and complexity domains of program structure and size (volume). Further, the strength of this relationship suggests that predictive models are indeed possible for the determination of program errors from these orthogonal complexity domains     

Fixed-point error analysis of CORDIC processor based on the variance propagation formula

This paper presents a fixed-point mean-square error (MSE) analysis of coordinate rotation digital computer (CORDIC) processors based on the variance propagation method, whereas the conventional approaches provide only the error bound which results in large discrepancy between the analysis and actual implementation. The MSE analysis is aimed at obtaining a more accurate analysis of digital signal processing systems with CORDIC processor, especially when the design specification is given by the signal-to-noise ratio or MSE. For the MSE analysis, the error source and models are first defined and the output error is derived in terms of MSE in the rotation mode of the conventional CORDIC processor. It is shown that the proposed analysis can also be applied to the modified CORDIC algorithms. As an example of practical application, a fast Fourier transform processor using the CORDIC processor is presented in this paper, and its output error variance is analyzed with respect to the wordlength of CORDIC. The results show a close match between the analysis and simulation.     

Effects of channel estimation error on array processing based QO-STBC coded OFDM systems

This letter addresses the issues on performance degradation caused by channel estimation error of quasiorthogonal space-time block (QO-STBC) coded OFDM systems employing array processing decoder. The least square (LS) channel estimator is employed to obtain the required channel state information (CSI). Taking mean square error (MSE) as a performance metric, we evaluate the performance of the LS estimator in MIMO-OFDM systems over frequency selective channels, and its impact on symbol error rate (SER) using both analysis and simulations.     

Control-relevant models for glucose control using a priori patient characteristics

One of the difficulties in the development of a reliable artificial pancreas for people with type 1 diabetes mellitus (T1DM) is the lack of accurate models of an individual's response to insulin. Most control algorithms proposed to control the glucose level in subjects with T1DM are model-based. Avoiding postprandial hypoglycemia ( 60 mg/dl) while minimizing prandial hyperglycemia ( > 180 mg/dl) has shown to be difficult in a closed-loop setting due to the patient-model mismatch. In this paper, control-relevant models are developed for T1DM, as opposed to models that minimize a prediction error. The parameters of these models are chosen conservatively to minimize the likelihood of hypoglycemia events. To limit the conservatism due to large intersubject variability, the models are personalized using a priori patient characteristics. The models are implemented in a zone model predictive control algorithm. The robustness of these controllers is evaluated in silico, where hypoglycemia is completely avoided even after large meal disturbances. The proposed control approach is simple and the controller can be set up by a physician without the need for control expertise.