Acceptance sampling: an efficient, accurate method for estimatingand optimizing parametric yield [IC manufacture]

Acceptance sampling is a new yield estimation and optimization method which combines the accuracy of Monte Carlo analysis with the computational efficiency of response surface methods. Response surface approximations are used to guide selection of simulation samples. Formulas based on established statistical methods (viz., confidence intervals, stratified sampling) estimate yield and predict accuracy. Yield optimization procedures employ conventional search algorithms. Examples using 50 to 100 simulations demonstrate accuracy matching 1000 to 10000 Monte Carlo samples     

Nonlinear statistical modeling and yield estimation technique foruse in Monte Carlo simulations [microwave devices and ICs]

A novel nonlinear statistical modeling technique for microwave devices and a new approach to yield estimation for microwave integrated circuits are presented. The statistical modeling methodology is based on a combination of applied multivariate methods with heuristic techniques. These include principal component analysis and factor analysis in conjunction with maximally flat quadratic interpolation and group method of data handling. The proposed modeling approach, when applied to the database of extracted equivalent circuit parameters (ECPs) for a pseudomorphic high electron mobility transistor device, has proven that it can generate simulated ECPs, S-parameters, that are statistically indistinguishable from measured ones. A new yield estimation technique based on a Latin hypercube sampling (LHS) is also demonstrated. The LHS-based simulation is utilized as an alternative to primitive Monte Carlo (PMC) simulation in yield analysis. An equally confident yield estimate based on the LHS method requires only one-fourth of those simulations needed when the PMC technique is used     

基于蒙特卡洛方法的线性混合效应状态空间模型

针对线性混合效应状态空间模型中的状态估计问题,提出了一种新的统计推断方法,在假设总体参数已知及个体随机效应未知的情况下,通过卡尔曼滤波算法与序贯蒙特卡洛算法的结合,实现了对模型中状态的估计。最终在实际模型产生的模拟数据的基础上,通过文中所提算法与卡尔曼滤波算法的实例比较,验证了该方法的有效性。     

基于粒子滤波的OFDM载波频偏和信道联合估计

一种基于粒子滤波(PF)的正交频分复用(OFDM)系统在慢衰落瑞利信道下联合信道估计和载波恢复的新方法被提出。该算法适用于多径时变信道模型以及等效离散时间信道模型。算法引入了在非线性系统参数估计和跟踪领域上十分有效的PF方法,将Kaman滤波与序贯蒙特卡罗采样(SMCS)相结合来估计信道衰落系数以及载波频偏(CFO)的后验概率密度,从而通过计算得到信道的响应函数,并在此基础上,利用MMSE均衡器消除码间串扰(ICI),进行码元估计。仿真结果表明了算法的有效性和优越性。     

A low-complexity sequential Monte Carlo algorithm for blind detection in MIMO systems

In statistical signal processing, the sequential Monte Carlo (SMC) method is powerful and can approach the theoretical optima. However, its computational complexity is usually very high, especially in multiple-input multiple-output (MIMO) systems. This paper presents a new low-complexity SMC (LC-SMC) algorithm for blind detection in MIMO systems, the main idea of which is to shrink the sampling space via channel estimation which is initialized using the first differentially modulated symbol and then updated using the Monte Carlo samples. Since the a posteriori probability of the transmitted symbols can be calculated separately by each transmit antenna, the proposed LC-SMC algorithm is not only computationally efficient, as compared to the original SMC whose complexity grows exponentially with the number of transmit antennas, but also makes blind turbo receiver more feasible for multilevel/phase modulations. Simulation results are presented to demonstrate the effectiveness of the LC-SMC algorithm.     

QAM constellation classification based on statistical sampling for linear distortive channels

The classification method for phase-amplitude-modulated signals transmitted through linear, frequency-selective fading channels is presented. Utilizing a Monte Carlo (MC) sampling technique, QAM constellation classification is based on Gibbs sampling. Constellation classification is achieved along with joint estimation of unknown system parameters, including channel taps and data symbols. When all channel parameters are known, direct steps are proposed for classification and data estimation without altering the general approach. Moreover, a multistage classification algorithm is also presented to improve computational efficiency. The proposed QAM classification methods are shown to perform well on various constellations with different cardinalities, as well as constellations with symbols.     

Accurate and time efficient estimation of the probability of error in bursty channels

A method and a technique for the probability of error estimation in digital channels with memory are developed and demonstrated. The expressions for the mean and variance of a random variable, representing a block of bits transmission in a bursty channel (channel with memory), are derived. The influence of the memory is expressed by a parameter called the memory factor. It is shown that the traditional Monte Carlo method can be applied for the probability of error estimation. In order to control the accuracy and increase the time efficiency of estimation this method is modified and a new method, called the modified Monte Carlo method, is proposed. Based on this modified method a technique of estimation with controlled accuracy is developed and demonstrated using data obtained by simulation. According to this technique the sample size is adjusted in the course of estimating procedure to give an accurate estimate of the probability of error for a minimum required time of estimation. Copyright © 2003 John Wiley & Sons, Ltd.     

Least-square estimation of average power in digital CMOS circuits

The estimation of average-power dissipation of a circuit through exhaustive simulation is impractical due to the large number of primary inputs and their combinations. In this work, two algorithms based on least square estimation are proposed for determining the average power dissipation in complementary metal-oxide-semiconductor (CMOS) circuits. Least square estimation converges faster by attempting to minimize the mean square error value during each iteration. Two statistical approaches namely, the sequential least square (SLS) estimation and the recursive least square estimation are investigated. The proposed methods are distribution independent in terms of the input samples, unbiased and point estimation based. Experimental results presented for the MCNC'91 and the ISCAS'89 benchmark circuits show that the least square estimation algorithms converge faster than other statistical techniques such as the Monte Carlo method and the DIPE     

Blind identification of multipath channels: a parametric subspaceapproach

In this paper, blind identification of single-input multiple-output (SIMO) systems using second-order statistics (SOS) only is considered. Using the assumption of a specular multipath channel, we investigate a parametric variant of the so-called subspace method. Nonparametric subspace-based methods require precise estimation of the model order; overestimation of the model order leads to inconsistent channel estimates. We show that the parametric subspace method gives consistent channel estimates when only an upper bound of the channel order is known. A new algorithm, which exploits parametric information on the channel structure, is presented. A statistical performance analysis of the proposed parametric subspace criterion is presented; limited Monte Carlo experiments show that the proposed algorithm is second-order optimal for a large class of channels     

短采样宽带信号方位估计快速算法

该文针对短采样宽带信号近似最大似然(AML)方位估计计算量大的问题,将马尔可夫蒙特卡罗方法与近似最大似然方位估计相结合,提出一种基于Gibbs抽样的近似最大似然方位估计新方法(Approximated Maximum Likelihood DOA estimator based on Gibbs Sampling, AMLGS)。研究结果表明,AMLGS方法不但保持了原近似最大似然方位估计方法的优良性能,而且显著减小了计算量。把原方法的计算复杂度从O(LK)减少到O(KJNs)。     

A fast Monte Carlo method for evaluating reliability indexes

This paper presents a fast Monte Carlo method, based on importance sampling (IS) distribution, for estimation of reliability indexes in hydroelectric systems. More precisely, it considers the case where the IS distribution comes from the same parametric family as the original (true) one and proposes a simulation-based optimization algorithm for selecting the parameters of the IS in an optimal way. The superiority of this method, relative to the usual Monte Carlo one, is shown for the well-known test problem: IEEE Reliability Test System     

平坦衰落MIMO信道的序列蒙特卡罗双层迭代检测

针对平坦衰落MIMO信道,该文在传统采样检测技术仅依靠时间或空间样本的基础上,提出一种基于序列蒙特卡罗的空时双层迭代采样检测算法。算法将符号的后验概率计算分解为多维的空时双层积分,利用序列蒙特卡罗技术在空间和时间维度上抽取样本,通过加权样本累加得到多维积分的解;同时利用时间样本对信道进行联合估计。仿真结果表明算法可以逼近理想条件下的最优性能,并具有较低的计算复杂度。     

OFDM时变信道的粒子流滤波估计算法

粒子滤波是一种基于序贯重要性采样原理的蒙特卡罗方法,其重采样步骤将导致“粒子贫化”,传统的基于粒子滤波的OFDM时变信道估计算法精度较低、计算复杂度较高。本文从消除“粒子贫化”角度出发,用粒子流的方法取代了重采样。通过建立微分方程实现贝叶斯估计,采用粒子流将粒子平滑移动到状态空间中的后验分布上,实现从先验粒子到后验粒子的更新,提出了一种基于粒子流滤波的OFDM时变信道估计算法。与基于粒子滤波的信道估计方法相比,本方法计算复杂度低,估计精度高,对环境噪声具有较好鲁棒性。      

Partition function estimation of Gibbs random field images usingMonte Carlo simulations

A Monte Carlo simulation technique for estimating the partition function of a general Gibbs random field image is proposed. By expressing the partition function as an expectation, an importance sampling approach for estimating it using Monte Carlo simulations is developed. As expected, the resulting estimators are unbiased and consistent. Computations can be performed iteratively by using simple Monte Carlo algorithms with remarkable success, as demonstrated by simulations. The work concentrates on binary, second-order Gibbs random fields defined on a rectangular lattice. However, the proposed methods can be easily extended to more general Gibbs random fields. Their potential contribution to optimal parameter estimation and hypothesis testing problems for general Gibbs random field images using a likelihood approach is anticipated     

一种基于混合RJMCMC方法的宽带信号DOA估计方法

该文基于被动多传感器阵列,在可逆跳转马尔可夫链蒙特卡罗方法基础上引入随机游走抽样理论,提出一种混合RJMCMC方法,通过将局部采样与全空间采样相结合,可以在更短的时间内得到更好地服从目标分布的随机数,结合信号和噪声的统计特性以及贝叶斯参数估计理论可实现宽带信号源数目和波达方向联合估计。仿真结果证明,该文方法能更为快速、准确地估计出信号源个数和波达方向。     

传输线的随机建模及瞬态响应数值实验分析

该文考虑集成电路制造过程中传输线制造工艺参数随机扰动对传输线传输性能的影响,建立了传输线的随机模型,针对该模型提出了基于蒙特卡洛法的传输线瞬态响应统计分析方法。统计分析中采用精细积分算法求解传输线样本瞬态响应,并对模型输出的正态性进行偏度-峰度检验,给出了最差情况估计。试验结果表明该文提出的传输线随机模型及统计分析方法可以对传输线的传输性能进行有效的评估,对于传输线制造过程的控制及优化有着重要意义。     

基于群Monte Carlo的大气噪声二维模型参数估计

为解决多天线最佳接收下的多维非高斯噪声参数估计问题,提出了基于群蒙特卡洛的大气噪声二维模型参数估计方案,通过联合设计蒙特卡洛马尔科夫链和优化重要性重采样算法,实现噪声模型的全局最优参数估计。针对该算法高强度运算需求,在GPU平台上对核心运算作细粒度并行计算处理并优化设计,使运算速度大幅提升,以满足实时处理要求。仿真实验结果表明,该算法迭代收敛快,精度高,各参数估计相对误差普遍小于0.02,最大相对误差可控制在0.05以内,运算速度较传统计算有大幅度的提高,可充分满足低频通信系统中实时计算的要求。     

Optimized sampling distribution based on nonparametric learning for improved compressive sensing performance

In this work, an optimized nonparametric learning approach for obtaining the data-guided sampling distribution is proposed, where a probability density function (pdf) is learned in a nonparametric manner based on past measurements from similar types of signals. This learned sampling distribution is then used to better optimize the sampling process based on the underlying signal characteristics. A realization of this stochastic learning approach for compressive sensing of imaging data is introduced via a stochastic Monte Carlo optimization strategy to learn a nonparametric sampling distribution based on visual saliency. Experiments were performed using different types of signals such as fluorescence microscopy images and laser range measurements. Results show that the proposed optimized sampling method which is based on nonparametric stochastic learning outperforms significantly the previously proposed approach. The proposed method is achieves higher reconstruction signal to noise ratios at the same compression rates across all tested types of signals.     

Statistical Leakage Estimation of Double Gate FinFET Devices Considering the Width Quantization Property

This paper presents a statistical leakage estimation method for FinFET devices considering the unique width quantization property. Monte Carlo simulations show that the conventional approach underestimates the average leakage current of FinFET devices by as much as 43% while the proposed approach gives a precise estimation with an error less than 5%. Design example on subthreshold circuits shows the effectiveness of the proposed method.     

非频率选择性衰落MIMO信道上基于序列蒙特卡罗的数字调制识别方法

该文针对非频率选择性衰落多输入多输出(MIMO)信道提出了一种基于序列蒙特卡罗(SMC)方法的幅度-相位调制方式识别方法。首先将MIMO系统等效为一个动态状态空间模型,然后利用序列重要性采样和模式转移步骤估计每根发送天线采用的各种可能调制方式的概率,最后利用各个信道上发送符号的不相关性在长为N的观测信道上进行噪声平均。该方法能够在识别数字调制方式的同时估计发送数据符号。其复杂度是信道观测长度、发送天线数、采样大小、调制星座大小的线性函数。仿真结果表明提出的数字调制识别方法在各种调制星座上具有良好的性能。