SOQPSK-TG信号的EM半盲载波频偏估计

针对成形偏移四相相移键控-TG(Shaped Offset Quadrature Phase Shift Keying-Telemetry Group version, SOQPSK-TG)信号在训练序列长度受限时频偏估计精度较低的问题,利用双二进制分解(Doubinary Decomposition, DBD)原理提出基于期望最大化(Expectation Maximization, EM)的SOQPSK半盲载波频偏(Carrier Frequency Offset, CFO)估计算法。为了确保EM算法收敛到预期性能范围,使用基于非线性四次方码元定时估计算法的非数据辅助频偏估计方法优化了EM算法初始点选择。仿真实验结果表明,该算法相比于使用训练序列进行数据辅助估计的方法,在不增加辅助数据数量的前提下能够进一步提升CFO估计的精度,并在较高信噪比下拥有接近序列总长度所对应的克拉美罗界(Cramér-Rao Bound, CRB)的优秀性能。     

A reduced-complexity online state sequence and parameter estimatorfor superimposed convolutional coded signals

This paper develops a reduced-complexity online state sequence and parameter estimator for superimposed convolutional coded signals. Joint state sequence and parameter estimation is achieved by iteratively estimating the state sequence via a variable reduced-complexity Viterbi algorithm (VRCVA) and the model parameters via a recursive expectation maximization (EM) approach. The VRCVA is developed from a fixed reduced-complexity Viterbi algorithm (FRCVA). The FRCVA is a special case of the delayed decision-feedback sequence estimation (DDFSE) algorithm. The performance of online versions of the FRCVA, VRCVA, and the standard Viterbi algorithm (VA) are compared when they are used to estimate the state sequence as part of the reduced-complexity online state sequence and parameter estimator     

The expectation-maximization algorithm for symbol unsynchronizedsequence detection

The expectation-maximization (EM) algorithm for maximizing likelihood functions, combined with the Viterbi algorithm, is applied to the problem of sequence detection when symbol timing information is not present. Although the EM algorithm is noncausal, results obtained using the algorithm on the problem of nonsynchronized sequence detection indicate that it converges most of the time in three iterations, making it both of theoretical and of practical interest. A practical algorithm based on the EM algorithm is introduced. It reduces the computational burden and improves performance by making use of timing estimates in previous observation windows     

Iterative EM receiver for space-time coded systems in MIMO frequency-selective fading channels with channel gain and order estimation

An iterative receiver for a space-time trellis coded system in frequency-selective fading channel is proposed. It performs channel gain estimation and sequence detection by using the expectation-maximization (EM) algorithm. Channel order estimation is included in the receiver to avoid unnecessary trellis computations by using the conditional model order estimator (CME). In addition, three modifications to the original CME criterion are proposed to improve the estimation accuracy. Simulation results show that the proposed receiver has a slight degradation in frame error rate performance to the known channel maximum likelihood receiver. Moreover, it outperforms the conventional fixed long-tap length EM receiver with a lesser complexity. Furthermore, the proposed modifications to the CME criterion improve the channel order estimation accuracy, thus minimizing unnecessary computations.     

Recursive algorithms for identification of impulse noise channels

The Class A Middleton model is a widely accepted statistical-physical parameteric model for impulsive interference superimposed on a Gaussian background. In the present work, a recursive decision-directed estimator for online identification of the parameters of the Class A model is proposed. This estimator is based on an adaptive Bayesian classification of each of a sequence of Class A envelope samples as an impulsive sample or as a background sample. As each sample is so classified, recursive updates of the estimates of the second moment of the background component of the interference envelope density, the second moment of the impulsive component of the interference envelope density, and the probability with which the impulsive component occurs, are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly, since closed-form expressions for the parameters exist in terms of these quantities. The performance characteristics of this algorithm are investigated and an appropriately modified version is found to yield a recursive algorithm with excellent global performance     

Time-delay estimation for filtered Poisson processes using anEM-type algorithm

We develop a modified EM algorithm to estimate a nonrandom time shift parameter of an intensity associated with an inhomogeneous Poisson process N_t, whose points are only partially observed as a noise-contaminated output X of a linear time-invariant filter excited by a train of delta functions, a filtered Poisson process. The exact EM algorithm for computing the maximum likelihood time shift estimate generates a sequence of estimates each of which attempt to maximize a measure of similarity between the assumed shifted intensity and the conditional mean estimate of the Poisson increment dN_t. We modify the EM algorithm by using a linear approximation to this conditional mean estimate. The asymptotic performance of the modified EM algorithm is investigated by an asymptotic estimator consistency analysis. We present simulation results that show that the linearized EM algorithm converges rapidly and achieves an improvement over conventional time-delay estimation methods, such as linear matched filtering and leading edge thresholding. In these simulations our algorithm gives estimates of time delay whose mean square error virtually achieves the CR lower bound for high count rates     

MIMO-OFDM系统中一种改进的Bayesian EM信道估计器

该文提出了MIMO-OFDM系统中一种改进的Bayesian EM信道估计器。利用软球形译码器的搜索列表和解码器反馈的先验信息对传统EM信道估计中的软信息近似处理进行了修正,计算了更为准确的软符号后验概率分布以及一阶、二阶统计量。基于初始估计得到的信道先验信息,设计了新的考虑软符号后验互相关的时域信道冲激响应最大后验概率(MAP)估计算法。仿真试验结果表明:该算法和传统EM信道估计算法相比具有更低的误码率和更小的估计均方误差值。     

An improvement to the interacting multiple model (IMM) algorithm

Computing the optimal conditional mean state estimate for a jump Markov linear system requires exponential complexity, and hence, practical filtering algorithms are necessarily suboptimal. In the target tracking literature, suboptimal multiple-model filtering algorithms, such as the interacting multiple model (IMM) method and generalized pseudo-Bayesian (GPB) schemes, are widely used for state estimation of such systems. We derive a reweighted interacting multiple model algorithm. Although the IMM algorithm is an approximation of the conditional mean state estimator, our algorithm is a recursive implementation of a maximum a posteriori (MAP) state sequence estimator. This MAP estimator is an instance of a previous version of the EM algorithm known as the alternating expectation conditional maximization (AECM) algorithm. Computer simulations indicate that the proposed reweighted IMM algorithm is a competitive alternative to the popular IMM algorithm and GPB methods     

Joint carrier frequency synchronization and channel estimation for OFDM systems via the EM algorithm

A joint carrier frequency synchronization and channel estimation scheme is proposed for orthogonal frequency-division multiplexing (OFDM) system. In the proposed scheme, carrier frequency synchronization and channel estimation are performed iteratively via the expectation-maximization (EM) algorithm using an OFDM preamble symbol. Moreover, we analytically investigate the effect of frequency offset error on the mean square error (MSE) performance of channel estimator. Simulation results present that the proposed scheme achieves almost ideal performance for both channel and frequency offset estimation.     

Fast EM-like methods for maximum "a posteriori" estimates in emission tomography

The maximum-likelihood (ML) approach in emission tomography provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. The expectation-maximization (EM) algorithm is an iterative algorithm for maximizing the Poisson likelihood in emission computed tomography that became very popular for solving the ML problem because of its attractive theoretical and practical properties. Recently, (Browne and DePierro, 1996 and Hudson and Larkin, 1994) block sequential versions of the EM algorithm that take advantage of the scanner's geometry have been proposed in order to accelerate its convergence. In Hudson and Larkin, 1994, the ordered subsets EM (OS-EM) method was applied to the ML problem and a modification (OS-GP) to the maximum a posteriori (MAP) regularized approach without showing convergence. In Browne and DePierro, 1996, we presented a relaxed version of OS-EM (RAMLA) that converges to an ML solution. In this paper, we present an extension of RAMLA for MAP reconstruction. We show that, if the sequence generated by this method converges, then it must converge to the true MAP solution. Experimental evidence of this convergence is also shown. To illustrate this behavior we apply the algorithm to positron emission tomography simulated data comparing its performance to OS-GP.     

Diversity combining for DS/SS systems with time-varying, correlated fading branches

Diversity combining of multiple time varying and correlated fading branches is investigated for direct-sequence spread-spectrum systems. The correlated branches are modeled and estimated jointly as a vector autoregressive (VAR) process. The joint estimation is shown to provide performance gain over separate estimation of the fading branches. The parameter matrices of the VAR model are estimated via the method of expectation maximization (EM) with two algorithms. The first algorithm, using results from Kalman smoothing, provides a closed-form solution to the maximization problem in the iterative EM procedure. However, the iterative EM-Kalman algorithm operates repeatedly on a batch of training data and results in large storage requirements and long processing delays. To overcome these disadvantages, a new algorithm with only forward-time recursions is proposed that approximates the iterative EM solution and efficiently adapts to slowly changing Doppler spreads. As a result, the new algorithm significantly reduces memory and training sequence requirements. Through computer simulations, a near ideal bit-error rate performance is found for both algorithms, and the efficacy of the new adaptive algorithm for channels with changing Doppler spreads is demonstrated.     

Low-complexity SAGE-based MMSE channel estimation for CoMP multi-user systems

Coordinated multiple point(CoMP) transmission/reception has been investigated recently as a promising technology to increase the cell-edge user performance of long term evolution-advanced(LTE-A),and channel estimation is a crucial technology for CoMP systems.In this paper,we consider a reduced-complexity minimum mean square error(MMSE) channel estimator for CoMP systems.The estimator uses space alternating generalized expectation maximization(EM)(SAGE) algorithm to avoid the inverse operation of the joint MMSE estimator.In the proposed scheme,the base stations(BSs) in the CoMP system estimate the channels of all the coordinated users serially and iteratively.We derive the SAGE-based estimators and analyze complexity.Simulation results verify that the performance of the proposed algorithm is close to the joint MMSE estimation algorithm while reducing the complexity greatly.     

A recursive algorithm for computing Cramer-Rao-type bounds onestimator covariance

We give a recursive algorithm to calculate submatrices of the Cramer-Rao (CR) matrix bound on the covariance of any unbiased estimator of a vector parameter &thetas;_. Our algorithm computes a sequence of lower bounds that converges monotonically to the CR bound with exponential speed of convergence. The recursive algorithm uses an invertible “splitting matrix” to successively approximate the inverse Fisher information matrix. We present a statistical approach to selecting the splitting matrix based on a “complete-data-incomplete-data” formulation similar to that of the well-known EM parameter estimation algorithm. As a concrete illustration we consider image reconstruction from projections for emission computed tomography     

Joint channel and symbol estimation by oblique projections

The problem of simultaneous blind channel and symbol estimation of a single-input multiple-output (SIMO) communication channel is considered. It is shown that the outer product of the channel vector and the channel input sequence can be obtained by a linear estimator that has the finite sample convergence property. Furthermore, this estimator can be obtained by the use of oblique projections. An order detection algorithm that avoids the use of subjective thresholding is also proposed. Applications to multiuser detection are also considered     

SNR estimation in time-varying fading channels

Signal-to-noise ratio (SNR) estimation is considered for phase-shift keying communication systems in time-varying fading channels. Both data-aided (DA) estimation and nondata-aided (NDA) estimation are addressed. The time-varying fading channel is modeled as a polynomial-in-time. Inherent estimation accuracy limitations are examined via the Cramer-Rao lower bound, where it is shown that the effect of the channel's time variation on SNR estimation is negligible. A novel maximum-likelihood (ML) SNR estimator is derived for the time-varying channel model. In DA scenarios, where the estimator has a simple closed-form solution, the exact performance is evaluated both with correct and incorrect (i.e., mismatched) polynomial order. In NDA estimation, the unknown data symbols are modeled as random, and the marginal likelihood is used. The expectation-maximization algorithm is proposed to iteratively maximize this likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.     

OFDM channel estimation with dispersive fading channels

In this paper, we studied the channel estimation problem for the orthogonal frequency division multiplexing (OFDM) system when the statistics of the multi-path fading channel is not known or partial known. A channel estimation approach based on polynomial approximation of the channel response is proposed. The pilot symbols are periodically inserted the channel responses for entire OFDM data sequence for exploiting channel correlation in both time and frequency domain, which is obtained from a time-variant frequency-selective Rayleigh fading channel model. Simulation shows that the method is robust to different channel statistics. Moreover, a window dimension adaptation algorithm is proposed to adapt the channel estimator to the channel statistics which further improves the robustness of the system.     

Blind frequency-offset estimator for OFDM systems transmitting constant-modulus symbols

We address the problem of carrier frequency offset (CFO) synchronization in OFDM communications systems in the context of frequency-selective fading channels. We consider the case where the transmitted symbols have constant modulus, i.e., PSK constellations. A novel blind CFO estimation algorithm is developed. The new algorithm is shown to greatly outperform a previously published blind technique that exploits the fact that practical OFDM systems are not fully loaded. Further, the proposed algorithm is consistent even when the system is fully loaded. Finally, the proposed CFO estimator is obtained via a one-dimensional search, the same as with the existing virtual subcarrier-based estimator, but achieves a substantial gain in performance (10-dB SNR or one order of magnitude in CFO MSE).     

New iterative closest point algorithm for isotropic scaling registration of point sets with noise

This paper proposes a new probability iterative closest point (ICP) approach with bounded scale based on expectation maximization (EM) estimation for isotropic scaling registration of point sets with noise. The bounded-scale ICP algorithm can handle the case with different scales, but it could not effectively yield the alignment of point sets with noise. Aiming at improving registration precision, a Gaussian probability model is integrated into the bounded-scale registration problem, which is solved by the proposed method. This new method can be solved by the E-step and M-step. In the E-step, the one-to-one correspondence is built up between two point sets. In the M-step, the scale transformation including the rotation matrix, translation vector and scale factor is computed by singular value decomposition (SVD) method and the properties of parabola. Then, the Gaussian model is updated via the distance and variance between transformed point sets. Experimental results demonstrate the proposed method improves the performance significantly with high precision and fast speed.     

Adaptive Iterative Detectors for Phase-Uncertain Channels via Variational Bounding

The problem of iterative detection/decoding of data symbols transmitted over an additive white Gaussian noise (AWGN) channel in the presence of phase uncertainty is addressed in this paper. By modelling the phase uncertainty either as an unknown deterministic variable/process or random variable/ process with a known a priori probability density function, a number of non-Bayesian and Bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem. In this paper, a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique. Especially, applying the generic variational Bayesian (VB) framework, efficient iterative joint estimation and detection/decoding schemes are derived for the constant phase model as well as for the dynamic phase model. In addition, the relation of the VB-based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation-maximization (EM) algorithm is provided. Performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors. Furthermore, an incremental scheduling of the VB (or EM) algorithm is shown to reduce the overall complexity of the receiver.     

放大转发双向中继系统中降低总功率的波束成形设计

本文研究了多天线放大转发双向中继系统中在满足源节点信噪比要求条件下最小化系统总功率的波束设计问题。该问题是非凸优化问题,为了有效求解该问题,采用分层优化方法将原问题分解成发送波束成形向量优化、接收波束成形向量优化和中继波束成形矩阵优化三类子问题。发送/接收波束成形向量通过求解Rayleigh商最小化问题来获得。中继波束成形矩阵优化问题通过半正定松弛方法转化成半正定优化问题来求解。在求解这三类优化问题的基础上,提出了一种迭代波束成形算法,并采用单调有界序列定理证明了所提算法的收敛性。计算机仿真表明:所提算法经过若干次迭代即可收敛到稳定点;相比于已有算法,本文算法能显著降低系统总功率。