Unified analysis of a class of blind feedforward symbol timing estimators employing second-order statistics

In this letter, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are casted into a unified framework. The finite sample mean-square error (MSE) expression for this class of estimators is established. Simulation results are also presented to corroborate the analytical results. It is found that the feedforward conditional maximum likelihood (CML) estimator and the square law nonlinearity (SLN) estimator with a properly designed prefilter perform the best and their performances coincide with the asymptotic conditional Cramer-Rao bound (CCRB), which is the performance lower bound for the class of estimators under consideration.     

Parameter Estimation for Correlated MIMO Channels with Frequency-Selective Fading

In this paper, we address the problem of frequency offset and channel gain estimation for frequency-selective multi-input multi-output (MIMO) correlated fading channels. A maximum-likelihood (ML) frequency offset (FO) estimator is proposed by using the Bayesian approach. We show that the proposed FO estimator is efficient and asymptotically optimal. Based on the FO estimate, we derive the linear minimum mean square error (LMMSE) channel estimator and analytically investigate the effect of frequency offset estimation error on the mean square error (MSE) performance of the channel estimator. Finally, the performances of the FO and channel estimation are evaluated by simulation results.     

On Clock Synchronization Algorithms for Wireless Sensor Networks Under Unknown Delay

In this paper, three clock-synchronization algorithms for wireless sensor networks (WSNs) under unknown delay are derived. They include the maximum-likelihood estimator (MLE), a generalization of the estimator of Noh , and a novel low-complexity estimator. Their corresponding performance bounds are derived and compared, and complexities are also analyzed. It is found that the MLE achieves the best performance with the price of high complexity. For the generalized version of the estimator of Noh , although it has low complexity, its performance is degraded with respect to the MLE. On the other hand, the newly proposed estimator achieves the same performance as the MLE, and the complexity is at the same level as that of the generalized version of the estimator of Noh      

Sequential sequence estimation for channels with intersymbolinterference of finite or infinite length

A sequential sequence estimator for channels with finite or infinite intersymbol interference (ISI) is proposed. It consists of a whitened matched filter followed by a sequential algorithm. A key component of a sequential estimator is a suitable metric that accounts for the unequal search depth. This metric is derived for the ISI channel. Computer simulations for three different channels show that the sequential sequence estimator has considerably less computational complexity than a maximum-likelihood sequence estimator using the Viterbi algorithm (MLSE-VA), while still maintaining the optimum estimation performance of the MLSE-VA, a performance not achievable with any reduced-state Viterbi algorithm     

Analysis of LMS-adaptive MLSE equalization on multipath fadingchannels

We consider a practical maximum-likelihood sequence estimation (MLSE) equalizer on multipath fading channels in conjunction with an adaptive channel estimator consisting of a least mean square (LMS) estimator and a linear channel predictor, instead of assuming perfect channel estimates. A new LMS estimator model is proposed which can accurately characterize the statistical behavior of the LMS estimator over multipath fading channels. Based on this model, a new upper-bound on block error rate is derived under the consideration of imperfect channel estimates. Computer simulations verify that our analytical results can correctly predict the real system performance and are applicable over a wide range of the step size parameter of the LMS estimator     

OFDM Carrier Synchronization Based on Time-Domain Channel Estimates

Carrier frequency synchronization is critical to the quality of signal reception in OFDM systems. This paper presents an approximate maximum-likelihood (ML) carrier frequency offset (CFO) estimation scheme based on time-domain channel estimates which retain the CFO information in the form of phase rotation. The proposed ML CFO estimate is investigated under static as well as time-varying fading channels. Statistical properties of the estimator are examined and Cramer-Rao lower bound (CRLB) is derived. Theoretical analysis and numerical simulations show that the proposed CFO estimator renders excellent performance with lower computational complexity. The proposed CFO estimate also has an advantage of allowing for more flexible pilot patterns     

Multicast topology inference from measured end-to-end loss

The use of multicast inference on end-to-end measurement has been proposed as a means to infer network internal characteristics such as packet link loss rate and delay. We propose three types of algorithm that use loss measurements to infer the underlying multicast topology: (i) a grouping estimator that exploits the monotonicity of loss rates with increasing path length; (ii) a maximum-likelihood estimator (MLE); and (iii) a Bayesian estimator. We establish their consistency, compare their complexity and accuracy, and analyze the modes of failure and their asymptotic probabilities     

MMSE estimator for speech enhancement considering the constructive and destructive interference of noise

A new minimum mean square error (MMSE) estimator for discrete cosine transform (DCT) domain speech enhancement considering the two state possibilities of signal and noise DCT coefficients, the constructive and destructive interference, is proposed. Given proper identification of these events are possible, an optimum non-linear MMSE estimator can be derived considering the conditional events. Compared to the previously reported dual-gain multiplicative filters, the proposed estimator demonstrates superior theoretical and experimental performance with respect to mean square error (MSE) improvement and other objective quality measures.     

Frequency estimation of a single complex sinusoid using a generalized Kay estimator

This letter introduces a generalized version of Kay's estimator for the frequency of a single complex sinusoid in complex additive white Gaussian noise. The Kay estimator is a maximum-likelihood (ML) estimator at high signal-to-noise ratio (SNR) based on differential phase measurements with a delay of one symbol interval. In this letter, the corresponding ML estimator with an arbitrary delay in the differential phase measurements is derived. The proposed estimator reduces the variance at low SNR, compared with Kay's original estimator. For certain delay values, explicit expressions for the window function and the corresponding high SNR variance of the proposed generalized Kay (GK) estimator are presented. Furthermore, for some delay values, the window function is nearly uniform and the implementation complexity is reduced, compared with the original Kay estimator. For a delay value of two, we show that the variance at asymptotically high SNR approaches the Cramer-Rao bound as the sequence length tends to infinity. We also explore the effect of exchanging the order of summation and phase extraction for reduced-complexity reasons. The resulting generalized weighted linear predictor estimator and the GK estimator are compared with both autocorrelation-based and periodogram-based estimators in terms of computational complexity, estimation range, and performance at both low and high SNRs.     

An improved conditional maximum-likelihood detector for noncoherent UWB communication

A noncoherent UWB demodulation and detection algorithm has been proposed that circumvents the problems of timing synchronization and channel estimation for impulse based UWB systems. Originally two detection algorithms were offered, a maximum-likelihood sequence detector and a reduced complexity conditional ML detector. We offer an improvement to the conditional ML detector based on the observation that for this algorithm it does not matter whether or not the bursts are detected in forward or reverse order. Our algorithm improves performance by roughly 0.8 dB over the previous conditional ML detector.     

基于准正交空时分组码的高速率码字设计

为了适应通信系统对高速率大容量的需求,设计了码率为2及1.5的非正交空时分组码。在此基础上,采用简化了的条件最大似然译码方法,可以大大降低译码复杂度。仿真结果表明,该编码设计具有接近实际信道容量的特点,并且其误比特率性能优于正交及准正交编码设计。     

Minimax nonparametric classification. II. Model selection foradaptation

For pt.I see ibid., vol.45, no.7, p.2271-84 (1999). We study nonparametric estimation of a conditional probability for classification based on a collection of finite-dimensional models. For the sake of flexibility, different types of models, linear or nonlinear, are allowed as long as each satisfies a dimensionality assumption. We show that with a suitable model selection criterion, the penalized maximum-likelihood estimator has a risk bounded by an index of resolvability expressing a good tradeoff among approximation error, estimation error, and model complexity. The bound does not require any assumption on the target conditional probability and can be used to demonstrate the adaptivity of estimators based on model selection. Examples are given with both splines and neural nets, and problems of high-dimensional estimation are considered. The resulting adaptive estimator is shown to behave optimally or near optimally over Sobolev classes (with unknown orders of interaction and smoothness) and classes of integrable Fourier transform of gradient. In terms of rates of convergence, the performance is the same as if one knew which of them contains the true conditional probability in advance. The corresponding classifier also converges optimally or nearly optimally simultaneously over these classes     

Target Location Estimation in Sensor Networks With Quantized Data

A signal intensity based maximum-likelihood (ML) target location estimator that uses quantized data is proposed for wireless sensor networks (WSNs). The signal intensity received at local sensors is assumed to be inversely proportional to the square of the distance from the target. The ML estimator and its corresponding Crameacuter-Rao lower bound (CRLB) are derived. Simulation results show that this estimator is much more accurate than the heuristic weighted average methods, and it can reach the CRLB even with a relatively small amount of data. In addition, the optimal design method for quantization thresholds, as well as two heuristic design methods, are presented. The heuristic design methods, which require minimum prior information about the system, prove to be very robust under various situations     

Comparison of estimators for frequency offset

A model is proposed for errors that occur in estimating frequency offset between oscillators driving two clocks that are synchronized with a digital control loop. Three specific estimators are analyzed. One, the maximum-likelihood estimator, yields a minimum-mean-square estimate for Gaussian errors. A second offers convenient implementation and provides performance close to that of the maximum-likelihood estimator     

Low-Complexity Channel Estimation Using Short-Point FFT/IFFT for an Iterative Receiver in an LDPC-Coded OFDM System

A frequency-domain channel expanding technique (FD-CET) is proposed for channel estimation in an iterative receiver of a low-density parity-check coded orthogonal frequency division multiplexing system. The FD-CET can expand a subset of the frequency-domain channel gains to all of them in a short-point FFT/IFFT format. This technique is applied to estimate the channel gains utilizing the pilots and information from the decoder in an iterative manner. It is a novel low complexity channel estimation method. Numerical results demonstrate that the proposed channel estimator approaches a maximum-likelihood performance within only a few iterations under multi-path channels.     

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     

一种新的每符号两个样点的前向定时估计算法

针对线性调制信号,提出一种新的每符号两个样点的非数据辅助的前向符号定时估计算法。算法的基本思想是：首先,将两倍符号速率的样点信号通过一个特定的抗混叠滤波器,使得输出信号经非线性变换后,在符号速率处不再混叠;然后,利用传统的平方律非线性等算法估计定时相位偏差。文中详细描述了算法的推导过程。最后的仿真数据表明,在计算复杂度相当的情况下,新算法的估计性能优于其他两种算法,并且对于信号及应用环境的适应性更强。     

A Maximum-Likelihood Sequence Estimator with Decision-Feedback Equalization

A decision-feedback equalizer (DFE) is proposed as a prefilter which limits the complexity of a maximum-likelihood sequence estimator (MLSE) implemented by the Viterbi algorithm (VA) for channels having a long impulse response. By imbedding a DFE into the structure of the MLSE, the overall impulse response of the system is truncated to a short duration. With this practical receiver, a compromise may be made between performance and complexity by properly choosing the duration of a desired impulse response. A technique is also developed to estimate the performance of the receiver numerically, taking into account the effect of incorrect decision feedback on the VA. Analysis and computer simulation over a single-pole channel show that the proposed scheme can reduce the complexity of the MLSE while retaining much of its performance advantages.     

Training-based time-delay estimation for CPM signals over time-selective fading channels

In this paper, we consider training-based symbol timing synchronization for continuous phase modulation over channels subject to flat, Rayleigh fading. A high signal-to-noise-ratio maximum-likelihood estimator based on a simplified channel correlation model is derived. The main objective is to reduce algorithm complexity to a single-dimensional search on the delay parameter, similar to that of the static-channel (slow fading) estimator. The asymptotic behavior of the algorithm is evaluated, and comparisons are made with the Cramer-Rao lower bound for the problem. Simulation results demonstrate highly improved performance over the conventional, static-channel delay estimator.     

Joint signal detection and parameter estimation in multiusercommunications

The problem of simultaneously detecting the information bits and estimating signal amplitudes and phases in a K-user asynchronous direct-sequence spread-spectrum multiple-access communication system is addressed. The joint maximum-likelihood (ML) estimator has a computational complexity that is exponential in the total number of bits transmitted and thus does not represent a practical solution to the problem. An estimator that combines a suboptimum tree-search algorithm with a recursive least-squares estimator of complex signal amplitude is considered. The complexity of this estimator is O(K²) computations per decoded bit, and its performance is very close to that of the joint ML receiver. This receiver has the advantage that the transmitted signal powers and phases are extracted from the received signal in an adaptive fashion without using a test sequence