基于有色信源MIMO-FIR信道的盲均衡准则

针对有色信源具有的统计特性,分析了该类信源的二阶与四阶相关统计量在时间和空间上所呈现的规律,提出了一种在对信源要求比较弱的条件下MIMO-FIR信道的新盲均衡准则,并构建了基于该准则的盲均衡算法。通过计算机仿真验证了提出算法的有效性。     

Blind equalization/detection for OFDM signals overfrequency-selective channels

A novel equalization/detection algorithm for orthogonal frequency division multiplexing (OFDM) signals transmitted over frequency-selective channels is introduced and investigated. The algorithm stems from the recognition that the Fourier transform processing inherent in OFDM turns a single wideband frequency-selective channel into a set of correlated narrowband frequency-flat fading channels. This suggests that sequence detection techniques, such as those discussed by Vitetta et al. (see IEEE Trans. Commun., vol.43, p.2750-8, 1995, IEEE Trans. Commun., vol.43, pt.II, p.1256-9, 1995, and Proc. IEEE Commun. Theory Mini-Conf (Globecom '96), London, UK, p.153-7, 1996), for time-selective flat-fading channels, can be also profitably utilized for joint equalization and decoding of OFDM signals in the frequency domain. Simulation results show that the proposed detection strategy, implemented via a standard Viterbi algorithm, provides improved performance over differential detection, with a moderate increase in receiver complexity and without requiring the periodic transmission of training blocks     

Blind source separation/channel equalization of nonlinear channels with binary inputs

In this paper, we consider blind source separation/channel equalization problems for an unknown nonlinear channel. By using the property of the binary alphabets, we show that blind nonlinear source separation/channel equalization problems can be easily solved by existing linear methods.     

Blind equalization of nonlinear channels from second-orderstatistics

This paper addresses the blind equalization problem for single-input multiple-output nonlinear channels, based on the second-order statistics (SOS) of the received signal. We consider the class of "linear in the parameters" channels, which can be seen as multiple-input systems in which the additional inputs are nonlinear functions of the signal of interest. These models include (but are not limited to) polynomial approximations of nonlinear systems. Although any SOS-based method can only identify the channel to within a mixing matrix (at best), sufficient conditions are given to ensure that the ambiguity is at a level that still allows for the computation of linear FIR equalizers from the received signal SOS, should such equalizers exist. These conditions involve only statistical characteristics of the input signal and the channel nonlinearities and can therefore be checked a priori. Based on these conditions, blind algorithms are developed for the computation of the linear equalizers. Simulation results show that these algorithms compare favorably with previous deterministic methods     

Blind channel equalization using chirp modulating signals

This paper addresses the problem of blind channel equalization in the context of digital communications. Recent results have shown that certain operations applied to the source signal at the transmitter help in the blind identification and equalization of the channel at the receiver. In this paper, the baseband data signal is multiplied with a chirp sequence. Exploiting certain structural properties arising from this operation, a batch-type algorithm is obtained for calculating the equalizer's coefficients. Conditions on the chirp sequence parameters are obtained that guarantee an equalization solution. A low-complexity adaptive algorithm is also proposed. Finally, extensive simulations, and comparisons with other well-known blind techniques, illustrate the excellent performance of this algorithm.     

Blind decision feedback equalization of time-varying channels withDPSK inputs

This paper presents a new method to the blind equalization of a class of linear time-varying mobile channels for differentially encoded channel input signals. Through reparameterization, the equivalent discrete system can be modeled as a single-input multiple-output system. Unlike some existing methods, this new algorithm does not require prior knowledge on the cyclic frequency of the channel response. The algorithm is simple and relies only on a decision feedback structure and the differential encoding nature of the channel input signals. The convergence analysis of the decision feedback system is presented along with simulation examples     

联合多脉冲检测的PSWF正交调制信号盲自适应均衡

卫星通信信道的复杂时变特性,使基于椭圆球面波函数(Prolate Spheroidal Wave Function,PSWF)的正交调制信号脉冲组的正交性受到破坏,已有均衡方法未能充分利用多脉冲干扰中的有用信息,效果有限。针对该问题,结合信道均衡与多脉冲检测各自的优势,提出一种联合多脉冲检测的PSWF时域正交调制信号自适应均衡方法,利用多脉冲检测消除脉冲间干扰的能力,降低均衡模块的阶数及算法难度;同时,利用均衡模块对信道的部分补偿作用,为多脉冲检测改善信道环境。在相同信道条件下,所提方法获得同等量级误比特率所需信噪比较自适应判决反馈均衡算法降低约2 dB。     

Blind estimation and equalization of MIMO channels via multidelaywhitening

Blind channel estimation and blind minimum mean square error (MMSE) equalization of multiple-input multiple-output (MIMO) communications channels arising in multiuser systems is considered, using primarily the second-order statistics of the data. The basis of the approach is the design of multiple zero-forcing equalizers that whiten the noise-free data at multiple delays. In the past such an approach has been considered using just one zero-forcing equalizer at zero-delay. Infinite impulse response (IIR) channels are allowed. Moreover, the multichannel transfer function need not be column-reduced. The proposed approach also works when the “subchannel” transfer functions have common zeros so long as the common zeros are minimum-phase zeros. The channel length or model orders need not be known. Using second-order statistics, the sources are recovered up to a unitary mixing matrix, and are further “unmixed” using higher order statistics of the data. Two illustrative simulation examples are provided where the proposed method is compared with its predecessors and an existing method to show its efficacy     

Blind equalization and channel estimation with partial responseinput signals

The problem of blind equalization and channel estimation for partial-response signals (PRS) is considered. Three approaches are investigated; two of them exploit the prior knowledge of the PR code, and the third approach does not. We propose a constrained optimization approach involving a quadratic cumulant matching criterion where the coding structure of the transmitted signal is assumed to be a priori known. The other two approaches exploit the Godard blind equalizer. Computer simulation results using 16-QAM signals, and duobinary and modified duobinary PR codes, show that the constrained optimization approach yields the best performance as measured via the probability of symbol detection error     

Blind equalization of constant modulus signals using an adaptiveobserver approach

The paper addresses the problem of blind equalization of constant modulus signals which are degraded by frequency selective multipath propagation and additive white noise. An adaptive observer is used to update the weights of an FIR equalizer in order to restore the signal's constant modulus property. The observer gain is selected using fake algebraic Riccati methods in order to guarantee local stability. The performance of this method is compared to the constant modulus algorithm for simulated FM-FDM signals and exhibits significantly better convergence properties, particularly for heavy-tailed noise     

基于盲源分离的水声信道盲均衡处理方法

提出了一种基于盲源分离的水声信道讯均衡处理方法,通过对接收信号过采样构成源信号,采用了基于信息最大化原理(Infomax)在线分离算法进行了水声信道的盲均衡,并研究了时变水声信道条件下算法的均衡情况,仿真实验结果表明,该处理方法对多径水声信道具有较好的均衡效果,同时不受最小相位的条件限制。     

Linear bispectrum of signals and identification of nonminimum phaseFIR systems driven by colored input

The identification of non-minimum-phase finite-impulse-response (FIR) systems driven by third-order stationary colored signals that are not linear processes is addressed. Modeling the linear part of the bispectrum of a signal is discussed. The bispectrum of a signal is decomposed into two multiplicative factors. The linear bispectrum is defined as the factor of the bispectrum that can exactly be modeled using a third-order white-noise-driven linear shift-invariant (LSI) system. The linear bispectrum of the output of the unknown LSI system is represented using an ARMA (autoregressive moving average) model, where the MA parameters correspond to the unknown FIR system impulse response coefficients, and the AR parameters model the linear bispectrum of the input signal. An algorithm for identifying the MA and AR parameters is given. How the proposed method is different from fitting an ARMA model directly to the bicumulants or the bispectrum of the system output is discussed. The method is applied to blur identification     

Blind equalization of nonminimum phase channels: higher ordercumulant based algorithm

Higher order cumulant analysis is applied to the blind equalization of linear time-invariant (LTI) nonminimum-phase channels. The channel model is moving-average based. To identify the moving average parameters of channels, a higher-order cumulant fitting approach is adopted in which a novel relay algorithm is proposed to obtain the global solution. In addition, the technique incorporates model order determination. The transmitted data are considered as independently identically distributed random variables over some discrete finite set (e.g., set {±1, ±3}). A transformation scheme is suggested so that third-order cumulant analysis can be applied to this type of data. Simulation examples verify the feasibility and potential of the algorithm. Performance is compared with that of the noncumulant-based Sato scheme in terms of the steady state MSE and convergence rate     

Adaptive equalization of CPM signals transmitted over fast Rayleighflat-fading channels

A new algorithm for adapting the coefficients of an equalizer for continuous phase modulated data signals in a flat-fading environment is presented. The cost function to optimize is based on the maximum likelihood sequence estimation index for such signals and channel conditions. It is shown that this equalizer algorithm, called the maximum likelihood equalizer, involves the iterative computation of one of the eigenvectors of a matrix. An implementation is proposed, which combines iterative estimation procedures for QR decomposition, matrix eigenvalue tracking and channel prediction error. Simulation results are presented that demonstrate the ability of the algorithm to equalize the channel filtering effects in a fast fading environment, without requiring phase coherent carrier recovery     

应用萤火虫算法的混沌信号盲源分离

针对现有的独立成分分析法分离混合混沌信号精度不理想的问题,提出了一种新的混沌信号盲分离方法。该方法以求解最优解混矩阵为目标,利用峭度构造目标函数,将混沌信号的盲源分离转化为一个优化问题,并用萤火虫算法求解。同时,通过预白化和正交矩阵的参数化表示降低优化问题的维数,能有效提高分离精度。仿真结果表明,无论是处理混合的混沌映射信号还是混合的混沌流信号,该方法都能快速收敛,并且其分离精度在各项实验中都优于独立成分分析法等现有的盲源分离方法。     

Blind detection of CPM signals transmitted over frequency-flatfading channels

We propose a novel detection scheme for continuous-phase modulation (CPM) signals transmitted over frequency-flat fading channels. Its most significant feature is that it operates without statistical information on the fading channel, and for this reason it is nicknamed “blind detector.” Its error-rate performance is assessed with minimum shift keying (MSK) and Gaussian MSK (GMSK) schemes and compared with the performance of other decoders     

Blind channel equalization with colored sources based onsecond-order statistics: a linear prediction approach

We consider the blind equalization and estimation of single-user, multichannel models from the second-order statistics of the channel output when the channel input statistics are colored but known. By exploiting certain results from linear prediction theory, we generalize the algorithm of Tong et al. (1994), which was derived under the assumption of a white transmitted sequence. In particular, we show that all one needs to estimate the channel to within an unitary scaling constant, and thus to find its equalizers, is (a) that a standard channel matrix have full column rank, and (b) a vector of the input signal and its delays have positive definite lag zero autocorrelation. An algorithm is provided to determine the equalizer under these conditions. We argue that because this algorithm makes explicit use of the input statistics, the equalizers thus obtained should outperform those obtained by other methods that neither require, nor exploit, the knowledge of the input statistics. Simulation results are provided to verify this fact     

Blind equalization of IIR channels using hidden Markov models andextended least squares

In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization     

Blind equalization of dispersive fast fading Ricean channels via the EMV algorithm

A blind maximum likelihood equalization method is proposed for frequency selective fast fading Ricean channels. This method employs the expectation-maximization Viterbi algorithm (EMVA) developed in for blind channel estimation and signal detection. Since the Viterbi algorithm (VA) is used to execute the E-phase of an expectation-maximization (EM) iteration, it requires that the observed sequence can be modelled as a finite-state hidden Markov process. We develop a hidden Markov model for frequency selective fast fading Ricean channels, so that the observed process can be viewed as the noisy output of a finite state machine (FSM), to which the VA is applicable. The EMVA is then employed to obtain a blind maximum likelihood estimate of the specular part of the channel and, for one special case, of a noise parameter measuring the total power of the additive and multiplicative channel noise components. Simulation results are presented which show that the EMVA achieves an accurate estimate of the channel specular part and has an error rate performance close to that of the maximum likelihood detector based on true parameters for the given FSM model.