Blind channel identification and equalization in OFDM-based multiantenna systems

Wireless systems employing multiple antennas at the transmitter and the receiver have been shown to have the potential of achieving extraordinary bit rates. Orthogonal frequency division multiplexing (OFDM) significantly reduces the receiver complexity in multiantenna broadband systems. We introduce an algorithm for blind channel identification and equalization in OFDM-based multiantenna systems. Our approach uses second-order cyclostationary statistics, employs antenna precoding, and yields unique channel estimates (up to a phase rotation for each transmit antenna). Furthermore, it requires only an upper bound on the channel order, it does not impose restrictions on channel zeros, and it exhibits low sensitivity to stationary noise. We present simulation results demonstrating the channel estimator and the corresponding multichannel equalizer performance.     

Blind channel identification and equalization using periodicmodulation precoders: performance analysis

The paper deals with blind identification and equalization of communication channels within the so-called modulation induced cyclostationarity (MIC) framework, where the input symbol stream is modulated by a P periodic precoder with the purpose of inducing cyclostationarity in the transmit sequence. By exploiting the cyclostationarity induced by the periodic precoder, a subspace-based channel identification algorithm that is resilient to the location of channel zeros, channel order overestimation errors, and color of additive stationary noise, is developed. The asymptotic performance of the subspace-based identification approach is analyzed and compared with the asymptotic lower bound provided by the nonlinear cyclic correlation matching approach. Criteria for optimally designing the periodic precoder are also presented. The performance of MMSE-FIR and MMSE-DFE equalizers is quantified for the proposed cyclostationarity-induced framework in terms of the MMSE. Although cyclostationarity-inducing transmitters present several advantages relative to their stationary counterparts from a channel estimation viewpoint, it is shown that from an equalization viewpoint, MIC-based systems exhibit a slightly increased MMSE/BER when compared with the stationary case     

Blind system identification and channel equalization of IIR systemswithout statistical information

A novel approach is proposed to blindly identify an unknown IIR system. The approach is based on faster sampling at the system output and requires neither a priori statistical information on the unknown input nor training signals. The methods presented are linear in the parameters of the unknown system so that many standard recursive algorithms can be readily applied. It is also shown in the paper that under a generic condition, any finite-order IIR system is identifiable, provided the oversampling ratio is appropriately chosen     

利用调制引入循环平稳的CDMA盲信道估计

针对CDMA系统给出了一种新颖的盲辨识方法.它利用了恒模复序列调制产生的循环平稳性,其特色是,在接收机上可以根据循环统计量的支撑的不同,将各个用户的二阶循环统计量分离开来,每个用户形成自己的小规模子空间.采用已成熟的子空间方法,可以识别出不同的用户各自的片率传输信道,以便于均衡和检测.文中给出了自己的信道可辨识性的条件和证明,提出了辨识算法,并对算法进行了模拟.方法简单明了,适合于CDMA上行链路信道.     

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 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     

Unbiased blind adaptive channel identification and equalization

The blind adaptive equalization and identification of communication channels is a problem of important current theoretical and practical concerns. Previously proposed solutions for this problem exploit the diversity induced by sensor arrays or time oversampling, leading to the so-called second-order algebraic/statistical techniques. The prediction error method is one of them, perhaps the most appealing in practice, due to its inherent robustness to ill-defined channel lengths as well as for its simple adaptive implementation. Unfortunately, the performance of prediction error methods is known to be severely limited in noisy environments, which calls for the development of noise (bias) removal techniques. We present a low-cost algorithm that solves this problem and allows the adaptive estimation of unbiased linear predictors in additive noise with arbitrary autocorrelation. This algorithm does not require the knowledge of the noise variance and relies on a new constrained prediction cost function. The technique can be applied in other noisy prediction problems. Global convergence is established analytically. The performance of the denoising technique is evaluated over GSM test channels     

基于周期平稳的盲信噪比估计方法

过采样和成型滤波引入了信号的周期平稳性。基于对信号的周期平稳统计量的分析，提出了一种高斯白噪声信道下的盲信噪比估计方法。对信号的调制方式没有要求，也不需要发送端发送已知数据。蒙特卡罗仿真结果证明，在较宽的信噪比范围内，该方法的性能优于二阶四阶矩方法（M2M4）、信号方差比（SVR）方法等其他经典的盲信噪比估计方法。分析了估计的克拉美-罗下界，作为估计的绝对性能的参照。     

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。     

Blind equalization and identification of nonlinear and IIRsystems-a least squares approach

A deterministic approach to blind nonlinear channel equalization and identification is presented. This approach applies to nonlinear channels that can be approximately linearized by either finite memory, finite-order Volterra filters, or by a finite number of finite memory nonpolynomial nonlinearities. Both the nonlinear equalizers and the linearized channels are identified. This method also applies to blind identification of linear IIR channels. General conditions for existence and uniqueness are discussed, and numerical examples are given     

Blind wideband spatio-temporal filtering based on higher-order cyclostationarity properties

In this paper, a new method for blind minimum mean-square error (MMSE) spatio-temporal signal filtering is proposed. To obtain a blind version of the MMSE filter, the proposed method utilizes the signal selectivity property of the higher order cyclostationary statistics (HOCS) exhibited by the signal of interest. An accurate estimate of the transfer function of the MMSE filter is obtained also in the presence of strong frequency-overlapped interfering signals, which would render standard stationarity-based techniques ineffective. The method is useful when the desired signal cannot be accurately extracted by exploiting its second-order cyclostationarity properties because they are too weak and/or they are shared by the interference and noise signals as well. The performance analysis, which is carried out by computer simulations in the case of quadrature amplitude modulated (QAM)-modulated signals with partially overlapping bands, substantiates the effectiveness of the proposed method.     

用滤波器组构成的传送多路复用器结构实现MMSE盲信道均衡

冗余滤波器组构成的传送多路复用器可以用来对FIR信道进行估计和均衡.本文提出一种在FIR滤波器组框架结构下,首先利用信号的相关矩阵对信道进行估计,然后在此基础上用MMSE准则下设计的FIR均衡器对数据进行均衡的盲算法.该均衡算法的性能要明显好于基于ZF准则的方法,并且在消除ISI的同时可以抑制噪声的影响,从而使系统的输出信噪比达到最优,而增加的复杂度很有限.文中最后在两种典型信道下对所提出的盲信道均衡算法进行了仿真,结果验证了上述性能.     

Blind identification of LTI-ZMNL-LTI nonlinear channel models

A simple method is proposed for blind identification of discrete-time nonlinear models consisting of two linear time invariant (LTI) subsystems separated by a polynomial-type zero memory nonlinearity (ZMNL) of order N (the LTI-ZMNL-LTI model). The linear subsystems are allowed to be of nonminimum phase (NMP), though the first LTI can be completely identified only if it is of minimum phase. With a circularly symmetric Gaussian input, the linear subsystems can be identified using simple cepstral operations on a single 2-D slice of the N+1 th-order polyspectrum of the output signal. The linear subsystem of an LTI-ZMNL model can be identified using only a 1-D moment or polyspectral slice if it is of minimum phase. The ZMNL coefficients are not identified and need not be known. The order N of the nonlinearity can, in principle, be estimated from the output signal. The methods are analytically simple, computationally efficient, and possess noise suppression characteristics. Computer simulations are presented to support the theory     

Blind identification and equalization based on second-orderstatistics: a time domain approach

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel     

基于GPS循环平稳特性的盲波束形成算法改进

在卫星导航自适应阵列抗干扰中,为避免先验信息误差和阵列误差对自适应波束形成性能的影响,可采用基于正交投影和导航信号循环平稳特性的盲波束形成算法(OCAB)。针对GPS信号,采用周期延迟信号处理的OCAB算法(CDOCAB)时,由于数据长度有限,噪声循环自相关函数估计量不为零,因此将影响循环平稳方法的性能,而采用解重扩数据辅助的OCAB算法(DS-OCAB)可以避免噪声估计误差的影响。将两种算法进行仿真比较,仿真结果表明,DS-OCAB算法可在少数据量的情况下保证算法性能不下降,提高算法的稳健性。     

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.     

基于Turbo均衡和信道估计的单通道盲信号恢复算法

定时同步是单通道盲信号接收端处理的难点,提出了一种无需定时同步基于Turbo均衡的单通道盲信号恢复算法。该算法将定时同步偏差等效为符号间干扰（ISI,inter-symbol interference）信道,通过信道估计和Turbo均衡相互反馈软信息来改善源信号信息恢复性能。重点研究了初始盲均衡算法、信道估计算法、混合信号的MMSE均衡算法以及三者间的软信息交互。算法复杂度低、计算量小,适用于高阶调制信号。仿真结果表明,对BPSK、QPSK和8PSK信号,该算法都能得到较好的性能,且对等功率和不等功率信号同样适用。     

Blind channel estimation and equalization in wireless sensor networks based on correlations among sensors

In densely deployed wireless sensor networks, signals of adjacent sensors can be highly cross-correlated. This paper proposes to utilize such a property to develop efficient and robust blind channel identification and equalization algorithms. Blind equalization can be performed with complexity as low as O(N/spl tilde/), where N/spl tilde/ is the length of equalizers. Transmissions can be more power and bandwidth efficient in multipath propagation environment, which is especially important for wideband sensor networks such as those for acoustic location or video surveillance. The cross-correlation property of sensor signals and the finite sample effect are analyzed quantitatively to guide the design of low duty-cycle sensor networks. Simulations demonstrate the superior performance of the proposed method.     

Effective channel order estimation based on combined identification/equalization

Channel order estimation is a critical step in most blind single-input multiple-output (SIMO) channel identification/equalization algorithms. Several methods for estimating either the true channel order or its most significant part (the so-called effective channel order) have been recently proposed, but a solution able to work in practical scenarios (low or moderate signal-to-noise ratios (SNRs) and channels with small leading and/or trailing coefficients) has not been found yet. In this paper, a new criterion for effective channel order detection of SIMO channels is presented. The method is based on the fact that the cost function typically used in blind identification algorithms decreases monotonically with the estimated channel order, whereas for blind equalization algorithms, the cost function increases monotonically. In this paper, it is shown that a straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate SNRs. The proposed method is able to work with small data sets, colored signals, and channels with small head and tail taps, which is a common problem in communication applications. The improvement of the proposed criterion over a number of existing algorithms is demonstrated through simulations.     

Blind FSR-LPTV equalization and interference rejection

We address the problem of synthesizing blind channel identification and equalization methods for digital communications systems, aimed at counteracting the presence of cochannel or adjacent-channel interference. Owing to the presence of the interfering signal, the minimum mean-square error equalizer turns out to be linear periodically time-varying, which is implemented by resorting to its Fourier series representation. Moreover, by exploiting the cyclic conjugate second-order statistics of the channel output, we propose a new weighted subspace-based channel identification method, which is asymptotically immune to the presence of high-level interference. Computer simulation results confirm the effectiveness of the proposed identification/equalization technique.