Blind channel identification and equalization withmodulation-induced cyclostationarity

Recent results have pointed out the importance of inducing cyclostationarity at the transmitter for blind identification and equalization of communication channels. This paper addresses blind channel identification and equalization relying on the modulation-induced cyclostationarity, without introducing redundancy at the transmitter. It is shown that single-input single-output channels can be identified uniquely from output second-order cyclic statistics, irrespective of the location of channel zeros, color of additive stationary noise, or channel order overestimation errors, provided that the period of modulation-induced cyclostationarity is greater than half the channel length. Linear, closed-form, nonlinear correlation matching, and subspace-based approaches are developed for channel estimation and are tested using simulations. Necessary and sufficient blind channel identifiability conditions are presented. A Wiener cyclic equalizer is also proposed     

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     

ARMA system identification based on second-order cyclostationarity

Previous work has presented novel techniques that exploit cyclostationarity for channel identification in data communication systems. The present authors investigate the identifiability of linear time-invariant (LTI) ARMA systems based on second-order cyclic statistics. They present a parametric and a nonparametric method. The parametric method directly identifies the zeros and poles of ARMA channels with a mixed phase. The nonparametric method estimates the channel phase based on the cyclic spectra alone. They analyze the phase estimation error of the nonparametric method for finite dimensional ARMA channels. For specific, finite dimensional ARMA channels, an improved method is given, which combines the parametric method with the nonparametric method. Computer simulation illustrates the effectiveness of the methods in identifying ARMA system impulse responses     

Exploiting input cyclostationarity for blind channel identificationin OFDM systems

Transmitter-induced cyclostationarity has been explored previously as an alternative to fractional sampling and antenna array methods for blind identification of FIR communication channels. An interesting application of these ideas is in OFDM systems, which induce cyclostationarity due to the cyclic prefix. We develop a novel subspace approach for blind channel identification using cyclic correlations at the OFDM receiver. Even channels with equispaced unit circle zeros are identifiable in the presence of any nonzero length cyclic prefix with adequate block length. Simulations of the proposed channel estimator along with its performance in OFDM systems combined with impulse response shortening and Reed-Solomon coding are presented     

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     

Transmitter induced cyclostationarity for blind channelequalization

Fractional sampling has received considerable interest recently as a means of developing blind equalization techniques without resorting to higher order statistics. Instead, cyclostationarity introduced at the receiver by fractional sampling is exploited. We show that simpler solutions are possible if cyclostationarity is introduced at the transmitter instead of the receiver. We propose specific coding and interleaving strategies at the transmitter that induce cyclostationarity and facilitate the equalization task. Novel batch and adaptive equalization algorithms are derived that make no assumptions on the channel zeros locations. Subspace methods are also proposed and, in the absence of noise, guarantee perfect estimation from finite data. Synchronization issues and bandwidth considerations are briefly discussed, and simulation examples are presented     

Equalization and semi-blind channel estimation for space-time block coded signals over a frequency-selective fading channel

In this paper, we investigate the equalization and channel identification for space-time block coded signals over a frequency-selective multiple-input multiple-output (MIMO) channel. The equalization has been considered by taking into account the cyclostationarity of space-time block coded signals. The minimum mean square error (MMSE) solutions have been derived for the linear and decision feedback (DF) equalizers. The channel estimation is required for the equalization. With known symbols (as pilot symbols), MIMO channels can be estimated. In addition, due to the redundancy induced by space-time block code, it is possible to identify MIMO channels blindly using the subspace method. We consider both blind and semi-blind channel estimation for MIMO channels. It is shown that the semi-blind channel estimate has fewer estimation errors, and it results in less (bit error rate) performance degradation of the MMSE linear and DF equalizers.     

基于循环累量的多用户信道盲辨识

本文针对多用户系统提出了一种新颖的盲辨识方法。它首次利用了调制产生的高阶循环平稳性,可以识别出不同的用户各自的传输信道。最后文中提出了循环c(q,n)公式,给出信道的估计。方法简单明了,适合于多用户上行链路信道的盲辨识。文中还讨论了对算法进行的计算机模拟。     

Blind MIMO Channel Estimation Based on Modulation-Induced Two-Cycle Cyclostationarity

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On channel identification based on second-order cyclic spectra

Gardener (1991) presented an interesting use of cyclostationarity for channel identification in PAM data communication systems. The present paper presents new results that address the identification of linear rational channels with higher than minimum bandwidth based on the use of second-order cyclic statistics. Unlike the approach taken by Gardener, the present authors show that channel identification is achievable for a class of linear channels without the need for a pilot tone or training periods. Moreover, channel identification based on cyclic statistics does not preclude Gaussian or near-Gaussian inputs     

双循环相关复倒谱的多用户盲信道辨识方法

该文针对多用户系统提出了一种新颖的盲辨识方法.它利用了调制引入的循环平稳性,可以识别出不同的用户不同的传感器(天线)各自的传输信道.利用循环复倒谱的方法可以给出信道的估计,方法简单明了,与现有的方法相比有一定的优势,适合于多用户上行链路信道.文中还给出了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.     

联合利用两种不同性质循环平稳的盲辨识盲均衡

针对SISO FIR信道,该文提出了一种盲辨识和盲均衡的方案,它可以联合利用调制引入循环平稳和过抽样引入循环平稳这两种不同性质的循环平稳。该方法的好处是发射端和接收端只需要一个天线,并且可以抑制较强的干扰。仿真结果验证了算法的有效性。     

MAXIMUM LIKELIHOOD SOURCE SEPARATION FOR FINITE IMPULSE RESPONSE MULTIPLE INPUT—MULTIPLE OUTPUT CHANNELS IN THE PRESENCE OF ADDITIVE NOISE

Blind identification-blind equalization for finite Impulse Response(FIR)Multiple Input-Multiple Output(MIMO)channels can be reformulated as the problem of blind sources separation.It has been shown that blind identification via decorrelating sub-channels method could recover the input sources.The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators,which decorrelate the output signals of subchannels,and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix.In this paper,a new qpproximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed.The proposed method outperforms BIDS in the presence of additive white Garssian noise.     

Cyclostationarity-Based Frequency Synchronization for OFDM Systems Over Doubly-Selective Fading Channels

Orthogonal frequency division multiplexing (OFDM) systems are highly sensitive to carrier frequency offset (CFO), especially in doubly-selective fading environment. Cyclostationarity-based blind synchronization methods are appealing in high-data-rate applications and low signal-to-noise regions. However, the cyclostationarity has not been exploited for frequency synchronization of OFDM systems under doubly-selective fading channels. In this paper, we derive the close-form second order cyclic statistics of the received OFDM signal in presence of CFO, by modeling the doubly-selective fading channel with basis expansion model. Both transmitter-induced cyclostationarity and doubly-selective channel information are contained in the derived cyclic moments, and they are efficiently utilized for CFO estimation. Simulation results demonstrate that the proposed estimator provides significant improvements on frequency synchronization performance.     

Adaptive blind estimation algorithm for OFDM-MIMO radio systems over multipath channels

This paper investigates the blind algorithm for channel estimation of Orthogonal Frequency Division Multiplexing-Multiple Input Multiple Output (OFDM-MIMO) wireless communication system using the subspace decomposition of the channel received complex baseband signals and proposes a new two-stage blind algorithm. Exploited the second-order cyclostationarity inherent in OFDM with cyclic prefix and the characteristics of the phased antenna, the practical HIPERLAN/2 standard based OFDM-MIMO simulator is established with the sufficient consideration of statistical correlations between the multiple antenna channels under wireless wideband multipath fading environment, and a new two-stage blind algorithm is formulated using rank reduced subspace channel matrix approximation and adaptive Constant Modulus (CM) criterion. Simulation results confirm the theoretical analysis and illustrate that the proposed algorithm is capable of tracking matrix channel variations with fast convergence rate and improving acceptable overall system performance over various common wireless and mobile communication links.     

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。     

Blind identification of FIR MIMO channels by decorrelating subchannels

We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required by most existing methods for the same problem.     

基于粒子滤波的Turbo盲均衡

粒子滤波是一种基于贝叶斯估计的算法,在信道盲辨识和盲均衡问题上具有快收敛、抗深衰信道等优势。Turbo盲均衡在低信噪比条件下有较好的误码性能。为了在深衰信道下使通信具有良好的误码性能,对粒子滤波盲均衡算法进行改进,改进算法的重要性采样函数利用了粒子的先验信息,得到一种软输入软输出的粒子滤波盲均衡算法。依据Turbo盲均衡的框架结构实现了一种基于粒子滤波的Turbo盲均衡算法,该算法利用信道编码带来的编码增益,提高了均衡和信道辨识的性能。仿真结果表明相比粒子滤波盲均衡算法本文提出算法的误码率性能提高1dB左右,误帧率性能则提高了3dB以上,经分析可知在信道系数估计较为准确的条件下,系统数据帧几乎没有误码。      

Blind channel and carrier frequency offset estimation usingperiodic modulation precoders

Previous results have shown that blind channel estimators, which are resilient to the location of channel zeros, color of additive stationary noise, and channel order overestimation errors, can be developed for communication systems equipped with transmitter-induced cyclostationarity precoders. The present paper extends these blind estimation approaches to the more general problem of estimating the unknown intersymbol interference (ISI) and carrier frequency offset/Doppler effects using such precoders. An all-digital open-loop carrier frequency offset estimator is developed, and its asymptotic (large sample) performance is analyzed and compared to the Cramer-Rao bound (CRB). A subspace-based channel identification approach is proposed for estimating, in closed-form, the unknown channel, regardless of the channel spectral nulls. It is shown that compensating for the carrier frequency offset introduces no penalty in the asymptotic performance of the subspace channel estimator. Simulations are presented to corroborate the performance of the proposed algorithms     

基于OFDM信号周期平稳特性的盲信道估计算法

该文提出一种基于信号周期平稳特性的盲信道估计算法,用于频率选择性衰落环境下的正交频分复用(OFDM)系统。该算法利用OFDM信号本身具有的周期平稳特性以及其周期自相关函数值的能量分布规律,确定信道阶数,并通过确定信道零点的方法间接获得信道冲激响应。理论和仿真结果表明,与其它的盲信道估计算法相比,该算法不仅可以较准确地获得信道阶数,而且降低了信道估计的均方误差。