MIMO blind equalization algorithm based on successive interference cancellation

Modified Constant Modulus Algorithm (MCMA) is widely used in the SISO blind equalization for its simplicity. For MIMO systems MCMA can only equalize one of the source signals. Through the combination of channel estimation and successive interference cancellation, source signals can be equalized in turn. However the recovery of the first source and the channel estimation are the key points, which directly affects the recovery of the subsequent sources. This paper proposes a channel estimation method with a small amount of calculation, and can accurately estimate the channel vector. Meanwhile, a new blind equalization algorithm is put forward to reliably recovery the first source signal. Simulation results verify the effectiveness of the proposed algorithm.     

A hybrid adaptive blind equalization algorithm for QAM signals in wireless communications

A hybrid adaptive channel equalization technique for quadrature amplitude modulation (QAM) signals is proposed. The proposed algorithm, which is referred to as the modified constant modulus algorithm (MCMA), minimizes an error cost function that includes both amplitude and phase of the equalizer output. In addition to the amplitude-dependent term that is provided by the conventional constant modulus algorithm (CMA), the cost function includes an additive signal constellation matched error (CME) term. This term can be designed to satisfy a set of desirable properties. The MCMA is compared with the CMA for blind equalization. The performance is measured for wireless channels using both transient and steady-state behavior of the mean square error (MSE). It is shown that MCMA is superior and more robust in low signal-to-noise ratio (SNR) environments. Simulation results demonstrate that using MCMA improves adaptive channel equalization by increasing the convergence rate and decreasing the steady-state mean square error.     

修正恒模算法分数间隔盲均衡

分数间隔均衡(FSE)能够降低对时间同步误差的敏感性,本文通过修正恒模算法(CMA)的误差函数,提出了一种分数间隔盲均衡修正恒模算法(MCMA).利用通信信号8PSK,对修正恒模算法和传统恒模算法进行了盲均衡性能比较,在噪声环境下的频率选择性信道中,MCMA比CMA具有更强的鲁棒性.模拟结果显示MCMA能够通过提高收敛速率、降低稳态均方误差和符号间干扰(ISI),有效地改进了信道均衡性能.     

瑞利衰落电离层信道的自适应盲均衡

研究通过具有加性高斯白噪声的瑞利慢衰落电离层信道的恒模盲均衡技术。著名的恒模算法(CMA)是一种重要的盲信道均衡方法。但是，普通恒模算法存在收敛速度慢和相位旋转等不足。采用一种修正恒模算法(MCMA)，该算法使修正的误差函数最小并且自适应学习率由接收序列即时调整。两种算法用8—PSK信号进行了盲均衡比较，模拟结果显示修正恒模算法比普通恒模算法的收敛速度快，符号间干扰(ISI)小，另外该算法均衡器输出具有既无相位旋转也无延迟等优点。     

OFDM-TDM在快衰落环境下的双模式MCMA算法研究

OFDM-TDM快衰落下的盲均衡算法做了研究,结合插值算法及DD-LMS和MCMA算法,并将其应用到盲判决反馈均衡器进行抽头更新,在频率选择性衰落下得到一种均衡性能更优的插值DD-LMS+MCMBDFE算法。该算法在均衡的同时不仅能自动补偿相位误差,还能有效补偿多普勒频偏误差,收敛后剩余误差更小。仿真结果表明插值DD-LMS+MCMBDFE算法是一种有效的盲判决反馈均衡算法。     

一种新的混合信道盲均衡算法

为同时提高盲均衡的稳态MSE性能和收敛速度,该文提出了一种混合信道盲均衡算法。该算法采用判决反馈均衡器结构,在判决可靠时采用DD-LMS(Directed Decision-Least Mean Square)误差项进行迭代,而判决不可靠时采用改进恒模算法(Modified Constant Modulus Algorithm,MCMA)误差项进行迭代,同时判决可靠区间根据直接判决误差进行自适应迭代。该文采用经过数字无线信道的64QAM信号进行了信道均衡仿真。仿真结果表明,相比MCMA算法,该文提出的混合盲均衡算法有效地提高了收敛速度,并具有良好的稳态MSE性能,在误比特率为10-6时,能提高SNR约2dB。     

基于DD-LLMS算法的盲自适应判决反馈均衡器

本文把用于自适应线性滤波器的LLMS算法推广到盲判决反馈均衡器,并应用于短波信道的盲均衡。仿真结果表明这种DD—LLMS盲判决反馈均衡算法具有很好的稳定性、较快的收敛速度和较好的跟踪性能。     

一种新型盲均衡算法－RG算法的研究

本文提出了一种新的盲均衡算法－ＲＧ算法，它保持了Ｇｏｄａｒｄ盲均衡算法收敛性能好的特点，其误码性能也优于Ｇｏｄａｒｄ盲均衡算法，尤其是对ＭＰＳＫ调制系统。理论分析及计算机模拟表明，ＲＧ算法是一种性能优良的盲均衡算法。     

基于样条函数Renyi熵的时间分集小波盲均衡算法

针对时间分集分数间隔判决反馈盲均衡算法(TD)计算量大、收敛速度慢的缺点,该文提出了一种基于样条函数Renyi熵的时间分集小波盲均衡算法。该算法直接把定义的样条函数Renyi熵作为代价函数用于TD的权向量更新,利用分数间隔获得更详细的信道信息;由正交小波变换降低输入信号的自相关性,以加快收敛速度;利用时间分集和判决反馈结构来降低多径衰落对通信质量的影响。水声信道盲均衡的仿真实验,验证了该算法的有效性。     

On the (non)existence of undesirable equilibria of Godard blindequalizers

Existing results in the literature have proved that particular blind equalization algorithms, including Godard algorithms, are globally convergent in an ideal and nonimplementable setting where a doubly infinite dimensional equalizer is available for adaptation. Contrary to popular conjectures, it is shown that implementable finite dimensional equalizers which attempt to approximate the ideal setting generally fail to have global convergence to acceptable equalizer parameter settings without the use of special remedial measures. A theory based on the channel convolution matrix nullspace is proposed to explain the failure of Godard algorithms for such practical blind equalization situations. This nullspace theory is supported by a simple example showing ill convergence of the Godard algorithm     

基于组合代价函数的双模式盲均衡新算法

以常数模和判决引导准则设计的双模式盲均衡算法可显著提高均衡性能,目前已有双模式盲均衡算法均需设置切换参数且切换参数选择和设定缺乏理论依据.为解决双模式盲均衡算法中切换参数难以确定的问题,提出来一种组合代价函数的双模式盲均衡新算法.利用常数模和判决引导准则通过加权设计代价函数,在盲均衡器更新过程中自适应调节权值实现算法由常数模算法向判决引导算法的切换,避免了在双模式算法中设计切换参数,提高了算法的泛化性能.为克服常数模算法相位盲的缺点,在虚实分开改进的常数模算法基础上优化组合代价函数以及盲均衡器更新算法的设计,进一步提高了算法收敛性能.仿真结果证明,组合代价函数双模式盲均衡新算法可充分发挥常数模算法和判决引导算法的优点,具有更快的收敛速度和更小的稳态剩余误差.     

含软方向判决的修正CMA盲均衡新算法

提出了一种新颖的盲均衡算法MCMA SDD,它一方面在代价函数中同时包含了幅度和相位的信息,另一方面,增加了并行的软方向判决(SDD)盲均衡算法,使得MCMA SDD算法无需载波跟踪环就能够纠正相位的失真;在低信噪比条件下具有较快的收敛速度和较低的稳态均方误差。     

一种适用于MPSK调制的混合型盲均衡算法

提出了一种特别适用于ＭＰＳＫ信号的混合型盲均衡算法，理论分析和计算机模拟表明，这种混合型盲均衡算法的收敛性能与Ｇｏｄａｒｄ类盲均衡算法的收敛性能相同，而误码性能优于Ｇｏｄａｒｄ类盲均衡算法，并接近系统ＬＭＳ均衡算法的性能，是一种具有实用价值的盲均衡算法。     

Detection of coded modulation signals on linear, severely distortedchannels using decision-feedback noise prediction with interleaving

On linear bandlimited Gaussian noise channels with sufficiently high SNR, channel capacity can be approached by combining powerful coded modulation schemes designed for Nyquist channels with the equalization power of decision-feedback equalization (DFE). However, this combination may not be realized in a straightforward manner, since, in general, DFE requires delay-free decisions for feedback, and in a coded system such decisions are not sufficiently reliable. A technique is proposed that combines periodic interleaving with noise-predictive DFE, so that delayed reliable decisions can be used for feedback. When sufficient delay in the interleavers can be tolerated, this technique can attain the DFE performance. On severely distorted channels, modest delays can be sufficient to obtain respectable gains over linear equalization     

Dual-mode type algorithms for blind equalization

Adaptive channel equalization accomplished without resorting to a training sequence is known as blind equalization. The Godard algorithm and the generalized Sato algorithm are two widely referenced algorithms for blind equalization of a QAM system. These algorithms exhibit very slow convergence rates when compared to algorithms employed in conventional data-aided equalization schemes. In order to speed up the convergence process, these algorithms may be switched over to a decision-directed equalization scheme once the error level is reasonably low. The authors present a scheme which is capable of operating in two modes: blind equalization mode and a mode similar to the decision-directed equalization mode. In this proposed scheme, the dominant mode of operation changes from the blind equalization mode at higher error levels to the mode similar to the decision-directed equalization mode at lower error levels. Manual switch-over to the decision-directed mode from the blind equalization mode, or vice-versa, is not necessary since transitions between the two modes take place smoothly and automatically     

使用差分相位调制解决盲自适应恒模算法相位旋转问题的研究

恒模算法CMA是一种重要的盲均衡自适应算法,但存在相位旋转问题,本文研究把差分相位调制应用到CMA盲均衡系统中,解决恒模算法的相位旋转问题。通过跟MCMA算法比较,证明了这种方法可以补偿信道任意角度的相位失真,能更有效地解决恒模算法的相位旋转问题,并且能够抵抗一定的多普勒频移。仿真结果表明这种方法降低了CMA盲均衡系统的误码率,提高了系统的性能。     

A Blind Equalizer Based on Unsupervised Gaussian Cluster Formation with an Adaptive Non—Linearity

In this paper we present a blind equalizer algorithm based on an unsupervised Gaussian cluster formation technique with an optimized gradient adaptive step-size to update the equalizer coefficients.The novelty of this work lies in the optimization of the nonlinearity of the cluster formation used to achieve an optimal soft decision.The proposed iterative procedure combined with the variable step-size gradient-based adaptation,significantly accelerates the convergence speed of the blind equalization.The advantages of the proposed equalization techniques are illustrated by simulation.Simulation results obtained are compared with the Sato and Godard blind equalizers.     

一种DDLMS和CMA方法相结合的盲均衡算法

本文提出一和种适用于M-PAM和M-QAM的新的盲均衡算法：DDMLS-CMA算法。该算法克服了盲均衡算法Sato、Godrd残余误差大的缺点，又保持了快速收敛特性，并且收敛速率比BG算法快，是一种稳定的盲均衡算法。     

一种改进的QAM信号的恒模算法：MCADAMA

提出了一种适用于QAM系统的改进的双模式算法MCADAMA（modified CMA-assisted decision adjusted modulus algorithm）。通过研究Axford提出的CADAMA双模式算法,受到MCMA算法的启发,结合恒模算法的特性,研究出了MCADAMA算法。通过搭建QAM盲均衡系统平台,把MCADAMA算法和其他3种恒模算法进行了比较,仿真结果表明,MCADAMA算法具有更快的收敛速度和更低的误码率,有效地提高了系统性能。     

适用于水声信道的快速判决反馈盲均衡算法研究

基于对常数模判决反馈算法(CMDFE)进行分析,提出了一种能够快速收敛的判决反馈盲均衡算法。该算法中构造了一种与常数模算法不同的、能够快速收敛的误差函数,并利用该误差函数对前向权进行调整,而反馈权的调整仍使用常数模误差项。前向权和反馈权系数的更新取不同的步长值。最后通过水声信道模型仿真,对这几种算法进行了数值分析研究。结果表明：所提出的算法可有效地实现对多途水声信道的均衡,并且其收敛速度高于常数模算法和常模判决反馈算法。