基于回声状态网络的卫星信道在线盲均衡算法

针对非线性卫星信道,该文提出了两种基于回声状态网络(ESN)的在线盲均衡算法。利用ESN良好的非线性逼近能力,将发送信号的高阶统计量(HOS)代入ESN,结合常模算法(CMA)和多模算法(MMA)构造盲均衡的代价函数,并采用递归最小二乘(RLS)算法对ESN输出权值进行迭代寻优,实现了Volterra卫星信道下常模和多模信号的在线盲均衡。实验表明,该文算法可以有效降低非线性信道对发送信号产生的畸变,相较于传统的Volterra滤波方法,有更快的收敛速度和更低的均方误差值。     

基于正交小波变换的前馈神经网络盲均衡算法

针对前馈神经网络（FNN）盲均衡算法收敛速度慢、均方误差大的缺点,在分析FNN盲均衡算法和正交小波变换（OWT）理论的基础上,提出了基于正交小波变换的FNN盲均衡算法。该算法利用正交小波变换良好的去相关性,对FNN均衡器输入信号进行预处理后,降低了输入信号的自相关性,从而加快了收敛速度和减小了均方误差。水声信道盲均衡的仿真结果表明,该算法在收敛速度与均方误差方面的性能比FNN盲均衡算法优越。     

Blind equalization algorithm based on complex support vector regression

A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method.     

基于最大相关熵准则的恒模盲均衡算法

针对恒模算法(constant modulus algorithm, CMA)在脉冲噪声环境下性能退化的问题,本文基于最大相关熵准则(maximum correntropy criterion, MCC)对恒模算法中基于最小均方误差(mean square error, MSE)准则的代价函数进行修正,推导出适用于脉冲噪声环境的基于MCC准则的恒模盲均衡算法(MCC_CMA)。该算法利用通信信号的恒模特性,首先得到发送信号与均衡器输出信号模值的误差信号,再通过使模值误差信号的相关熵最大来获得其迭代误差调节项,避免了传统高阶统计量算法在脉冲噪声环境下性能退化的问题。对高斯噪声以及α-稳定分布和混合高斯分布两种脉冲噪声环境下的信道均衡问题的仿真实验表明,相对于经典的自适应恒模盲均衡算法,MCC_CMA算法不依赖噪声的先验知识就能获得较快的收敛速度、较低的剩余码间干扰和误码率,并且在不同脉冲强度的脉冲噪声环境下都能够得到较好的均衡结果,表明MCC_CMA算法具有很好的鲁棒性。      

基于支持向量机的非线性信道盲均衡算法研究

针对非线性信道盲均衡问题,考察了一种基于支持向量机的单输入单输出(SISO)盲均衡算法,该算法利用通信信号的常数模特性,将非线性盲均衡问题转化为非线性支持向量回归问题。在此基础上,本文利用分集技术,将该算法拓展至单输入多输出(SIMO)的情况。对两种算法进行的计算机仿真表明,基于支持向量机的SISO盲均衡算法能够有效地抑制信道中的非线性码间干扰;本文提出新算法由于更好地利用了信道的时空特性,具有剩余平均模误差小的优点。     

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.     

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。      

Sparse adaptive constant blind equalization algorithm for sparse multipath channel

In order to improve the convergence rate of the blind equalizer for sparse multipath channel,a novel blind equalization approach called l₀-norm constraint proportionate normalized least mean square constant algorithm was proposed for M-order phase-shift keying (MPSK) signal.Based on the constant modulus characteristics of MPSK signal and the sparse property of equalizer,a new blind equalization cost function with the l₀-norm penalty on the equalizer tap coefficients was firstly constructed.Then the update formula of the tap coefficients was derived according to the gradient descent algorithm.Moreover,the iteration step was updated by drawing upon the normalized proportionate factor.The algorithm not only assigned step sizes proportionate to the magnitude of the current individual tap weights,but also attracted the inactive taps to zero adaptively.Theoretical analysis and simulation results show that the proposed algorithm outperforms the existing blind equalization algorithms for sparse channel in reducing ISI and improving convergence rate.     

l0-范数约束的稀疏多径信道分数间隔双模式盲均衡算法

针对深衰落稀疏多径信道下多进制相移键控（Multiple Phase Shift Keying,MPSK）信号的盲均衡问题,提出了一种l₀-范数约束的分数间隔稀疏自适应双模式盲均衡算法.该算法借鉴传统的分数间隔双模式盲均衡算法思想,结合稀疏自适应滤波理论,首先利用l₀-范数对均衡器抽头系数进行稀疏性约束,构造出一种l₀-范数约束的分数间隔双模式最小均方误差代价函数,然后依据梯度下降法推导出盲均衡器抽头系数更新公式,并对迭代步长进行归一化和比例系数化.理论分析和仿真实验表明,与基于门限稀疏化的盲均衡算法、基于分数阶范数的盲均衡算法及分数间隔双模式盲均衡算法相比,本文所提算法在保证较快收敛速度的前提下,能有效降低剩余符号间干扰.本文设计的盲均衡算法为水声通信系统中接收方恢复出发送信号,提供了一种快速有效的方法.     

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。     

l0-范数约束的稀疏多径信道RLS常模盲均衡算法

针对稀疏多径信道下MPSK信号的快速盲均衡问题,提出了一种l0-范数约束的递归最小二乘常模盲均衡算法.该算法借鉴传统的递归最小二乘常模盲均衡算法思想,结合稀疏自适应滤波理论,首先利用l0-范数对均衡器抽头系数进行稀疏性约束,构造出一种l0-范数约束的加权最小二乘误差代价函数,然后依据递归最小二乘算法推导出均衡器抽头系数更新公式.该算法发挥递归最小二乘常模算法收敛速度快的优势,并对幅度极小系数附加零点吸引调整,从而实现不同幅度抽头系数的快速收敛.理论分析与仿真结果表明,与现有算法相比,该算法在保证较低剩余符号间干扰的前提下,能有效提高均衡器的收敛速度.     

多通道SAR-GMTI通道盲均衡算法

基于回波数据相关矩阵特征分解的通道盲均衡算法可有效校正多通道SAR系统中由各种非理想因素引起的通道幅度相位误差,但该算法主要的缺点是收敛速度慢。该文首先分析了基于回波数据相关矩阵特征分解的通道盲均衡算法的基本工作原理;在此基础上,针对算法收敛性差的缺点,结合降维处理技术,提出一种快速收敛的通道盲均衡算法。仿真及实测数据实验结果表明:与常规的基于回波数据相关矩阵特征分解的通道盲均衡算法相比,该文所提算法收敛所需的样本数目显著减少,即可在小训练样本条件下实现对通道幅度相位误差的均衡。     

适用于圆形高阶QAM信号的盲均衡算法

针对现有盲均衡算法大多仅适用于方形或十字形QAM信号的问题,本文提出了一种适用于圆形高阶QAM信号的混合盲均衡算法。该算法首先对区域多模盲均衡算法进行改进,提出了新的区域划分方法并将其与改进的多模盲均衡算法相结合,构造了新的误差模型使之与圆形星座匹配程度大大提高;然后将其与判决引导算法和数据重用技术有机结合,构成低稳态误差的快速混合盲均衡算法。与其他同类型盲均衡算法相比,本文算法对于圆形星座高阶QAM信号尤为有效,不仅降低了稳态误差,且大幅度减少了收敛所需符号数。仿真结果验证了该算法的有效性。      

采用归一化最小均方误差准则的LM-BP算法

传统神经网络通常以最小均方误差(LMS)或最小二乘（RLS）为收敛准则,而在自适应均衡等一些应用中,使用归一化最小均方误差（NLMS）准则可以使神经网络性能更加优越。本文在NLMS准则基础上,提出了一种以Levenberg-Marquardt（LM）训练的神经网络收敛算法。通过将神经网络的误差函数归一化,然后采用LM算法作为训练算法,实现了神经网络的快速收敛。理论分析和实验仿真表明,与采用最速下降法的NLMS准则和采用LM算法的LMS准则相比,本文算法收敛速度快,归一化均方误差更小,应用于神经网络水印系统中实现了水印信息的盲提取,能更好的抵抗噪声、低通滤波和重量化等攻击,性能平均提高了4%。      

模糊神经网络控制的混合小波神经网络盲均衡算法

针对传统恒模算法(CMA)收敛速度与均方误差之间的矛盾,提出了模糊神经网络控制的混合小波神经网络(FHWNN)盲均衡算法.该算法在小波神经网络输入层之前级联一个横向滤波器,将横向滤波器的节点输出分为实部和虚部两路经过小波神经网络后再合成为一路复数信号;利用模糊神经网络(FNN)设计的模糊规则控制小波函数的尺度因子和平移...     

Application of Recurrent Wavelet Neural Networks to the Digital Communications Channel Blind Equalization

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基于深度学习的单载波频域均衡算法研究

单载波频域均衡(Single-Carrier Frequency-Domain Equalization,SC-FDE)是一种有效的抗码间干扰的算法,在无线通信系统中得到了广泛的应用.传统线性SC-FDE算法主要包括信道估计、噪声功率估计和信道均衡三个模块,其中每个模块都是单独优化的.为了联合优化这三个模块,本文提出了...     

Nonlinear revised error aided feedback equalization in high-speed satellite communication

Aiming to the question of nonlinear interference which is caused by the high order signal modulation of satellite communications, and the question of obvious memory characteristics of the satellite channel, the existing equalization algorithms are difficult to quickly cancel the nonlinear interference and memory interference. This paper proposed a nonlinear revised error aided feedback equalization algorithm. Due to the nonlinear characteristics of amplitude and phase don’t interference with each other, so the amplitude and phase of the received signals are separated in first, then they utilize the feedback equalization to abate memory interference. In addition, the nonlinear interference of satellite channel would degrade the performance of satellite communication, the algorithm utilizes the three-order Volterra module to revise the nonlinear error and it is adapted to renews the parameters of feedback equalization to reduce the nonlinear interference. Our proposed algorithm utilized the nonlinear revised error instead of the nonlinear revised received signal to reduce the computation complexity and improve the nonlinear interference cancelation. In the simulation part, we compare the proposed equalization algorithm with the existing equalization algorithms in three parts of SER, convergence speed and computation complexity. The simulation and analysis results show that our proposed algorithm have better nonlinear interference cancelation and less computational complexity.     

Blind MMSE Equalization of FIR/IIR Channels Using Oversampling and Multichannel Linear Prediction

A linear‐prediction‐based blind equalization algorithm for single‐input single‐output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second‐order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single‐input multiple‐output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum‐mean‐square‐error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.     

基于模拟退火粒子群优化的恒模算法

传统的固定步长恒模盲均衡算法在收敛速度和稳态剩余误差之间存在着矛盾,影响了恒模算法的均衡性能。为了解决这一缺点,提高恒模算法的性能,本文在介绍恒模盲均衡原理的基础上,提出一种变步长恒模算法,利用粒子群优化算法对均衡器的权向量进行优化,为避免优化过程进入局部极小值引入了模拟退火算法。通过在短波信道中的仿真,证明了该算法不仅加快了收敛速度,并且具有更高的收敛精度。