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.     

水声稀疏时变SIMO信道盲均衡算法

基于复指数基扩展模型（Complex exponential basis expansion model, CE-BEM）,利用信道的稀疏特性和发送信号的常模特性（Constant Modulus, CM）,提出水声稀疏时变（Time-variant, TV）SIMO信道盲均衡算法。首先采用l0-范数约束的比例系数归一化最小均方误差常模算法对等效信道矩阵的稀疏时不变部分进行均衡,然后采用基频率估计算法估计基频率并对多普勒频移进行补偿,最后对恢复信号中存在的相偏进行估计补偿。仿真实验结果表明,本文算法提高了均衡器的收敛速度,降低了剩余码间干扰。      

一种抗飞行器衰落的快速均衡算法

对流层散射通信信道为时变多径信道,当飞行器飞越散射通信链路会导致飞行器衰落。针对飞行器衰落,提出了一种收敛速度快、跟踪能力强、数值稳定性高、复杂度低的快速自适应均衡算法——基于选择更新的累积误差递归最小二乘自适应均衡算法。根据指数加权最小二乘准则,推导出累积误差递归最小二乘算法,依据共轭斜量算法提出抽头系数选择更新准则。均衡算法的复杂度分析和仿真实验表明提出的快速自适应均衡算法不仅复杂度低,而且有效地提高了均衡器克服信道时间衰落的能力。     

基于DD-LMS和MCMA的盲判决反馈均衡算法研究

文章对具有DFE结构的盲均衡算法作了研究,在一种修正常模算法(MCMA)代价函数中引入泄漏因子,并将常模算法(CMA)和直接判决-最小均方误差算法(DD-LMS)同时应用到盲判决反馈均衡器的抽头更新中,得到一种适用范围广?均衡特性好?变步长的DD-LLMS MCMBDFE算法。该算法在均衡的同时能自动补偿由信道引起的相位误差,收敛速度快,收敛后剩余误差小,同时还能克服当均衡器长期没有持续输入激励时,LMS算法产生的抽头系数漂移问题。仿真结果表明DD-LLMS MCMBDFE算法是一种有效的盲判决反馈均衡算法。     

简化的复数非圆信号广义线性盲均衡算法

为了提高复数非圆信号的盲均衡性能,本文深入分析广义线性滤波理论,利用常模准则的简便性和稳健性,针对低阶复数非圆信号构造了简化的广义线性盲均衡器,并提出了一种简化的广义线性递归最小二乘常模盲均衡算法。简化的广义线性盲均衡器直接利用接收信号的实部和虚部作为均衡器输入,从而得到接收信号完整的实部和虚部的二阶统计量信息。新算法将标准的广义线性均衡算法的复数运算变成实数运算,有效地降低了标准广义线性均衡器的复杂度。仿真实验结果表明,与传统常模盲均衡算法相比,新算法在不提高计算复杂度的基础上,能够有效降低剩余码间干扰和误码率。      

基于Hammerstein模型的非线性信道广义线性盲均衡算法

为了提高非线性信道盲均衡的性能、降低运算复杂度,本文以Hammerstein模型代替传统的Volterra级数模型来模拟非线性信道,利用非线性信道接收信号呈现非圆性的特点,构造了一种新的基于Wiener非线性模型的广义线性盲均衡器,并在常模准则的基础上提出了NCWL-CMA和NCWL-CMA Newton-like两种非线性信道广义线性盲均衡器抽头系数更新算法.理论分析和仿真实验结果表明,与传统盲均衡算法相比,新算法显著地降低了剩余码间干扰,提高了收敛速度.     

平方根变步长lp范数LMS算法的稀疏系统辨识

针对稀疏未知系统的辨识问题,提出了一种基于lp(0相似文献   

稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法

针对稀疏信道的盲均衡问题,在精简星座均衡算法框架下建立线性模型,利用稀疏信道下均衡器固有的稀疏特性,引入具有稀疏促进作用的先验分布对均衡器系数加以约束,使用稀疏贝叶斯学习方法迭代求解均衡器系数得到最大后验估计值。该文提出的均衡方法属于数据复用类均衡算法的范畴,能够适用于数据较短的应用场合。与随机梯度方法相比,算法性能受均衡器长度影响较小,收敛后误符号率性能更好,仿真实验验证了算法的有效性。     

一种基于信道缩短的深衰落稀疏均衡改进算法

针对较低信噪比下的深衰落稀疏多径信道,提出了一种基于信道缩短的自适应稀疏均衡改进算法。该算法采用前置分数间隔信道缩短均衡器与后置自适应稀疏均衡器级联的均衡器结构,其中,首先利用短训练序列设计基于最小均方误差准则的前置均衡器,前置均衡器与稀疏多径信道级联后得到能量集中于较短时间区域且分布稀疏的等效信道,使得原始信道的深衰落畸变得到部分有效补偿;然后采用能实现稀疏信号重构的随机梯度追踪算法调整后置自适应均衡器的抽头系数,后置均衡器用于消除等效信道的剩余符号间干扰。仿真结果表明,与传统的单级分数间隔自适应均衡器相比,该算法具有收敛速度快和运算复杂度低的优点。     

自适应递推最小二乘灰色均衡器用于时变信道

本文提出了适用于时变信道的自适应递推最小二乘灰色均衡的新思想，以收敛性比较。最小二乘灰色均衡器优于传统的最小二乘均衡器，而以时变信道上的误码率来看，其性能是盲均衡器盲均衡所不能比拟的，而且也优于传统的均衡器，理论分析及计算机模拟表明，自适应最小二乘灰色均衡器是时变信道上性能优良的均衡。     

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.     

Application of Fast Kalman Estimation to Adaptive Equalization

Very rapid initial convergence of the equalizer tap coefficients is a requirement of many data communication systems which employ adaptive equalizers to minimize intersymbol interference. As shown in recent papers by Godard, and by Gitlin and Magee, a recursive least squares estimation algorithm, which is a special case of the Kalman estimation algorithm, is applicable to the estimation of the optimal (minimum MSE) set of tap coefficients. It was furthermore shown to yield much faster equalizer convergence than that achieved by the simple estimated gradient algorithm, especially for severely distorted channels. We show how certain "fast recursive estimation" techniques, originally introduced by Morf and Ljung, can be adapted to the equalizer adjustment problem, resulting in the same fast convergence as the conventional Kalman implementation, but with far fewer operations per iteration (proportional to the number of equalizer taps, rather than the square of the number of equalizer taps). These fast algorithms, applicable to both linear and decision feedback equalizers, exploit a certain shift-invariance property of successive equalizer contents. The rapid convergence properties of the "fast Kalman" adaptation algorithm are confirmed by simulation.     

基于l1-范数约束的递归互相关熵的稀疏系统辨识

为了有效解决脉冲噪声环境下的稀疏系统辨识(Sparse system identification, SSI)问题,以l1 -范数为约束构建稀疏递归互相关熵准则(Recursive maximum correntropy criterion, RMCC)算法来解决脉冲噪声对于辨识性能的影响。结合带遗忘算子的互相关熵准则和l1 -范数作为代价函数,推导出一种递归形式的算法,其相对于传统的最大相关熵算法具有快的收敛速度及小的稳态误差。仿真实验结果表明:该算法对于脉冲噪声干扰环境下的SSI问题具有强的鲁棒性。      

Recursive least square–based fast sparse multipath channel estimation

In the next‐generation wireless communication systems, the broadband signal transmission over wireless channel often incurs the frequency‐selective channel fading behavior and also results in the channel sparse structure, which is supported only by few large coefficients. For the stable wireless propagation to be ensured, linear adaptive channel estimation algorithms, eg, recursive least square and least mean square, have been developed. However, these traditional algorithms are unable to exploit the channel sparsity. Actually, channel estimation performance can be further improved by taking advantage of the sparsity. In this paper, 2 recursive least square–based fast adaptive sparse channel estimation algorithm is proposed by introducing sparse constraints, L1‐norm and L0‐norm, respectively. To improve the flexibility of the proposed algorithms, this paper introduces a regularization parameter selection method to adaptively exploit the channel sparsity. Finally, Monte Carlo–based computer simulations are conducted to validate the effectiveness of the proposed algorithms.     

Adaptive MLSE equalizers with parametric tracking for multipathfast-fading channels

We use the parametric channel identification algorithm proposed by Chen and Paulraj (see Proc. IEEE Vehicular Technology Conf., p.710-14, 1997) and by Chen, Kim and Liang (see IEEE Trans. Veh. Technol., p.1923-35, 1999) to adaptively track the fast-fading channels for the multichannel maximum likelihood sequence estimation (MLSE) equalizer using multiple antennas. Several commonly-used channel tracking schemes, decision-directed recursive least square (DD/RLS), per-survivor processing recursive least square (PSP/RLS) and other reduced-complexity MLSE algorithms are considered. An analytic lower bound for the multichannel MLSE equalizer with no channel mismatch in the time-varying specular multipath Rayleigh-fading channels is derived. Simulation results that illustrate the performance of the proposed algorithms working with various channel tracking schemes are presented, and then these results are compared with the analytic bit error rate (BER) lower bound and with the conventional MLSE equalizers directly tracking the finite impulse response (FIR) channel tap coefficients. We found that the proposed algorithm always performs better than the conventional adaptive MLSE algorithm, no matter what channel tracking scheme is used. However, which is the best tracking scheme to use depends on the scenario of the system     

Adaptive linear equalization combined with noncoherent detectionfor MDPSK signals

A novel noncoherent receiver for M-ary differential phase-shift keying signals transmitted over intersymbol interference channels is presented. The noncoherent receiver consists of a linear equalizer and a decision-feedback differential detector. A significant performance gain over a previously proposed noncoherent receiver can be observed. For an infinite number of feedback symbols, the optimum equalizer coefficients can be calculated analytically, and the performance of the proposed receiver approaches that of a coherent linear minimum mean-squared-error equalizer. Moreover, a modified least mean square and a modified recursive least squares algorithm for adaptation of the equalizer coefficients are discussed     

基于自适应滤波的压缩感知重构方法

自适应滤波框架中,滤波器的抽头系数可以利用特定的自适应算法达到近似维纳解,从而使滤波器的输出误差达到最小.将这个框架应用到压缩感知重构信号中,信号的稀疏系数等效为滤波器系数权值向量,从而可获得最佳的稀疏系数,以高概率重构信号.本文介绍了已有学者研究出的一种L0最小均方算法(L0-LMS),该算法中引入零引力项加快了权矢量向稀疏解收敛的速度,保证解的稀疏性.通过仿真可知,基于自适应滤波算法重构稀疏信号的性能较好,甚至优于压缩感知中常用的OMP算法.