RLS算法自适应信道估计的性能分析

文中首先介绍了基本的RLS算法,分析了RLS算法中的初始化系数δ和遗忘因子λ对RLS算法收敛性能的影响。通过仿真可以看出,在相同的信噪比下,不同的δ对应不同的收敛性能。而不同的λ对收敛性能也有较大的影响。     

无人机数据链路信道LMS和RIS自适应均衡性能比较

通过时LMS和RLS自适应算法在无人机数据链路不同信道特性均衡中的应用进行研究，得出了两种均衡算法在对于不同信道均衡效果的差异，给出了两种均衡算法的最佳应用条件。     

改进型RLS算法用于信道估值与信道均衡的比较

文对用于信道估值和信道均衡的几种改进型RLS算法进行了比较,理论分析和计算机仿真结果说明信道估值的最优解可以模拟出信道的传输函数,噪声仅对自适应滤波算法的收敛过程产生影响,而信道均衡的最优解受噪声功率谱密度的限制。因此性能良好的信道估值算法可以用于信道均衡中,以改善自适应均衡的性能。     

无人机数据链信道均衡技术仿真研究

信道均衡技术是无线通信中解决码间干扰的关键技术.选取LMS,RLS和CMA常用的均衡算法以及信道均衡器的结构作为研究对象,通过对三种均衡算法的收敛性能的仿真分析,以及结合无人机信道模型对常用的两种均衡结构的误码性能进行的仿真,所得结果对于后期基于FPGA实现高速的信道均衡器时算法和结构的选择具有参考的价值.     

基于LMS及RLS的自适应均衡算法仿真分析

在通信系统中采用均衡技术是改善信道特性行之有效的方法,为此从时域均衡原理出发,讨论了基于LMS和基于RLS的自适应均衡算法,并利用Matlab对两类算法进行了仿真,从均衡前后信号的星座图、算法收敛特性以及均衡前后系统的误码特性这三个方面对两类算法的性能进行了比较.     

一种基于无偏协方差估计RLS的自适应阵列天线抗干扰算法

RLS是自适应阵列天线抗干扰的主要算法之一。为提高RLS算法对遗忘因子选择健壮性,避免因遗忘因子选择不当所造成的算法不收敛问题,本文针对自适应阵列天线的多路接收信号,基于其无偏协方差矩阵模型,推导设计出了一种新的RLS算法,相比于常规RLS,在该算法中遗忘因子可以更加精确地控制RLS迭代过程项,降低因遗忘因子设置不当而造成的算法不收敛风险。通过仿真验证了算法的有效性。     

IEEE 802.16单载波调制系统的信道均衡技术

简要介绍了固定宽带无线接入标准IEEE 802.16以及一种用于信道均衡的自适应算法——指数加权RLS,对判决导引信道均衡技术的原理进行了具体描述。最后分别就两种自适应的均衡算法（LMS、RLS）,结合一种具体IEEE 802.16单载波调制系统推荐测试信道进行了仿真,得出RLS算法优于LMS的结论。     

RLS算法及其改进形式在信号分离中的应用分析

本文着重研究了自适应滤波器的重要实现形式——递推最小二乘算法（RLS）的原理,分析了RLS算法在应用中的优点及存在问题。为解决RLS算法收敛速度和稳态误差的矛盾及系统在趋于平稳时跟踪效果差的问题,本文从实现可变遗忘因子和增加自扰动项两个方面介绍了RLS算法的几种改进方法。并将它们应用于复杂电磁环境、强干扰背景下的信号分离中去。通过仿真实验,对RLS算法及其两种改进方法在信号分离中的效果进行了比较,得出可变遗忘因子RLS算法在收敛速度和分离信号的准确性上都具有较好的性能。     

一种基于MMSE的低复杂度自适应均衡算法

传统的MMSE均衡算法需要作信道估计,算法复杂度也很高,采用了一种基于最陡梯度下降的LMS迭代算法,该算法不需要信道估计,也不需要复杂的矩阵逆运算,其算法复杂度远远低于MMSE均衡算法的复杂度。仿真结果表明,该LMS迭代算法的收敛速度比RLS算法的收敛速度稍慢,而误码率与RLS算法相当,但其算法复杂度远远低于RLS算法和传统的MMSE均衡算法。     

基于Simulink的NLMS弥散信道均衡滤波器的参数优选方法

为了克服弥散信道的影响,改善通信质量,提高传输数据速率,近年来出现了多种解决方案,其中最有效的方法就是采用NLMS算法的自适应信道均衡技术.设计NLMS算法的自适应信道均衡器的关键就是寻找一组最佳的自适应滤波器的长度L和步长因子,使系统误码率最低.基于此,本文提出了一种能有效搜索均衡滤波器参数的方法,并构建了该方法的Simulink仿真平台.通过仿真,搜索寻找到了系统误码性能最佳条件下自适应滤波器的长度和步长,同时分析了自适应均衡器的性能,仿真结果证明了其有效性.该平台为最优自适应弥散信道均衡滤波器的设计提供了一个很好的平台,具有很高的实用价值.     

RLS Algorithm with Variable Fogetting Factor for Decision Feedback Equalizer over Time-Variant Fading Channels

In a high-rate indoor wireless personal communication system, the delay spread due to multipath propagation results in intersymbol interference (ISI) which can significantly increase the transmission bit error rate (BER). Decision feedback equalizer (DFE) is an efficient approach to combating the ISI. Recursive least squares (RLS) algorithm with a constant forgetting factor is often used to update the tap-coefficient vector of the DFE for ISI-free transmission. However, using a constant forgetting factor may not yield the optimal performance in a nonstationary environment. In this paper, an adaptive algorithm is developed to obtain a time-varying forgetting factor. The forgetting factor is used with the RLS algorithm in a DFE for calculating the tap-coefficient vector in order to minimize the squared equalization error due to input noise and due to channel dynamics. The algorithm is derived based on the argument that, for optimal filtering, the equalization errors should be uncorrelated. The adaptive forgetting factor can be obtained based on on-line equalization error measurements. Computer simulation results demonstrate that better transmission performance can be achieved by using the RLS algorithm with the adaptive forgetting factor than that with a constant forgetting factor previously proposed for optimal steady-state performance or a variable forgetting factor for a near deterministic system.     

基于可变遗忘因子RLS在OFDM系统的载波频偏估计

在时变信道下正交频分复用（OFDM）系统中,通过导频辅助,提出基于可变遗忘因子RLS（VFF-RLS）的载波频偏（CFO）估计改进算法。针对传统RLS（CFF-RLS）算法中遗忘因子无法同时满足CFO估计收敛速度和收敛精度的缺陷,本文设计了线性变化遗忘因子(LFF)和非线性变化遗忘因子(NLFF) 两种可变遗忘因子方案来提升CFO估计性能。仿真结果显示：在低信噪比的情形下,基于VFF-RLS算法的CFO估计性能明显优于基于CFF-RLS算法的CFO估计性能。     

基于动态不确定度评定的加权数据融合算法

加权融合算法的AR模型,采用遗忘因子递推最小二乘法估计出测量方差.根据测量方差对单一导航传感器信息进行动态不确定度评定,进而确定组合导航系统的组合不确定度.在满足组合不确定度最小的情况下,利用拉格朗日乘数法进行加权值的求解.通过实例仿真表明该法具有较高的融合精度,对提高组合导航系统的定位精度具有实际意义.     

Variable forgetting factor linear least squares algorithm forfrequency selective fading channel estimation

In this article, the variable forgetting factor linear least squares algorithm is presented to improve the tracking capability of channel estimation. A linear channel model with respect to time change describes a time-varying channel more accurately than a conventional stationary channel model. To reduce the estimation error due to model mismatch, we incorporate the modified variable forgetting factor into the proposed algorithm. Compared to the existing algorithms-exponentially windowed recursive least squares algorithm with the optimal forgetting factor and linear least squares algorithm-the proposed method makes a remarkable improvement in a fast fading environment. The effects of channel parameters such as signal-to-noise ratio and fading rate are investigated by computer simulations     

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in \(2\times 1\) space–time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.     

Optimal training design for MIMO OFDM systems in mobile wireless channels

This paper describes a least squares (LS) channel estimation scheme for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems based on pilot tones. We first compute the mean square error (MSE) of the LS channel estimate. We then derive optimal pilot sequences and optimal placement of the pilot tones with respect to this MSE. It is shown that the optimal pilot sequences are equipowered, equispaced, and phase shift orthogonal. To reduce the training overhead, an LS channel estimation scheme over multiple OFDM symbols is also discussed. Moreover, to enhance channel estimation, a recursive LS (RLS) algorithm is proposed, for which we derive the optimal forgetting or tracking factor. This factor is found to be a function of both the noise variance and the channel Doppler spread. Through simulations, it is shown that the optimal pilot sequences derived in this paper outperform both the orthogonal and random pilot sequences. It is also shown that a considerable gain in signal-to-noise ratio (SNR) can be obtained by using the RLS algorithm, especially in slowly time-varying channels.     

Low Complexity Linear Channel Estimation for MIMO Communication Systems

Channel estimation is employed to get the current knowledge of channel states for an optimum detection in fading environments. In this paper, a new recursive multiple input multiple output (MIMO) channel estimation is proposed which is based on the recursive least square solution. The proposed recursive algorithm utilizes short training sequence on one hand and requires low computational complexity on the other hand. The algorithm is evaluated on a MIMO communication system through simulations. It is realized that the proposed algorithm provides fast convergence as compared to recursive least square (RLS) and robust variable forgetting factor RLS (RVFF-RLS) adaptive algorithms while utilizing lesser computational cost and provides independency on forgetting factor.     

基于自调整器的CDMA系统盲自适应干扰抑制

本文给出一种能同时抑制DS-CDMA系统多址干扰(MAI)和窄带干扰(NBI)的盲自适应算法.此方法基于遗忘因子具有自调整器的迭代最小二乘算法(SR-RLS),根据系统的变化自动调整遗忘因子,当系统趋于静态时,遗忘因子趋于1,以提高稳态精度,在动态系统中,遗忘因子减小,使算法能有效的跟踪系统参数.与其它的迭代最小二乘相比,具有较小的稳态误差和良好的动态跟踪能力.文章从理论上分析了算法的收敛性.最后,对算法在静态环境和动态环境中的性能分别进行了仿真分析.