基于可变遗忘因子在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,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。     

PCMA自适应自干扰对消算法与仿真

讨论了成对载波多址(PCMA)自适应自干扰对消原理,分析了参数估计误差对自干扰对消的影响。在频偏误差的影响下对消误差为非平稳过程,传统自适应对消滤波器无法收敛。为了克服频偏误差的影响,提出了一种自适应可变遗忘因子(VFF)RLS算法,同时,给出了具有高精度低运算量的模糊函数参数估计算法。仿真证明了VFF-RLS对消算法具有良好的对消性能和稳健性,且需要的额外功率较低,能满足PCMA系统要求。     

基于递推最小二乘算法的小信号检测

受到强干扰影响的小信号通常难于有效检测。在分析递推最小二乘算法(RLS)原理及其几种改进形式的基础上,采用自适应方法将已检测出的大信号与原混叠信号对消,降低大信号对小信号的遮蔽作用,再进行小信号的检测。最后通过仿真证明,该方法能够在较小失真的情况下,有效检测出被大调幅信号干扰下的小调频信号;同时分别比较了各种算法的优劣,得出基于可变遗忘因子的RLS(VFF-RLS)算法不仅具有较快的收敛速度,而且收敛之后具有很好的平稳性能。     

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。     

基于OGCE-BEM的OTFS信道估计

【目的】随着面向第六代移动通信技术研究工作的开展,传统的正交频分复用（OFDM）系统中的载波间干扰使得信道估计性能不足以提供高度可靠的通信,而正交时频空（OTFS）系统可以有效解决快速时变性和多普勒效应导致的通信系统可靠性下降问题,近年来受到了广泛关注。【方法】为了有效满足OTFS系统所需的信道估计性能需求,文章采用优化的广义复指数（OGCE）基扩展模型（BEM）将信道脉冲响应建模为时不变的基函数与基函数系数的形式,从而有效地拟合高速移动通信场景下的快速时变信道。OGCE-BEM通过更加密集的采样改善了频谱泄漏的问题,并且通过增加修正系数降低了高频基模型的误差。为了得到更为精确的基函数系数,文章基于遗忘因子与估计误差的关系,设计了可变遗忘因子的递归最小二乘（RLS）滤波器,使得RLS滤波器可以实时追踪基函数系数的变化。【结果】仿真结果表明,文章所提算法适用于高速移动通信场景,基函数的设计更为合理,相较于固定遗忘因子的估计方法,具有更低的均方误差,信道估计结果更加精确。与最小二乘（LS）、BEM-LS和BEM-线性最小均方差（LMMSE）信道估计方式相比,均方误差性能得到了明显提升。【结...     

一种新的用于UWB RAKE接收机的自适应算法:快速横向滤波算法

本文首次提出将快速横向滤波(FTF)算法引入超宽带(UWB)通信系统的接收机结构中.通过引入遗忘因子对角矩阵,推导了带有遗忘因子的FTF滤波器的递推算法.FTF算法可以自适应地跟踪接收机输入信号的幅度衰减,做出实时地估计.仿真实验表明:FTF算法在运算量、收敛速度和误码率等性能上要优于常用的RLS算法,尤其FTF算法的收敛速度对数据的相关性不敏感,比RLS算法更具有吸引力.     

一种改进RLS算法的性能研究及应用

提出了一种改进的RLS算法,它结合了可变遗忘因子的RLS算法和平方根Kalman 算法的优点,既有可变遗忘因子的RLS算法对时变参数的快速跟踪能力,又有平方根Kalman 算法对设备精度要求低的特点。改进后的RLS算法具有较小的参数估计误差,数值稳定性好,是一种适合工程实现的较优算法,已经在跳频通信中应用实现。     

UFMC系统中的载波频率同步技术

针对UFMC系统对频率偏差敏感的问题,提出一种适合用于UFMC 系统的频率同步算法。为了保留UFMC系统的良好特性、降低UFMC中滤波器的设计复杂度,该算法设计了一种子带间正交的导频序列,并采用非线性最小二乘（NLS）法进行CFO估计。通过序列构造和计算该构造序列与估计信号的相关性对算法进一步改进,使得低信噪比下的估计性能得到改善。理论分析和仿真表明,在高斯和瑞利衰落信道下,子带间正交导频序列的误码率、CFO性能均优于全1导频序列;在低信噪比环境下,改进算法的CFO性能优于NLS算法。     

基于可变遗忘因子RLS的Boost转换器在线多参数辨识北大核心CSCD

针对Boost转换器控制性能受电感和电容变化影响的问题,提出了一种基于可变遗忘因子递推最小二乘法(recursive least squares method,RLS)的在线多参数辨识算法.考虑电感电流纹波,推导了精确的电感和电容辨识模型.在此基础上,研究了RLS算法中遗忘因子动态取值问题.通过在算法的误差信号中恢复系统噪声的方法,动态计算遗忘因子的取值,解决了传统RLS算法难以兼顾稳态精度和参数跟踪能力的问题.仿真结果表明,该算法可以在动态条件下,精确且快速地跟踪电感和电容值的变化,且具有良好的鲁棒性.     

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。     

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.     

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.     

RLS-Based Joint Estimation and Tracking of Channel Response, Sampling, and Carrier Frequency Offsets for OFDM

This paper proposes a pilot-aided joint channel estimation and synchronization scheme for burst-mode orthogonal frequency division multiplexing (OFDM) systems. Based on the received signal samples containing pilot tones in the frequency domain, a cost function that includes the carrier frequency offset (CFO), sampling clock frequency offset (SFO) and channel impulse response (CIR) coefficients is formulated and used to develop an accompanying recursive least-squares (RLS) estimation and tracking algorithm. By estimating the channel CIR coefficients instead of the channel frequency response in the frequency domain, the proposed scheme eliminates the need for an IFFT block while reducing the number of parameters to be estimated, leading to lower complexity without sacrificing performance and convergence speed. Furthermore, a simple maximum-likelihood (ML) scheme based on the use of two long training symbols (in the preamble) is developed for the coarse estimation of the initial CFO and SFO to suppress dominant ICI effects introduced by CFO and SFO and to enhance the performance and convergence of the fine RLS estimation and tracking. Simulation results demonstrate that, over large ranges of CFO and SFO values, the proposed pilot-aided joint channel estimation and synchronization scheme provides a receiver performance that is remarkably close to the ideal case of perfect channel estimation and synchronization in both AWGN and Rayleigh multipath fading channels.      

Study of the transient phase of the forgetting factor RLS

We investigate the convergence properties of the forgetting factor RLS algorithm in a stationary data environment. Using the settling time as our performance measure, we show that the algorithm exhibits a variable performance that depends on the particular combination of the initialization and noise level. Specifically when the observation noise level is low (high SNR) RLS, when initialized with a matrix of small norm, it has an exceptionally fast convergence. Convergence speed decreases as we increase the norm of the initialization matrix. In a medium SNR environment, the optimum convergence speed of the algorithm is reduced as compared with the previous case; however, RLS becomes more insensitive to initialization. Finally, in a low SNR environment, we show that it is preferable to initialize the algorithm with a matrix of large norm     

RLS-Based Estimation and Tracking of Frequency Offset and Channel Coefficients in MIMO-OFDM Systems

In this paper, we investigate the problem of carrier frequency-offset (CFO) synchronization and channel estimation in multiple-input multiple-output orthogonal frequency-division multiplexing systems operating over unknown frequency-selective fading channels. We first propose a novel joint CFO and channel estimator, assuming time-domain training blocks are available. The proposed joint estimator consists of two recursive least-square (RLS) algorithms which iterate their estimated CFO and CIR values. We then derive a more precise pilot-aided RLS algorithm to estimate the residual frequency synchronization errors or track small CFO changes. With this, the accuracy of channel estimation is also enhanced. The analysis and simulation results show that, the proposed estimation and tracking scheme which is fully compatible with the existing standards is able to attain fast convergence, high stability, and ideal performances as compared with relevant Cramer–Rao bounds in all ranges of signal-to-noise ratio. Moreover, it can work well for wide tracking range up to ±0.5 of the subcarrier spacing.     

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.     

一种自适应可变遗忘因子DCD-RLS算法

RLS最终的问题可以归结为正规方程的求解。解决正规方程可以采用非线性搜索的方法。为了降低复杂度可以采用二分坐标下降法。可变遗忘因子对算法有影响。可变遗忘因子的设置是通过时间平均误差相关来自适应调节。相对于其他可变遗忘因子的设置方法它的复杂度很低。通过仿真结果发现此算法与传统的算法的性能差不多。     

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.