A novel variable step size adjustment method based on channel output autocorrelation for the LMS training algorithm

The least mean squares (LMS) algorithm, the most commonly used channel estimation and equalization technique, converges very slowly. The convergence rate of the LMS algorithm is quite sensitive to the adjustment of the step‐size parameter used in the update equation. Therefore, many studies have concentrated on adjusting the step‐size parameter in order to improve the training speed and accuracy of the LMS algorithm. A novel approach in adjusting the step size of the LMS algorithm using the channel output autocorrelation (COA) has been proposed for application to unknown channel estimation or equalization in low‐SNR in this paper. Computer simulations have been performed to illustrate the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/1 standard have demonstrated that the proposed variable step size LMS (VSS‐LMS) algorithm has considerably better performance than conventional LMS, recursive least squares (RLS), normalized LMS (N‐LMS) and the other VSS‐LMS algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

非高斯噪声下基于Wilcoxon范数的变步长符号扩散式仿射投影算法

扩散式仿射投影算法(DAPA)是实现分布式网络参数自适应估计的一种重要方法,该算法在输入信号存在相关性时仍快速收敛,但抑制具有脉冲特性的非高斯噪声能力弱,且固定步长对收敛性有所限制。为此,该文提出了基于Wilcoxon范数的变步长符号扩散式仿射投影算法(VSS-DWAPA)。首先,引入稳健估计理论中抗异常值能力强的Wilcoxon范数作为代价函数并根据其取值特点进行了符号量化,推导出了新的迭代方程;其次,针对固定步长的局限性,采用迭代方式实现了误差信号对步长的控制,在初始阶段和接近收敛阶段选择不同的步长,使算法具有更好的适应性。仿真结果表明,在非高斯噪声下本文的VSS-DWAPA算法在收敛性、跟踪性等方面均优于现有一些扩散式自适应滤波算法,同时在高斯噪声环境下也具有较好的性能。     

MC-CDMA上行链路盲多用户检测技术研究

文中提出一种在异步多径瑞利衰落MC-CDMA系统上行链路中改进型的仿射投影算法。利用盲信号空间分离的方法,将期望用户的信道信息估计出来,再将其结果作为参数带入到改进型仿射投影滤波器中。在权向量更新的过程中,抵抗多址干扰（MAI）的影响,将期望用户的信息可靠的恢复。计算机仿真显示,在考虑远近效应时,该盲算法的性能与非盲的最小均方误差接收器的性能接近。     

自适应扩散算法的误差性能分析

为了分析分布式协作频谱感知认知网络中融合矩阵、步长等参数对自适应扩散算法的影响,对检测量估计误差性能进行了研究。给出了自适应扩散算法检测量的通用迭代公式,分析了检测算法的数据处理流程;推导了检测量估计误差向量的迭代表达式,利用网络瞬时均方偏差性能和各节点的稳态均方偏差性能评价融合矩阵参数对算法的影响。结果表明,不同的融合矩阵选取原则影响算法的检测性能,可以采用检测量误差估计的方法对算法参数进行研究。     

Compressed sensing channel estimation for STBC‐SM based hybrid MIMO‐OFDM system for visible light communication

This paper presents the idea of sparse channel estimation using compressed sensing (CS) method for space–time block coding (STBC), and spatially multiplexing (SM) derived hybrid multiple‐input multiple‐output (MIMO) Asymmetrically clipped optical‐orthogonal frequency division multiplexing (ACO‐OFDM) optical wireless communication system. This hybrid system accounts multiplexing gain of SM and diversity gain of STBC technique. We present a new variant of sparsity adaptive matching pursuit (SaMP) algorithm called dynamic step‐size SaMP (DSS‐SaMP) algorithm. It makes use of the inherent and implicit structure of SaMP, along with dynamic adaptivity of step‐size feature which is compatible with the energy of the input signal, thus the name dynamic step size. Existing CS‐based recovery algorithms like orthogonal matching pursuit, SaMP, adaptive step‐size SaMP, and proposed DSS‐SaMP were compared for hybrid MIMO‐ACO‐OFDM visible light communication system. The performance analysis is demonstrated through simulation results with respect to bit error rate, symbol error rate, mean square error, computational complexity, and peak‐to‐average power ratio. Simulation results show that the proposed technique gives improved performance and lesser computational complexity in comparison with conventional estimation algorithms.     

基于M估计变步长自适应仿射投影算法的稳健时延估计

该文提出了一种基于M估计变步长自适应仿射投影方法的稳健时延估计(TDE)算法。该算法将自适应仿射投影算法应用于时延估计,无须事先假定信号和噪声的统计特性,自适应调整自身参数;应用稳健M估计理论,抵消重尾噪声干扰。数值仿真表明,在高斯噪声、非高斯噪声甚至冲激噪声的干扰下,该文算法比高阶统计量法和最小均方自适应法有更强的稳健性和更高的估计精度。     

Reduced‐rank space–time adaptive interference suppression for navigation systems based on QR decomposition and approximations of basis functions

This paper presents a novel reduced‐rank space–time adaptive processing (STAP) algorithm for interference suppression in global positioning system (GPS) receivers with low computational complexity for protection against the multipath and jamming interferences. The proposed STAP algorithm is based on the least‐squares (LS) criterion to jointly optimize a projection matrix, which is used for dimensionality reduction, and the reduced‐rank filter. The main novelties are the design of the projection matrix based on approximations of basis functions, the pattern matching between the projection matrix and the received data, and the derivation of a QR decomposition‐based reduced‐rank recursive LS algorithm for practical implementations. The proposed scheme works on an instantaneous basis, i.e. at each time instant, the most suitable pattern and the rank of the projection matrix are selected to reduce the dimensionality of the received data aiming at minimizing the squared error, while using an improved search algorithm to save the effort in finding the best projection matrix. Simulation results in a GPS system show that compared to existing reduced‐rank and full‐rank algorithms, the proposed algorithm has a much lower computational complexity, and remarkably better performance for interference suppression. Copyright © 2011 John Wiley & Sons, Ltd.     

抗脉冲干扰的分布式仿射投影符号算法

递增式和扩散式仿射投影算法收敛较快,但在脉冲噪声环境下这两种分布式估计算法收敛性较差或容易发散.本文采用受网络节点的权值向量更新约束的后验误差向量?₁范数最小化方法,提出了两种抗脉冲干扰的分布式估计算法,即递增式和扩散式仿射投影符号算法.仿真结果表明,与分布式仿射投影算法相比,分布式仿射投影符号算法在脉冲噪声环境下具有更好的鲁棒性.     

一种基于误差相关函数的改进变步长LMS算法

最小均方算法的收敛速度和稳态误差之间存在矛盾,为此人们提出了各种变步长LMS算法,其中E-LMS算法是将步长与瞬时误差平方相关联,R-LMS算法是将步长与误差的相关函数相关联。E-LMS算法的抗噪性能较差,在低信噪比条件下性能明显变差,R-LMS算法对突变系统的跟踪能力较差。为此文中给出了一种改进的,基于误差相关函数的VSS-LMS算法,该方法利用E-LMS算法的控制步长策略提高算法的跟踪能力。计算机仿真结果显示,该算法能够同时满足抗噪和跟踪两种要求。     

Hybrid channel estimation for MIMO relay systems with Doppler offset influences

The design of the channel estimation method in a multiple‐input multiple‐output (MIMO) relay system plays a highly crucial role in deciding the overall system performance. For the realistic scenarios specifically, with fast time‐varying channel conditions due to highly mobile communicating nodes, the degree of accuracy to which the channel estimates are obtained for MIMO relay systems influences the communication system reliability significantly. However, most of the channel estimation approaches proposed in literature for MIMO relay systems assume that the Doppler offset contributed by highly mobile nodes is already known to the receiver, ignoring the resulting nonlinear system dynamics. Hence, a novel hybrid algorithm is proposed to address the issue of time‐varying channel estimation under fast‐fading channel condition with Doppler offset influences contributed by high‐mobility communicating nodes for a 1‐way 2‐hop MIMO amplify‐and‐forward relaying system. The problem is first formulated as a nonlinear state‐space model, and then an algorithm is developed to estimate the individual source‐to‐relay and relay‐to‐destination channels in the presence of the associated dynamic Doppler offset. In the proposed method, a set of superimposed orthogonal pilots is used for aiding in the updation of the channel gains, since Kalman filter–based updation may lead to accumulation of estimation and prediction error. A detailed computational complexity analysis of the proposed hybrid algorithm is presented, which shows that the algorithm has moderate computational complexity with a good performance in fast time‐varying channel conditions with high node mobility in a dual‐hop MIMO relay system.     

Acoustic feedback cancellation based on weighted adaptive projection subgradient method in hearing aids

Acoustic feedback is an important factor that degrades the overall performance of hearing aids, and acoustic feedback cancellation has always been the research focus in the field of signal processing in hearing aids. The newly suggested adaptive projection subgradient method (APSM) for adaptive signal processing solves the problem of difficulty in finding the exact projection operator in the realization of affine projection by taking the subgradient projection hyperplane as the searching region for relaxed projection. This work applies APSM in the acoustic feedback cancellation system of hearing aids for the first time, and proposes a weighted adaptive projection subgradient method (WAPSM), which takes into consideration the exponential decay weight factor to incorporate the prior information of estimation system. The new method is compared with the traditional NLMS algorithm and APSM algorithm in simulation experiments. Incorporating the prior information of estimation system by setting the proper weighting matrix, WAPSM achieved notable improvements on the speed, stability and accuracy of the misalignment convergence. Numerical experiments demonstrate that the proposed algorithm is more robust for low SNR and real speech segment input than the traditional algorithms.     

A new evolving-update-based feedback cancellation scheme for hearing aids

This paper proposes an efficient adaptive feedback canceller (AFC) for hearing aid, which provides satisfactory performance both in sparse and in dispersive conditions as well as can adapt according to the variations in the sparseness level of the feedback path for coloured signal as input. This is achieved by incorporating the measure of sparseness intensity and the variable step size to the memory-improved proportionate affine projection algorithm (MIPAPA), and hence, an improved MIPAPA (IMIPAPA) is proposed. Further, in order to reduce the computations incurred by the AFC, an evolving-update IMIPAPA (E-IMIPAPA) is introduced, employing an intermittent update of taps of the adaptive filter by simultaneously adjusting the update interval. The proposed E-IMIPAPA is applied to the two-microphone-based AFC. The results of simulation-based experiments show the effectiveness of the proposed algorithm as compared to the existing methods for feedback cancellation in hearing aid in terms of misalignment and added stable gain. The proposed AFC model is further extended to the multiple-microphone and single-speaker set-up.     

A two-stage affine projection algorithm with mean-square-error-matching step-sizes

This paper proposes a two-stage affine projection algorithm (APA) with different projection orders and step-sizes. The proposed algorithm has a high projection order and a fixed step-size to achieve fast convergence rate at the first stage and a low projection order and a variable step-size to achieve small steady-state estimation errors at the second stage. The stage transition moment from the first to the second stage is determined by examining, from a stochastic point of view, whether the current error reaches the steady-state value. Moreover, in order to prevent the sudden drop of convergence rate on switching from a high projection order to a low projection order, a matching step-size method has been introduced to determine the initial step-size of the second stage by matching the mean-square errors (MSEs) before and after the transition moment. In order to continuously reduce steady-state estimation errors, the proposed algorithm adjusts the step-size of the second stage by employing a simple algorithm. Because of the reduced projection orders and variable step-size in the steady-state, the algorithm achieves improved performance as well as extremely low computational complexity as compared to the existing APAs with selective input vectors and APAs with variable step-size.     

基于可变阶数的集员滤波仿射投影算法

为了解决传统集员滤波仿射投影(SM-AP)算法收敛速度与稳态失调和计量复杂度之间的矛盾,提出一种新的数据选择性仿射投影算法。此算法在传统SM-AP算法的基础上,引入可变阶数(也称数据重用因子),称为基于可变数据重用因子的集员滤波仿射投影(VDRF-SM-AP)算法。通过利用步长提供的信息,此算法可以自动地分配数据重用因子,实现了在初始阶段数据重用因子大,收敛后数据重用因子小的目标,从而既保证了收敛速度又降低了稳态失调。通过理论分析和仿真验证,新算法的整体复杂度比其他传统的SM-AP算法低很多,同时保留了传统的SM-AP算法的快速收敛特性,但是却能达到更小的稳态失调。     

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton's method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.     

A hybrid joint ML technique for CFO,timing offset,and channel estimations in an OFDMA uplink

In this paper, we propose a novel hybrid joint maximum‐likelihood estimator for carrier frequency offset (CFO), timing offset, and channel response of all users in the uplink of an orthogonal frequency division multiple access (OFDMA) system. The proposed estimation method significantly reduces complexity of this multiparameter, multidimensional optimization problem, using the concept of separation of the different user signals by means of newly defined projection operators. This projection technique is combined with the alternating projection method available in the literature to arrive at a new hybrid algorithm that offers significant performance advantages in terms of computational complexity and estimator performance. The joint estimation of the CFOs, timing offsets, and channel coefficients for all active users together at the base station of the OFDMA uplink is a rarely addressed task. The proposed method also offers the flexibility of application to any subcarrier assignment scheme used in OFDMA systems. Extensive simulation studies corroborate the advantages of the new hybrid method for all three estimation requirements in the multiuser OFDMA uplink. Copyright © 2012 John Wiley & Sons, Ltd.     

Variable Step Size LMS Algorithm Based on Function Control

This paper proposes a function-controlled variable step size least mean square (VSLMS) algorithm for channel estimation in low-SNR or colored input signals. The proposed method aligns the step size update with the steady-state error and alleviates the impact of high-level noise. It improves the filter performance in terms of fast convergence rate and low misadjustment error. Simulation results demonstrate the effectiveness and verify the theoretic analysis of the proposed VSLMS algorithm.     

Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification

In this paper, an improved sparse-aware affine projection (AP) algorithm for sparse system identification is proposed and investigated. The proposed sparse AP algorithm is realized by integrating a non-uniform norm constraint into the cost function of the conventional AP algorithm, which can provide a zero attracting on the filter coefficients according to the value of each filter coefficient. Low complexity is obtained by using a linear function instead of the reweighting term in the modified AP algorithm to further improve the performance of the proposed sparse AP algorithm. The simulation results demonstrate that the proposed sparse AP algorithm outperforms the conventional AP and previously reported sparse-aware AP algorithms in terms of both convergence speed and steady-state error when the system is sparse.     

Mean-square deviation analysis of the zero-attracting variable step-size LMS algorithm

The well-known variable step-size least-mean-square (VSSLMS) algorithm provides faster convergence rate while maintaining lower mean-square error than the conventional LMS algorithm. The performance of the VSSLMS algorithm can be improved further in a channel estimation problem if the impulse response of the channel is sparse. Recently, a zero-attracting (ZA)-VSSLMS algorithm was proposed to exploit the sparsity of a channel. This was done by imposing an \(\ell _1\)-norm penalty to the original cost function of the VSSLMS algorithm which utilizes the sparsity in the filter taps during the adaptation process. In this paper, we present the mean-square deviation (MSD) analysis of the ZA-VSSLMS algorithm. A steady-state MSD expression for the ZA-VSSLMS algorithm is derived. An upper bound of the zero-attractor controller (\(\rho \)) that provides the minimum MSD is also provided. Moreover, the effect of the noise distribution on the MSD performance is shown theoretically. It is shown that the theoretical and simulation results of the algorithm are in good agreement with a wide range of parameters, different channel, input signal, and noise types.     

Edge‐Preserving Algorithm for Block Artifact Reduction and Its Pipelined Architecture

This paper presents a new edge‐protection algorithm and its very large scale integration (VLSI) architecture for block artifact reduction. Unlike previous approaches using block classification, our algorithm utilizes pixel classification to categorize each pixel into one of two classes, namely smooth region and edge region, which are described by the edge‐protection maps. Based on these maps, a two‐step adaptive filter which includes offset filtering and edge‐preserving filtering is used to remove block artifacts. A pipelined VLSI architecture of the proposed deblocking algorithm for HD video processing is also presented in this paper. A memory‐reduced architecture for a block buffer is used to optimize memory usage. The architecture of the proposed deblocking filter is verified on FPGA Cyclone II and implemented using the ANAM 0.25 µm CMOS cell library. Our experimental results show that our proposed algorithm effectively reduces block artifacts while preserving the details. The PSNR performance of our algorithm using pixel classification is better than that of previous algorithms using block classification.