Echo Cancellation of Voiceband Data Signals Using Recursive Least Squares and Stochastic Gradient Algorithms

The convergence properties of adaptive least squares (LS) and stochastic gradient (SG) algorithms are studied in the context of echo cancellation of voiceband data signals. The algorithms considered are the SG transversal, SG lattice, LS transversal (fast Kalman), and LS lattice. It is shown that for the channel estimation problem considered here, LS algorithms converge in approximately2Niterations whereNis the order of the filter. In contrast, both SG algorithms display inferior convergence properties due to their reliance upon statistical averages. Simulations are presented to verify this result, and indicate that the fast Kalman algorithm frequently displays numerical instability which can be circumvented by using the lattice structure. Finally, the equivalence between an LS algorithm and a fast converging modified SG algorithm which uses a maximum length input data sequence is shown.     

Givens rotation based least squares lattice and related algorithms

The author presents a general and systematic approach for deriving new LS (least squares) estimation algorithms that are based solely on Givens rotations. In particular, this approach is used to derive efficient Givens-rotation-based LS lattice algorithms-the Givens-lattice algorithms. By exploiting the relationship between the Givens algorithms and the recursive modified Gram-Schmidt algorithm, it is shown that the time and order update of any order-recursive LS estimation algorithm can be realized by employing only Givens rotations. Applying this general conclusion to LS estimation of time-series signals results in the Givens-lattice algorithms. Two Givens-lattice algorithms, one with square roots and the other without, are presented. It is shown that the Givens-lattice algorithms are computationally more efficient than the fast QR algorithm of Cioffi (1987). The derivation of other Givens rotation-based LS estimation algorithms and their systolic array implementations are discussed     

基于WLSE-KF的传感器融合定位算法研究

多传感器融合在定位中的应用越来越广泛。在利用这些传感器进行定位的过程中,需要对其采集的数据进行融合。射频识别融合定位一般采用最小二乘法,然而,它可能使定位误差较大。文中提出了将最小二乘和卡尔曼滤波相结合的算法。该算法先利用加权最小二乘估计获得移动用户的初步位置,再利用扩展卡尔曼滤波进一步使定位精度得到提高。仿真结果表明该算法相比多种传统定位算法,误差减少,定位精度明显提高。     

Domain-overriding and digital filtering for 3-D FDTD subgridded simulations

We report on an extension of finite-difference time-domain (FDTD) subgridding (SG) algorithms incorporating digital filters and domain-overriding to three-dimensional (3-D) simulations and to problems involving materials traversing the SG interfaces. We show that significant improvements in accuracy can be obtained for these cases as well.     

Optimisation of sensing time and transmission time in cognitive radio-based smart grid networks

Cognitive radio (CR)-based smart grid (SG) networks have been widely recognised as emerging communication paradigms in power grids. However, a sufficient spectrum resource and reliability are two major challenges for real-time applications in CR-based SG networks. In this article, we study the traffic data collection problem. Based on the two-stage power pricing model, the power price is associated with the efficient received traffic data in a metre data management system (MDMS). In order to minimise the system power price, a wideband hybrid access strategy is proposed and analysed, to share the spectrum between the SG nodes and CR networks. The sensing time and transmission time are jointly optimised, while both the interference to primary users and the spectrum opportunity loss of secondary users are considered. Two algorithms are proposed to solve the joint optimisation problem. Simulation results show that the proposed joint optimisation algorithms outperform the fixed parameters (sensing time and transmission time) algorithms, and the power cost is reduced efficiently.     

Least Squares Identification for Hammerstein Multi-input Multi-output Systems Based on the Key-Term Separation Technique

System modeling and parameter estimation are basic for system analysis and controller design. This paper considers the parameter identification problem of a Hammerstein multi-input multi-output (H-MIMO) system. In order to avoid the product terms in the identification model, we derive a pseudo-linear identification model of the H-MIMO system through separating a key term from the output equation of the system and present a hierarchical generalized least squares (LS) algorithm for estimating the parameters of the system. Moreover, we present a new LS algorithm to reduce the computational burden. The proposed algorithms are simple in principle and can achieve a higher computational efficiency than the over-parameterization-based LS estimation algorithm. Finally, we test the proposed algorithms by the simulation example and show their effectiveness.     

NB-IoT系统基于导频信道估计算法研究

NB-IoT(Narrow Band Internet of Things)是一种基于蜂窝的窄带物联网技术,该技术如今发展非常迅速,为了协助终端更好地解调出发射信号,在现有的LS算法和基于DFT的LS算法基础上进行改进,设计了基于阀值的变换域信道估计算法,并且通过MATLAB对以上三种算法的BER(误码率)进行仿真和验证,最终仿真结果显示改进算法的性能优于另外两种算法.     

Low-complexity adaptive step size constrained constant modulus SG algorithms for adaptive beamforming

In this paper, we propose two low-complexity adaptive step size mechanisms to enhance the performance of stochastic gradient (SG) algorithms for adaptive beamforming. The beamformer is designed according to the constrained constant modulus (CCM) criterion and the proposed mechanisms are employed in the SG algorithm for implementation. A complexity comparison is provided to show their advantages over existing methods, and a sufficient condition for the convergence of the mean weight vector is established. Theoretical expressions of the excess mean-squared error (EMSE), in both the steady-state and tracking cases, are derived based on the energy conservation approach. The effects of multiple access interference (MAI) and additive noise are considered. Simulation experiments are presented for both the stationary and non-stationary scenarios, illustrating that the proposed algorithms achieve superior performance compared with existing methods, and verifying the accuracy of the analyses.     

Bias compensation based recursive least-squares identification algorithm for MISO systems

For multi-input single-output output-error systems, the least-squares (LS) estimates are biased. In order to obtain the unbiased estimates, we present a recursive LS identification algorithm based on a bias compensation technique. The basic idea is to eliminate the estimation bias by adding a correction term in the LS estimates, and further to derive a bias compensation based recursive LS algorithm. Finally, we test the proposed algorithms by simulation and show their effectiveness.     

Comparison of robust and least-squares regression incomputer-generated probability plots

ML-estimators (maximum likelihood) offer a promising alternative to least squares (LS) methods and the best-guess of a researcher when fitting a line to failure-time data plotted on a computer-generated probability plot. In terms or model statistics, these robust regression algorithms performed better than LS in most data sets. The Andrews function and the Ramsay function always performed better than LS. The Huber function and the Hampel function usually performed better than LS except for those data sets where the residuals did not exceed the threshold criteria and all residuals were assigned a weight of 1.0. In those situations, these two ML-estimators provided results which were equivalent to LS. The ML-estimators were particularly effective in situations involving near-neighbors in the low region of the x-space (early contiguous failures). In terms of parameter estimation, there was no noticeable difference between LS and the ML-estimators     

一种高效的用于RF功率放大器线性化的自适应预失真结构

分析了当前文献中主要的几种自适应预失真结构,发现这些自适应预失真结构均不利于高效最小二乘算法的直接应用,从而限制了预失真技术的自适应性能.提出了一种新的自适应预失真结构,可直接使用高效的最小二乘算法对预失真器进行自适应更新.仿真结果表明利用此结构可快速、高效地实现非线性RF功率放大器的线性化.     

Parameter Identification Methods for an Additive Nonlinear System

This paper deals with parameter identification methods for an additive nonlinear system with a preload nonlinearity and a piece-wise nonlinearity. By using a switching function, we transfer the model of the additive nonlinear system into an identification model, and propose a recursive least squares algorithm and two modified stochastic gradient (SG) algorithms to estimate the parameters of the identification model. The simulation results indicate that the proposed methods converge faster than the SG algorithm.     

约束和非约束频域分组LMS自适应滤波器的快速算法

本文通过将约束和非约束频域分组LMS算法的权值迭代方程转换成时域方程,利用最小二乘法(LS)分别选取最优时变步长收敛因子,得到频域最佳分组算法(FOBA)和非约束频域最佳分组算法(UFOBA),虽然增加了计算量,但计算机仿真结果表明:提高了收敛速度和精度,算法稳定可靠。     

Adaptive Spread-Spectrum Systems Using Least-Squares Lattice Algorithms

Practical communication systems must cope with many uncertainties in addition to determining the transmitted data, e.g., the direction, timing, and distortion of the desired signal, and the spectral and spatial distribution of the interference, all of which may change with time. This paper describes exact least-squares (LS) recursive lattice algorithms which resolve these uncertainties in a direct-sequence spread-spectrum digital communication system. The adaptive LS algorithm is recursive beth in order and time, and converges rapidly to the uncertain parameters. Time-discrete algorithms may be mechanized by a receiver containing integrate-and-dump circuits operating at the chip rate of the pseudonoise (PN) sequence, one in each in-phase and each quadrature channel of each sensor array element's output. Different configurations of optimal time-discrete receivers are presented and transformed into adaptive receivers by taking advantage of the spectral properties of the different kinds of LS filters. Simulation results are presented and some guide lines are given for the architecture of an adaptive direct-sequence spread-spectrum system.     

Joint Power Allocation and Interference Mitigation Techniques for Cooperative Spread Spectrum Systems with Multiple Relays

This paper presents joint power allocation and interference mitigation techniques for the downlink of spread spectrum systems which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to an individual power constraint and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problem efficiently, we develop joint adaptive power allocation and interference suppression algorithms that can be implemented in a distributed fashion. The proposed stochastic gradient (SG) and recursive least squares (RLS) algorithms mitigate the interference by adjusting the power levels across the relays and estimating the parameters of the linear receiver. SG and RLS channel estimation algorithms are also derived to determine the coefficients of the channels across the base station, the relays and the destination terminal. The results of simulations show that the proposed techniques obtain significant gains in performance and capacity over non-cooperative systems and cooperative schemes with equal power allocation.     

Turbo编码MIMO-OFDM系统中的EM迭代信道估计

该文对频率选择性衰落信道条件下的Turbo编码多入多出正交频分复用(MIMO-OFDM)系统的信道估计进行了研究,提出了一种将Turbo迭代译码与EM迭代信道估计相结合的方法。采用该方法的EM迭代信道估计性能得到了明显提高,且适合于非常恶劣的信道环境。另外,通过仿真可知,利用EM迭代信道估计算法的系统性能优于利用简化的最小二乘(LS)信道估计算法所获得的系统性能。     

Genetic Grey Wolf Optimizer Based Channel Estimation in Wireless Communication System

Various methods are available for channel estimation in the orthogonal frequency division multiplexing and orthogonal frequency and code division multiplexing (OFCDM) based wireless communication schemes. Along with this, the most utilized techniques are namely the minimum mean square error (MMSE) and least square (LS). The process of LS channel estimation method is simple but it occupies a very high mean square error. On the other hand, the performance of MMSE is better than LS in terms of SNR, though it shows high computational complexity. Compared to MMSE and LS based techniques, the combination of MMSE and LS techniques using evolutionary programming reduces the error significantly to receive exact signal. In this study, we propose a hybrid method namely GGWO that includes grey wolf optimization (GWO) and genetic algorithms (GA) for estimate the channel in MIMO–OFCDM schemes. At first, the best channel is estimated using GWO and afterwards, the MMSE and LS are hybridized through GA for calculating the best channel to decrease error. Overall, the GWO and GA contribute in fine tuning the obtained channel scheme so that the channel model is derived further to correlate with the ideal scheme. Our results demonstrate that the proposed scheme is superior to conventional MMSE and LS in terms of BER and SNR.     

伪叠加Zadoff-Chu序列的LTE下行信道估计

分析了正交频分复用系统中基于叠加训练序列的最小二乘和线性最小均方误差信道估计算法,在此基础上针对LTE信道的结构特征,提出了一种利用伪叠加Zadoff-Chu序列进行信道估计的最小二乘算法。该方法可以大大降低算法复杂度,并且不需要信道先验信息、不占用带宽资源。仿真结果验证了该方法的有效性,其性能比频域的离散傅里叶变换算法和线性最小均方误差算法性能更优。     

In-band least-squares echo canceller

A least-squares echo canceller algorithm which exploits the algebraic properties of binary pseudorandom maximal length sequences is applied to an in-band canceller without the requirement for a Hilbert transformer. Its performance is compared with least-mean-squares (LMS) and other least-squares (LS) algorithms.     

Blind channel estimation using the second-order statistics:asymptotic performance and limitations

We consider the asymptotic performance and fundamental limitations of the class of blind estimators that use second-order statistics. An achievable lower bound of the asymptotic normalized mean-square error (ANMSE) is derived. It is shown that the achievable ANMSE is lower bounded by the condition number of the Jacobian matrix of the correlation function with respect to the channel parameters. It is shown next that the Jacobian matrix is singular if and only if the subchannels share common conjugate reciprocal zeros. This condition is different from the existing channel identification conditions. Asymptotic performance of some existing eigenstructure-based algorithms is analyzed. Closed-form expressions of ANMSE and their lower bounds are derived for the least-squares (LS) and the subspace (SS) blind channel estimators when there are two subchannels. Asymptotic efficiency of LS/SS algorithms is also evaluated, showing that significant performance improvement is possible when the information of the source correlation is exploited