宽带卫星信道群时延的自适应均衡校正研究

在宽带卫星通信链路中,由于器件通带特性不理想和行波管放大器的工艺受限等原因使卫星信道的群时延波动较大,导致误比特性能恶化。群时延的补偿算法需要复杂的数学运算,因此工程上一般采用线性均衡对信道群时延特性进行校正。针对群时延失真严重情况下线性均衡效果下降的问题,对比研究了线性均衡、非线性均衡对信道群时延校正的性能,仿真分析了采用恒模算法的线性均衡和采用voherra模型的非线性均衡在群时延失真信道下的性能,得出了两类均衡器在群时延失真信道下的误码率性能曲线,结果表明低阶调制下采用非线性均衡可以较好的消除宽带卫星信道群时延的影响。     

无方根广义多道Givens格型算法及其在自适应非线性均衡中的应用

对于自适应非线性均衡问题，当非线性信道的传输函数可由二阶Ｖｏｌｔｅｒｒａ级数表征时，可以作为广义多道滤波问题来处理。本文提出了一种无方根广义多道Ｇｉｖｅｎｓ格型算法来实现该非线性信道均衡器，研究了该均衡器消除由于信道非线性衰落引起的码间干扰的性能。实验结果表明：该自适应非线性均衡器具有快速收敛性能，对非线性信道的均衡效果优于ＬＳ格型判决反馈均衡器。     

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.     

CPSN在信道均衡中的应用

信道均衡是用来消除码间串扰、对信道畸变进行补偿,从而在接收端正确的重建发送信号的滤波方法。由于信道均衡可以看作一个模式分类问题,恰恰神经网络具有良好的模式分类特征,因此针对复信道可采用CPSN进行二进制自适应信道均衡。仿真结果表明,该算法对于严重码间串扰及适度非线性畸变的复信道效果良好。     

非线性卫星信道下的粒子滤波盲均衡方法

针对卫星信道中高功率放大器产生非线性失真的问题,本文提出了一种基于粒子滤波技术的盲均衡法。该算法优势在于不需要对非线性信道线性化处理,而是利用带权值的离散随机样本点来对期望分布进行近似,通过将非线性模型建模成状态空间模型,对信道参数进行跟踪和符号序列估计。仿真结果表明,算法实现了对放大器非线性幅度和相位特性的粒子滤波估计,并对符号序列进行了盲恢复,在误比特率为 较截断Volterra均衡有1.5 dB左右的性能增益;通过增加粒子数目和平滑长度能一定程度上提高算法性能,但在复杂度与性能折中考虑下,不能无限增加粒子数目。      

卫星信道MMSE和LS均衡算法性能比较

针对卫星信道采用OFDM系统模型,结合实际情况,在信道中加入多径干扰和多普勒频偏,为更好地消除子载波间干扰和码间干扰,在解调之后采用均衡算法。文中选用MMSE和LS均衡算法进行研究,得出两种算法均衡性能的差异,并指出了均衡算法的研究方向。     

无人飞行器通信中联合信道估计和Turbo均衡

Turbo均衡技术能有效克服信道多径、消除符号间干扰(ISI),从而提高接收机整体性能。针对无人飞行器地空视距链路信道这种快时变多径信道,提出了一种联合信道估计和Turbo均衡的迭代接收算法。该算法采用单载波调制体制,通过信道估计和Turbo均衡之间相互反馈软信息来消除ISI。Matlab仿真结果表明,经过2次以上的迭代后,相比较于传统的单载波频域Turbo均衡算法,新算法的误码率性能得到了显著改善。     

FMT系统中的迭代信道估计和Turbo均衡

在滤波多音调制(FMT)系统中,为了消除由于原型滤波器非理想重构特性和多径信道所导致的符号间干扰,提出了一种联合迭代信道估计和Turbo均衡的FMT系统接收方法,通过对FMT系统中每个子信道的等效冲激响应进行迭代估计,然后采用基于线性滤波器结构的Turbo均衡器来消除符号间干扰.仿真结果表明,不论采用QPSK调制方式还是16QAM调制方式,经过2次以上的迭代后,相比较于传统的判决反馈均衡算法,新算法的误码率性能得到了大大改善.     

引入直接序列扩频的线列阵通信信道均衡技术

在空间多径信道垂直线列阵通信中,由于多阵元码间干扰导致信道失衡,误码率较高,需要进行信道均衡设计。传统方法采用随机码扩频方法进行通信信道均衡,扩频信号经高放和混频后导致非线性失真,均衡效果不佳。文中提出一种基于直接序列扩频的线列阵通信信道均衡算法。基于PTRM技术构建了垂直线列阵通信的空间多径信道模型,采用直接序列扩频方法设计码间干扰抑制算法,利用垂直线列阵结构对PTRM时间压缩性能,接收到的扩频信号经高放和混频后,对中频扩频的调制信号进行相关解扩,重组多垂直线列阵的径分量,实现信道均衡。仿真结果表明,采用该算法进行垂直线列阵通信,信道均衡性能和码间干扰抑制性能较好,并有效降低了误比特率,改善了通信质量。     

瑞利衰落信道下的Turbo均衡

Turbo均衡是一种将Turbo原理和均衡技术结合起来的技术。他通过反复均衡和信道译码来提高接收机性能。针时瑞利衰落信道，采用基于线性滤波器的软输入／软输出均衡器来消除码间干扰，其系数由最小均方误差准则确定。译码器采用最大后验概率算法时卷积码译码。考虑到瑞利衰落信道为随机信道，用非相干检测时信道进行估计。接收机通过联合均衡和译码以充分利用已经获得的信息，实现信道估计及信道均衡与信道译码的迭代更新。仿真结果表明其性能不仅远远优于非迭代系统．而且在信噪比高于4dB时几乎可以完全消除符号间干扰的影响，与MAPSE相比其复杂度大大降低。     

Channel equalization using adaptive complex radial basis functionnetworks

It is generally recognized that digital channel equalization can be interpreted as a problem of nonlinear classification. Networks capable of approximating nonlinear mappings can be quite useful in such applications. The radial basis function network (RBFN) is one such network. We consider an extension of the RBFN for complex-valued signals (the complex RBFN or CRBFN). We also propose a stochastic-gradient (SG) training algorithm that adapts all free parameters of the network. We then consider the problem of equalization of complex nonlinear channels using the CRBFN as part of an equalizer. Results of simulations we have carried out show that the CRBFN with the SG algorithm can be quite effective in channel equalization     

Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization

Nonlinear intersymbol interference (ISI) leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel computationally efficient functional link neural network cascaded with Chebyshev orthogonal polynomial is proposed to combat nonlinear ISI. The equalizer has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomial and Chebyshev orthogonal polynomial. Due to the input pattern and nonlinear approximation enhancement, the proposed structure can approximate arbitrarily nonlinear decision boundaries. It has been utilized for nonlinear channel equalization. The performance of the proposed adaptive nonlinear equalizer is compared with functional link neural network (FLNN) equalizer, multilayer perceptron (MLP) network and radial basis function (RBF) along with conventional normalized least-mean-square algorithms (NLMS) for different linear and nonlinear channel models. The comparison of convergence rate, bit error rate (BER) and steady state error performance, and computational complexity involved for neural network equalizers is provided.     

Adaptive bit rate maximizing time-domain equalizer design for DMT-based systems

The classical discrete multitone receiver as used in, e.g., digital subscriber line (DSL) modems, combines a channel shortening time-domain equalizer (TEQ) with one-tap frequency-domain equalizers (FEQs). In a previous paper, the authors proposed a nonlinear bit rate maximizing (BM) TEQ design criterion and they have shown that the resulting BM-TEQ and the closely related BM per-group equalizers (PGEQs) approach the performance of the so-called per-tone equalizer (PTEQ). The PTEQ is an attractive alternative that provides a separate complex-valued equalizer for each active tone. In this paper, the authors show that the BM-TEQ and BM-PGEQ, despite their nonlinear cost criterion, can be designed adaptively, based on a recursive Levenberg-Marquardt algorithm. This adaptive BM-TEQ/BM-PGEQ makes use of the same second-order statistics as the earlier presented recursive least-squares (RLS)-based adaptive PTEQ. A complete range of adaptive BM equalizers then opens up: the RLS-based adaptive PTEQ design is computationally efficient but involves a large number of equalizer taps; the adaptive BM-TEQ has a minimal number of equalizer taps at the expense of a larger design complexity; the adaptive BM-PGEQ has a similar design complexity as the BM-TEQ and an intermediate number of equalizer taps between the BM-TEQ and the PTEQ. These adaptive equalizers allow us to track variations of transmission channel and noise, which are typical of a DSL environment.     

Nonlinear FM Distortion Equalizer

A new approach to FM distortion equalization is suggested and a nonlinear equalizer operating in the baseband frequency range is presented. It is shown that, using analog multipliers and differentiators, a baseband equalizer can be designed which eliminates all types of distortion products. The synthesis of the FM distortion equalizer is given with minimum number of multipliers. Using the synthesis procedure an equalizer circuit was designed for 1800 channel microwave FM systems. Experimental results obtained with the equalizer are discussed.     

Least squares approach to blind channel equalization

We present a least squares (LS) algorithm for blind channel equalization based on a reformulation of the Godard algorithm. A transformation for the equalizer parameters is considered to convert the nonlinear LS problem inherent in the Godard algorithm to a linear LS problem. Unlike the Godard (1980) algorithm, the proposed LS approach does not suffer from ill-convergence to closed-eye local minima. Methods for extracting the equalizer parameters from their transformed version are developed. Offline and recursive implementations of the LS algorithm are presented. The algorithm requires only a small number of channel output observations to estimate the equalizer parameters and is therefore fast vis-a-vis the Godard algorithm. The channel input correlation does not impose any restriction on the application of the algorithm, as long as a weak sufficient-excitation condition is satisfied. Simulation examples are presented to demonstrate the LS approach and to compare it with the Godard algorithm     

Robust MSE equalizer design for MIMO communication systems in the presence of model uncertainties

This paper considers a robust mean-square-error (MSE) equalizer design problem for multiple-input multiple-output (MIMO) communication systems with imperfect channel and noise information at the receiver. When the channel state information (CSI) and the noise covariance are known exactly at the receiver, a minimum-mean-square-error (MMSE) equalizer can be employed to estimate the transmitted signal. However, in actual systems, it is necessary to take into account channel and noise estimation errors. We consider here a worst-case equalizer design problem where the goal is to find the equalizer minimizing the equalization MSE for the least favorable channel model within a neighborhood of the estimated model. The neighborhood is formed by placing a bound on the Kullback-Leibler (KL) divergence between the actual and estimated channel models. Lagrangian optimization is used to convert this min-max problem into a convex min-min problem over a convex domain, which is solved by interchanging the minimization order. The robust MSE equalizer and associated least favorable channel model can then be obtained by solving numerically a scalar convex minimization problem. Simulation results are presented to demonstrate the MSE and bit error rate (BER) performance of robust equalizers when applied to the least favorable channel model.     

A Modified Adaptive FIR Equalizer for Multipath Echo Cancellation in FM Transmission

Multipath propagation is a severe problem in conventional FM broadcasting since this phenomenon causes nonlinear distortions after demodulation. Several proposals for the correction of multipath corrupted FM signals were given in some recent papers, but up to now there exist no satisfactory solutions with sufficiently low hardware complexity for commercial application. The main idea of the present paper is to take advantage of some a priori knowledge of the multipath transfer function with the goal to develop a nonrecursive equalizer structure with minimum hardware expense. For adaptive adjustment of the equalizer, we use a recently introduced constant modulus algorithm (CMA) in a modified form. Instead of direct coefficient adjustment, we regard the parameters of the transmission channel as unknowns which are updated adaptively. The equalizer coefficients are uniquely defined by the channel parameters and can be determined in one step. The advantage of this method is a large improvement of the convergence behavior in comparison to existing solutions. The methods presented here are tested by means of an FM hardware system including FM transmitter, multipath simulation, FM demodulator, and an FIR multipath equalizer, which allows real-time investigations of the equalizer performance under well-defined multipath conditions.     

Complex-valued neural networks with adaptive spline activationfunction for digital-radio-links nonlinear equalization

In this paper, a new complex-valued neural network based on adaptive activation functions is proposed. By varying the control points of a pair of Catmull-Rom cubic splines, which are used as an adaptable activation function, this new kind of neural network can be implemented as a very simple structure that is able to improve the generalization capabilities using few training samples. Due to its low architectural complexity (low overhead with respect to a simple FIR filter), this network can be used to cope with several nonlinear DSP problems at a high symbol rate. In particular, this work addresses the problem of nonlinear channel equalization. In fact, although several authors have already recognized the usefulness of a neural network as a channel equalizer, one problem has not yet been addressed: the high complexity and the very long data sequence needed to train the network. Several experimental results using a realistic channel model are reported that prove the effectiveness of the proposed network on equalizing a digital satellite radio link in the presence of noise, nonlinearities, and intersymbol interference (ISI)     

An FIR Equalizer Design for Nonlinear Channels Using Hybrid GA and LMI

In this paper, a finite impulse response (FIR) equalizer for nonlinear discrete-time channels is designed by employing a hybrid genetic algorithm (GA) and linear matrix inequality (LMI) approach from an H_∞ perspective. The GA technique is utilized to linearize the nonlinear channel model, and the approximate error can be viewed as a state uncertainty. Then, the design of the FIR equalizer is transformed into LMIs, and the coefficients of the FIR equalizer can be obtained by solving an LMI optimization problem. Finally, numerical examples are included to illustrate the effectiveness of the proposed methodology.