Application of Least Squares Lattice Algorithms to Adaptive Equalization

In many applications of adaptive data equalization, rapid initial convergence of the adaptive equalizer is of paramount importance. Apparently, the fastest known equalizer adaptation algorithm is based on a recursive least squares estimation algorithm. In this paper we show how the least squares lattice algorithms, recently introduced by Morf and Lee, can be adapted to the equalizer adjustment algorithm. The resulting algorithm, although computationally more complex than certain other equalizer algorithms (including the fast Kalman algorithm), has a number of desirable features which should prove useful in many applications.     

基于自适应投影学习算法的径向基函数网络设计及应用

基于自适应投影原理,本文提出了构造和训练径向基函数网络的自适应投影学习算法,该算法能迭代地确定径向基函数的个数、中心的位置以及网络的权系数,从而提供了生成径向基函数网络的一个简单和有效的方法.通过应用于信道均衡器和时间序列预报信号处理领域中的两个例子,进一步证明了该算法是行之有效的.     

基于复数径向基函数神经网络的自适应均衡器

本文对基于复数径向基函数网络（ＣＲＢＦＮ）的自适应均衡器作了研究；提出了两种新的复数径向基函数网络结构的均衡器；正交ＣＲＢＦＮ（ＯＣＲＢＦＮ）均衡器与非对称ＣＲＢＦＮ（ＡＣＲＢＦＮ）均衡器；并在此了提出了这两种自适应均衡器的训练算法，模拟结果表明对于线性信道或非线性信道定这两种基于ＣＲＢＦＮ均衡器的收敛性能比文献「２，５」中的元首衡器优越，具有一定的实用价值。     

自适应复信道均衡的一种新的神经网络方法

近年来,神经网络已经广泛地应用到许多信号处理问题中.自适应信道均衡是数字通信系统中的一个重要问题.在本文中我们提出一种基于复数赫布类型算法的自适应信道均衡器.计算机模拟表明,无论在线性还是非线性信道中,所提出的均衡器均表现出良好的性能,这为自适应复信道均衡提供了一种新的方法.     

Adaptive lossless coding scheme of lattice vector quantisation

In the process of quantisation, a lattice vector quantiser (LVQ) generates radius and index sequences. In lossless coding, the radius sequence is run-length coded and then Huffman or arithmetic coded, and the index sequence is represented by fixed binary bits. The author has improved the LVQ lossless coding by removing the redundant information between radius sequence and index sequence. An algorithm is developed that redistributes radius and index sequences. The algorithm adaptively shifts down large indices to smaller values and reduces the index bits. Hence, the proposed LVQ lossless coding method reduces the gap between actual coding bit rates and the optimal bit rate boundary. For a Laplacian source the proposed lossless coding scheme achieves more than 10% of bit reduction at bit rates higher than 0.7 bits/sample over the traditional lossless coding method     

一种基于自适应预测的医学图像高效无损压缩方法

随着数字化医学图像海量的增长及PACS系统的广泛应用,对医学图像进行高效的无损压缩已成为广泛关注的问题.本文提出一种基于自适应预测的无损压缩方法,该方法利用神经网络模型自学习的能力,自适应的调整预测器的预测系数.实验表明,该方法能有效去除X线医学图像的空间相关性,还能同时去除彩色医学图像的空间和谱间相关性,取得较高的压缩比,且编解码速度较高.     

基于误差反向传播算法的OFDM系统频域均衡

提出用一种基于神经网络的均衡方法,并用误差反向传播(BP)算法对多层前馈网络进行训练.分析和数值仿真结果表明,基于BP网络的频域均衡方法有较好的均衡效果,并且复杂度不高.     

A DSP line equalizer VLSI for TCM digital subscriber-linetransmission

The architecture of a line equalizer using digital-signal-processing (DSP) techniques is described. The equalizer is utilized in 320-kb/s time-compression multiplexing (TCM) subscriber-line transmission systems in the integrated services digital network (ISDN). It consists of two digital filter blocks, called the √f equalizer and the bridged-tap equalizer, and gain- and timing-control blocks. The √f equalizer achieves the processing speed of 20 MOPS by a powerful arithmetic unit composed of multipliers and adders. It provides an FIR filter with nine taps which satisfies an accurate equalization for the 1.92-Msample/s data. The bridged-tap equalizer performs both the adaptation algorithm of the √f equalizer and the decision-feedback algorithm. The microprogram control enables the hardware to be shared between these functions and assures flexibility. Algorithm-oriented instructions implemented in the ALU realize high-speed execution of the decision-feedback algorithm with a simple architecture. The 11.3-mm×8.5-mm chip with 61 K transistors has been implemented using 1.5-μm double-metal-layer CMOS technology     

一种新的神经网络均衡器:结构、算法与性能

本文根据克服数字通信中码间干扰（ISI）的最佳均衡解一般表达式，提出了一种新的自适应神经网络均衡器结构，然后导出了基于该结构的一种自适应算法和相应的学习规则，最后对提出的自适应神经网络均衡器性能进行了计算机模拟，模拟结果与分析表明：本文提出的神经网络均衡器用于实现最佳信道均衡非常有效，比传统线性均衡器和Gibson等人[1]提出的多层感知均衡器（MLPE）性能更优越，更具实用性．     

基于小波包变换和判决反馈RBF网络的组合均衡器

本文提出了一种基于小波包变换和判决反馈RBF网络的组合非线性均衡器的结构和算法.首先将信号进行小波包分解,再将分解后的信号分量送入带有判决反馈结构的RBF神经网络进行均衡.一方面,小波包具有很强的去相关能力,可以提高均衡器的收敛速度;另一方面,RBF神经网络具有较强的非线性模式分类能力,可降低均衡器的均方误差.在仿真实验中,针对无线通信数字信号传输过程中由于多径效应和信道衰落而产生的码间干扰(ISI)问题,比较了最小均方(LMS)算法和组合均衡器算法的均衡效果,结果表明,组合均衡算法具有更快的收敛速度,更低的误码率.     

一种脉冲噪声下的自适应均衡算法

由单层神经自适应滤波器和Chebyshev正交多项式滤波器级联构成一个均衡器,基于此均衡器结构,提出一种基于分数低阶统计量的自适应均衡算法(称为NC-NLMP算法).仿真结果表明,对线性信道和非线性信道,NC-NLMP算法的均衡性能均优于传统的FIR横向滤波器的自适应均衡算法和神经网络均衡算法;对于非线性信道,NC-NLMP算法的均衡性能远优于后两者;NC-NLMP算法对高斯噪声和稳定分布脉冲噪声都具有很好的均衡性能,对背景噪声韧性强.     

Reconstruction of chaotic signals with application to channel equalization in chaos‐based communication systems

A number of schemes have been proposed for communication using chaos over the past years. Regardless of the exact modulation method used, the transmitted signal must go through a physical channel which undesirably introduces distortion to the signal and adds noise to it. The problem is particularly serious when coherent‐based demodulation is used because the necessary process of chaos synchronization is difficult to implement in practice. This paper addresses the channel distortion problem and proposes a technique for channel equalization in chaos‐based communication systems. The proposed equalization is realized by a modified recurrent neural network (RNN) incorporating a specific training (equalizing) algorithm. Computer simulations are used to demonstrate the performance of the proposed equalizer in chaos‐based communication systems. The Hénon map and Chua's circuit are used to generate chaotic signals. It is shown that the proposed RNN‐based equalizer outperforms conventional equalizers as well as those based on feedforward neural networks for noisy, distorted linear and non‐linear channels. Copyright © 2004 John Wiley & Sons, Ltd.     

Adaptive Bayesian equalizer with decision feedback

A Bayesian solution is derived for digital communication channel equalization with decision feedback. This is an extension of the maximum a posteriori probability symbol-decision equalizer to include decision feedback. A novel scheme utilizing decision feedback that not only improves equalization performance but also reduces computational complexity greatly is proposed. It is shown that the Bayesian equalizer has a structure equivalent to that of the radial basis function network, the latter being a one-hidden-layer artificial neural network widely used in pattern classification and many other areas of signal processing. Two adaptive approaches are developed to realize the Bayesian solution. The maximum-likelihood Viterbi algorithm and the conventional decision feedback equalizer are used as two benchmarks to asses the performance of the Bayesian decision feedback equalizer     

Channel equalization via channel identification: algorithms andsimulation results for rapidly fading HF channels

The problem of channel equalization via channel identification (CEQCID) that has previously been considered by a handful of researchers is explored further. An efficient algorithm for mapping the channel parameters to the equalizers coefficients is proposed. The proposed scheme is compared with a lattice least squares (LS) based receivers. For the particular application of the high frequency (HF) radio channels, we find that the CEQCID has lower computational complexity. In terms of the tracking performance, also, the CEQCID has been found to be superior to the LS based receivers. We emphasize on the implementation of a fractionally tap-spaced decision feedback equalizer (DFE) and compare that with the T-spaced DFE. We show that the former is a better choice for the multipath HF channels     

基于遗传算法的微波均衡器仿真和工程设计

研究了用于均衡行波管增益的微波均衡器的计算机仿真和工程设计问题。理论分析并建立了均衡 器的一般微波网络模型,该网络模型具有数十个优化变量,能够使均衡器实现任意有理函数类的均衡曲线。利用遗 传优化算法完成多变量的单目标或多目标优化,最终根据优化结果来具体实现均衡器的工程设计并为后期调试提 供指导。设计了一种C 波段10 阶同轴均衡器,实物调节和测试表明该设计对该波段的各种均衡曲线均有很好的适 应性,均衡误差1dB 以内,其中均衡枝节还具有温度补偿特性,高低温温飘小于5MHz。     

基于结构相似度的大规模社交网络聚类算法

针对社交网络的有向交互性和大规模特性,该文提出一种基于结构相似度的有向网络聚类算法(DirSCAN),以及相应的分布式并行算法(PDirSCAN)。考虑社交网络中节点间的有向交互性,将行为结构相似的节点聚集起来,并进行节点功能分析。针对社交网络规模巨大的特点,提出MapReduce框架下的分布式并行聚类算法,在确保聚类结果一致的前提下,提高处理性能。大量真实数据集上的实验结果表明,DirSCAN比无向网络聚类算法(SCAN)在F1上可提高2.34%的性能,并行算法PDirSCAN比DirSCAN运行速度提升1.67倍,能够有效处理大规模的有向网络聚类问题。     

Computable real lattice structures for cochlea-like digital filters

A synthesis algorithm for a pipelined lattice implementation of cochlea-like digital filters is presented, based upon the properties of real, lossless lattice synthesis of ARMA filters. The algorithm operates on a simplified characterization of elementary lattice sections of degree one or degree two. This leads to a structure that is recursively designed and for which each lattice is precisely implemented by a pair of complex conjugate transmission zeros via Richard's function extractions. Except for zeros of transmission on the unit circle, all other types and multiplicities are allowed. Necessary and sufficient conditions are derived for the degree-two lattices to guarantee computability, i.e., realizability with no delay-free loops. In addition to being suitable for VLSI realization, the structure enables a systematic cochlea assessment from the scattering ear parameters.     

A general theory of broadband matching of an active load

Given a passive frequency-dependent source impedance and an active or passive load and a preassigned transducer power-gain characteristic, necessary and sufficient conditions are given for the existence of a lossless reciprocal equalizer which, when operating between the given source and the given load, yields the desired transducer power-gain characteristic. The significance of the present approach is that the realization of the equalizer involves only driving-point synthesis by the Darlington theory.     

预滤波式均衡器的原理和实现

提出了一种新的均衡器——预滤波式均衡器。这种均衡器由预滤波器和白适应均衡器组成。它利用高效的相关估计算法，计算出所有多径的时延和幅度，然后使用多径拆分算法，设置预滤波器的参数，使得接收信号经过预滤波之后，畸变得到了改善，进而可由后面的自适应均衡器将残余的码间干扰完全消除。结果表明，在性能上它比传统均衡器有显著提高，可以补偿幅度为0dB的副径。