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HIGH-SPEED VLSI architectures FOR SOFT-OUTPUT VITERBI DECODING

JOURNAL OF VLSI signal processing 1994年第2期8卷 169-181页

作者： JOERESSEN, OJ VAUPEL, M MEYR, H 1. Integrated Systems for Signal Processing (ISS-611810) Aachen University of Technology (RWTH) 52056 Aachen Germany

During the last years decoding algorithms that make not only use of soft quantized inputs but also deliver soft decision outputs have attracted considerable attention because additional coding gains are obtainable in concatenated systems. A prominent member of this class of algorithms is the Soft-Output Viterbi Algorithm. In this paper two architectures for high speed VLSI implementations of the Soft-Output Viterbi-Algorithm are proposed and area estimates are given for both architectures. The well known trade-off between computational complexity and storage requirements is played to obtain new VLSI architectures with increased implementation efficiency. Area savings of up to 40% in comparison to straightforward solutions are reported.

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signal exploitation by time and frequency correlation

Signal exploitation by time and frequency correlation

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conference on advanced signal processing algorithms, architectures, and implementations xI

作者： Nelson, DJ Tempkin, JA US Dept Def Ft George G Meade MD 20755 USA

ISBN: (纸本)0819441880

We address two classical problems relating to harmonic signals. The first of these is the blind recovery of the carrier of a single sideband-AM communication signal, and the second is the isolation and blind estimation of the fundamental of a time-varying harmonic signal. The methods are based on cross-spectra estimated from the short time Fourier transform, a generalization of the Chinese remainder theorem and joint Fourier and autocorrelation representations of the signal spectrum. These tools are developed, and their utility is demonstrated in the solutions of the the two problems. By an additional application of a frequency-lag autocorrelation function, it is demonstrated that the harmonic fundamental can be recovered, even if it is not present in the original spectrum.

关键词： single sideband AM autocorrelation signal estimation phase spectrum Chinese remainder theorem

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PARALLEL IMPLEMENTATION OF SOME FAST ADAPTIVE algorithms ON A DIGITAL signal PROCESSOR (DSP) NETWORK

PARALLEL IMPLEMENTATION OF SOME FAST ADAPTIVE ALGORITHMS ON ...

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CONF ON advanced signal processing algorithms, architectures, AND implementations 2

作者： REYNAUD, R CHEBIRA, A DEMOMENT, G Univ. de Paris-Sud (France)

ISBN: (纸本)0819406945

Several important problems in signal processing, such as linear prediction, linear regression, or spectrum factorization, need close-to-Toeplitz matrices to be factored. To solve these problems, several fast algorithms have been derived. They differ by the kind of adaptivity (block processing or exponential weighting of the data) and by the kind of recursion (order or time recursion), but they all have a common vector ladder recursion, involving an hyperbolic rotation. These algorithms are therefore well suited for implementation on an array processor. But there exists a number of applications where an efficient parallel implementation of these algorithms on a DSP network would be very attractive. The redundancy suppression, which is performed in the equations to get a fast algorithm, destroys the processing regularity of the corresponding standard algorithms, which prevents efficient high level parallel implementation. Auxiliary quantities, such as generalized reflections coefficients, are introduced that don't have the same dimensions as primary quantities. As a result there is a loss of efficiency in such tasks processing, and this leads to use array partitioning in as many sub-arrays as the number of DSPs available. If the number m of DSPs is low compared to primary quantities of dimension p (m << p/m) (i.e. a low level parallelism) and if the dimension a of reflection coefficients is also low ((alpha) << p/m), the global efficiency of a parallel implementation on a DSP network may still be interesting, with in addition, the advantages associated to such a network : simple design, simple control. To illustrate this, an application of the method to a Fast Recursive Least Squares algorithm is presented.

关键词： Digital signal processing signal processing Data modeling Algorithm development Array processing Filtering (signal processing) Scattering Digital filtering Matrices Network architectures

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ANALYSIS OF A LINEARLY CONSTRAINED LEAST-SQUARES ALGORITHM FOR ADAPTIVE BEAMFORMING

ANALYSIS OF A LINEARLY CONSTRAINED LEAST-SQUARES ALGORITHM F...

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CONF ON advanced signal processing algorithms, architectures, AND implementations 3

作者： LUK, FT QIAO, SZ Rensselaer Polytechnic Institute (United States) McMaster Univ. (Canada)

ISBN: (纸本)081940943X

The problem of linearly constrained least squares has many applications in signal processing. In this paper, we present a perturbation analysis of a novel, linearly constrained least squares algorithm for adaptive beamforming. The perturbation bounds for the solution as well as for the latest residual element are derived. We also propose an error estimation scheme for the residual element. This error estimation scheme can be incorporated into a systolic array implementation of the algorithm. The additional time required for error estimation is small, as we show how the processor idle time can be used to carry out almost all the calculations.

关键词： Error analysis Chemical elements Analytical research Silicon Evolutionary algorithms signal processing Condition numbers Einsteinium Information operations Matrices

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Fast blind signal separation for multi-user interference rejection in smart antenna arrays

Fast blind signal separation for multi-user interference rej...

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conference on advanced signal processing - algorithms, architectures, and implementations VII

作者： Sarajedini, A Chau, PM Univ. of California/San Diego (United States)

ISBN: (纸本)0819425842

Spread spectrum modulation techniques such as frequency-hopped code division multiple access (FH-CDMA) are an efficient way to allow multi-user transmission over a limited bandwidth. Recently, there has been a push to increase user capacity over the same bandwidth. In particular, smart antenna arrays have been studied for their capacity gain potential. In this regard, we propose the use of blind signal separation for recovery of signals received through an antenna array. To implement this technique requires the antenna array vectors to be stationary which does not hold in FH-CDMA, so we also propose a new method for frequency compensation. We present the theoretical details of the frequency compensation and compare its performance to no frequency compensation. We then present the blind signal separation algorithm applied to a complex antenna array matrix and complex signals with noise and show in simulation that the blind signal separation works.

关键词： blind signal separation frequency compensation frequency-hopped CDMA spread spectrum

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Operational data processing to improve SPOT image resolution

Operational data processing to improve SPOT image resolution

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conference on advanced signal processing algorithms, architectures, and implementations xI

作者： Breton, E Latry, C Lévy, F Rougé, B IGN F-31055 Toulouse France

ISBN: (纸本)0819441880

Mainly digital images are under sampled. It is the same for SPOT digital image satellite. The very meaning is that the instrument is too much powerful for this sampling. The worth side effect is that artifacts (aliasing) are introduced in the image, the good side is that images can be improved if the sampling density is increased. In this paper we use images from the two HRVIR instruments onboard SPOT1-4 satellite to multiply by a factor two the density and the resolution of the image.

关键词： super resolution operational data processing interlaced sampling interpolation correlation SPOT

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Color characterization for image indexing and machine vision

Color characterization for image indexing and machine vision

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conference on advanced signal processing algorithms, architectures, and implementations x

作者： Lin, SM Nair, D Natl Instruments Austin TX 78759 USA

ISBN: (纸本)0819437611

Color representation and comparison based on the histogram has proved to be very efficient for image indexing in content-based image retrieval and machine vision applications. However, the issues of color constancy and accurate color similarity measures remain unsolved. This paper presents a new algorithm for intensity-insensitive color characterization for image retrieval and machine vision applications. The color characterization algorithm divides the HSI (hue, saturation and intensity) color space into a given number of bins in such a way that the color characterization represents all the colors in the hue/saturation plane as well as black, white and gray colors. The color distribution in these bins of the HSI space is represented in the form of a one-dimensional vector called Color Spectrum Vector (CSV). The color information that is stored in the CSV is insensitive to changes in the luminance. A weighted version of CSV called WCSV is introduced to take the similarity of the neighboring bins into account A Fuzzy Color Spectrum Vector (FCSV) color representation vector that takes into account the human uncertainty in color classification process is also introduced here. The accuracy and speed of the algorithm is demonstrated in this paper through a series of experiments on image indexing and machine vision applications.

关键词： color comparison pattern matching clustering algorithm fuzzy set theory image indexing machine vision image processing

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algorithms FOR INSTANTANEOUS FREQUENCY ESTIMATION - A COMPARATIVE-STUDY

ALGORITHMS FOR INSTANTANEOUS FREQUENCY ESTIMATION - A COMPAR...

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CONF ON advanced signal-processing algorithms, architectures, AND implementations

作者： BOASHASH, B OSHEA, P ARNOLD, MJ Univ. of Queensland (Australia)

ISBN: (纸本)0819404098

This paper examines the problem of instantaneous frequency (IF) estimation for Frequency Modulated (FM) signals imbedded in white Gaussian noise. It reviews currently available techniques and in addition proposes some new ones based on a modelling of the signal phase as a polynomial. Both linear least-squares techniques and Maximum Likelihood (ML) techniques are investigated for estimating the polynomial coefficients. It is seen that the linear least squares approach is efficient (i. e. unbiased and meets the Cramer-Rao bounds) for high SNR while the ML scheme is efficient for a much larger range of SNR. Theoretical lower variance bounds are given for estimating the polynomial coefficients and are compared with the results of simulations. Guidelines are given as to which estimation method should be used for a given signal class and signal to Noise Ratio (SNR) level.

关键词： signal to noise ratio Fermium Frequency modulation Interference (communication) Modeling Modulation

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NEW ENCODING DECODING METHODS FOR DESIGNING FAULT-TOLERANT MATRIx OPERATIONS

NEW ENCODING DECODING METHODS FOR DESIGNING FAULT-TOLERANT M...

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CONF ON advanced signal processing algorithms, architectures, AND implementations 3

作者： TAO, DL HAN, YS HARTMANN, CRP SUNY/Stony Brook (United States) Syracuse Univ. (United States)

ISBN: (纸本)081940943X

The algorithm-based fault tolerance (ABFT) provides low-cost error protection for VLSI processor arrays used in real-time digital signal processing. Several ABFT techniques (weighted check-sum) have be proposed to design fault-tolerant LU decomposition, QR decomposition, and matrix inversion. In these schemes, encoding/decoding uses either multiplication or division so that overhead is high. In this paper, we propose new encoding/decoding methods for designing fault-tolerant matrix operations. Since the new encoding/decoding methods use only additions and subtractions, the overhead of a fault-tolerant processor array can be drastically reduced.

关键词： Array processing Digital signal processing Silicon Tolerancing Chlorine Information operations Osmium signal processing Very large scale integration Electrical engineering

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NEURAL-NETWORK processing TO MINIMIZE QUANTIZATION LOSSES 4

NEURAL-NETWORK PROCESSING TO MINIMIZE QUANTIZATION LOSSES

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4TH CONF ON advanced signal processing algorithms, architectures, AND implementations

作者： WU, YJ CHAU, PM Univ. of California/San Diego (United States)

ISBN: (纸本)0819412767

A general neural network co-processor has been investigated and designed to adaptively adjust the quantization thresholds of a data quantizer, and thus a data quantizer with minimum quantization loss can be obtained. For a given probability density function and number of quantization levels, the neural network is designed to learn the near optimal quantization uniform step-size which minimizes the loss caused by the quantizer. With this neural network co-processor approach, consistent and substantial performance improvements have been verified on either an AWGN or a Rayleigh fading communication channel with convolutional encoder and maximum likelihood decoder. This general neural network co-processor approach can be applied to any digital signal processing system which has quantization loss, such as digital communication, image data compression, or adaptive signal processing.

关键词： Neural networks Quantization Digital signal processing Data communications signal processing signal to noise ratio Analog electronics Modulation signal attenuation Telecommunications

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