We describe a class of DC-free subcodes of convolutional codes that satisfy certain runlength constraints and that also possess error-correcting capability. The running disparity and the maximum runlength of these codes are bounded by quantities that are independent of the free distance. They can be decoded using a Viterbi decoder for the underlying convolutional code.

Data transmission in a binary partial-response channel is often accomplished using a concatenated code consisting of an inner modulation code and an outer error-correcting code (ECC). We consider two inner decoders for such a code, each consisting of a reduced-complexity sequence detector modified to provide an estimate of the reliability of each bit. These reliability values are then used by the outer decoder to achieve improved performance. Although one of these decoders is considerably simpler than the other, their performances were comparable in the cases we considered. Both achieve considerable improvement over a decoder that uses hard-decision decoding of the inner code.

Considers the concatenation of an inner matched-spectral-null (MSN) code with an outer error-correcting code to achieve improved performance on a partial-response channel in the presence of additive white Gaussian noise. The authors show how to modify the reduced-complexity Viterbi algorithm of Karabed and Siegel [1991] to produce reliability values for each bit using the procedure proposed by Hagenauer and Hoeher [1989]. These reliability values are then used by the outer decoder to achieve improved performance. The authors simulated a number of codes and observed improvements due to soft-decision decoding of 1-2 db.>

Many of the previously-developed multiple-access detection algorithms have assumed knowledge of the time delay, the amplitude, and the phase of each user. This paper presents methods of estimating the amplitudes and phases, both when the time delays are known and when the delays are unknown. In neither case are the transmitted bits assumed to be known. The estimators are unbiased and consistent, and each has a computational complexity linear in the number of users. Simulations are provided to demonstrate performance.>

Considers three methods for constructing (d,k) codes with guaranteed error-correcting capability. All three methods begin with state-dependent modulation codes to satisfy the (d,k) constraints. The first is an ad hoc method that uses the state-splitting procedure of Adler et al. (1983), together with additional constraints to ensure that the required minimum distance is achieved. The second method partitions the state-dependent modulation code into a part with memory and a part without, and then combines the part with memory with a matched convolutional code to provide error protection. The third method is obtained by modifying of the procedure of Lee and Wolf (1989) to accommodate state-dependent modulation codes. All three methods have yielded codes whose rates, computational complexities, and error-correcting capabilities compare favorably with codes designed using other methods.>

The problem of simultaneously detecting the information bits and estimating signal amplitudes and phases in a K-user asynchronous direct-sequence spread-spectrum multiple-access communication system is addressed. The joint maximum-likelihood (ML) estimator has a computational complexity that is exponential in the total number of bits transmitted and thus does not represent a practical solution to the problem. An estimator that combines a suboptimum tree-search algorithm with a recursive least-squares estimator of complex signal amplitude is considered. The complexity of this estimator is O(K/sup 2/) computations per decoded bit, and its performance is very close to that of the joint ML receiver. This receiver has the advantage that the transmitted signal powers and phases are extracted from the received signal in an adaptive fashion without using a test sequence.>

Consideration is given to a class of suboptimum detectors for data transmitted asynchronously by K users employing direct-sequence spread-spectrum multiple access (DS/SSMA) on the additive white Gaussian noise (AWGN) channel. The general structure of these detectors consists of a bank of matched filters, a linear transformation that operates on the matched-filter outputs, and a set of threshold devices. The linear transformations are chosen to minimize either a mean-squared-error or a weighted-squared-error performance criterion. Each detector can be implemented using a tapped delay line. The number of computations performed per detected bit is linear in K in each case, and the resulting detectors are thus much simpler than the optimum detector. Under typical operating conditions, these detectors will perform much better than the conventional receiver and often nearly as well as the optimum detector.>

The application of sequential decoding to the detection of data transmitted over the additive white Gaussian noise channel by K asynchronous transmitters using direct-sequence spread-spectrum multiple access (DS/SSMA) is considered. A modification of R.M. Fano's (1963) sequential-decoding metric, allowing the messages from a given user to be safely decoded if its E/sub b//N/sub 0/ exceeds -1.6 dB, is presented. Computer simulation is used to evaluate the performance of a sequential decoder that uses this metric in conjunction with the stack algorithm. In many circumstances, the sequential decoder achieves results comparable to those obtained using the much more complicated optimal receiver.>

A new approach is presented to the digitization and compression of a class of voiceband modem signals. The approach, baseband residual vector quantization (BRVQ), relies heavily on the simple structure present in a modem signal. After the signal is converted to baseband, the magnitude sequence and the sequence of residuals obtained when the phase within each baud of the baseband signals is modeled by a straight line are separately vector quantized. Carrier frequency estimation and baud-rate classification schemes that were designed to carry out these operations are described. Experimental results show that the performance of the BRVQ system at and below 16 kb/s is better than that of a previously developed vector quantization scheme that has itself been shown to outperform traditional speech-compression techniques such as adaptive predictive coding, adaptive transform coding, and subband coding when these techniques are used to compress modem signals.>

A novel carrier frequency estimation scheme for a relatively broad class of voiceband data signals (modem signals) is presented. The class of signals being studied includes different types of phase-shift-keyed (PSK) and frequency-shift-keyed (FSK) signals. The frequency estimates are obtained averaging the estimates of the derivative of the instantaneous signal phase after discarding the estimates at the baud boundaries. Experimental results illustrate that the frequency estimates obtained are unbiased and have small variances. Results also show that the carrier frequency estimation scheme presented is superior to two other techniques in terms of both the mean and variance of the carrier frequency estimates.>

J.H. Conway and N.J.A. Sloane (1986) have introduced an algorithm for the exact maximum-likelihood decoding of the Golay (24, 12) code in the additive white Gaussian noise channel that requires significantly fewer computations than previous algorithms. An efficient bit-serial VLSI implementation of this algorithm is described. The design consists of two chips developed using path-programmable logic (PPL) and an associated system of automated design tools for three- mu m NMOS technology. It is estimated that this decoder will produce an information bit every 1.6-2.4 mu s. Higher speeds can be achieved by using a faster technology or by replicating the chips to perform more operations in parallel.>

The performances of three representative waveform coders (APC, ATC and SBC) are measured for the compression of voiceband data signals to 16 kb/s. Performance is evaluated with respect to four criteria: bit-error rate (BER), signal-to-quantization-noise ratio (SQR), critical data rate (Rc) and detection statistic dispersion figure (γ). The waveform coders pass low rate (0-1200 b/s) modem signals with adequate fidelity through a single encoding, but have difficulty with some medium rate (1200-4800 b/s) signals. An adaptive vector quantizer (ABCVQ) is proposed for voiceband data compression. The ABCVQ coder adapts to the energy and center frequency of a modem signal and employs four baseband codebooks to accommodate an ensemble of low and medium rate voiceband data signals. The ABCVQ coder outperforms the three waveform coders with respect to each of the four performance criteria for all modem signals tested.

We consider the design of imaging systems for which image restoration is a well-conditioned problem. By assuming that both the observation and the object are discrete, we are able to derive, via the Singular Value Decomposition, conditions which minimize the condition number for either coherent or incoherent illumination systems. We also illustrate the quality of signals restored by these well-conditioned systems.

We study in this paper a discrete-time, discrete-frequency model for image restoration using the singular value decomposition of the imaging matrix. We show that the resulting singular vectors have many of the properties possessed by Slepian's discrete prolate sphroidal sequences (DPSS). They are doubly orthogonal, they are bandlimited, they satisfy an equation very similar to that satisfied by the DPSSs, and they possess an extremal energy-concentration property. These properties continue to hold with appropriate modification for bandpass as well as lowpass operations.

A problem receiving considerable attention from researchers is that of producing a stable extrapolation of the Fourier transform of a function of finite support. The problem is ill-posed, and the sensitivity of the extrapolated result to measurement noise is pronounced for many of the solution algorithms proposed to date, In this paper an alternative algorithm is presented. This new algorithm is non-linear, and in certain cases can produce very accurate extrapolations over a wide range of signal-to-noise ratios. We discuss in rather heuristic terms the behavior of this algorithm, and summarize the results of some experiments.

Linear predictive coding (LPC) has been successfully applied to the encoding of speech and other time series. It has been widely observed, however, that the performance of an LPC algorithm deteriorates rapidly in the presence of background noise. In this paper, we describe and discuss one approach to the identification of a time series corrupted by additive white noise. A common approach to this problem is to prefilter the noisy time series, and then to apply an estimation algorithm which treats the time series as if it were noise-free. We describe an alternative approach which involves modifying the time-series model at the outset to account for the presence of noise. An estimation algorithm is then developed for this modified model. We discuss the development of the model, the estimation algorithm, and some representative experimental results.

Steiglitz (2) has recently described an algorithm for estimating the parameters of an autoregressive-moving-average (ARMA) process. This algorithm has application, for example, to the problem of determining the poles and zeroes of the vocal-tract transfer function. In this paper, we report and discuss the results of a number of simulations conducted using the Steiglitz algorithm. The bulk of the experiments involved driving the ten-pole, two-zero filter described in (2) with a single pulse, with a short pulse train, and with samples of white Gaussian noise. In each of these cases, we evaluated the effects of such processing options as windowing, preemphasis, and cepstral-domain filtering. We also discuss and compare results obtained by applying the Steiglitz algorithm and a Newton-Raphson conditional maximum-likelihood algorithm to a first-order process.

This paper describes a fast direct algorithm for obtaining least-squares phase estimates from arrays of noisy phase differences. The algorithm uses the fast Fourier transform (FFT) to diagonalize and decouple the system of equations which results from the application of the least-squares criterion. It is accurate and stable, and is perhaps an order of magnitude faster than the best iterative method. The effectiveness of the algorithm has been demonstrated by using it in connection with the Knox-Thompson speckle-imaging procedure to restore an optical object perturbed by simulated atmospheric turbulence.

Nonstationary acoustic noise with energy possibly equal to or greater than the speech is suppressed using a two microphone implementation of adaptive noise cancellation. The primary noise added to the speech is reduced by subtracting a filtered version of the second microphone reference noise. The reference noise filter is adaptively up dated using the Widrow-Hoff LMS algorithm [1]. The effectiveness of noise suppression depends directly on the ability of the filter to estimate the transfer function relating the primary and reference noise channels. A study of the filter length required to achieve a desired noise reduction level in a hard-walled room is presented. Results demonstrating noise reduction in excess 10dB in an environment with 0dB signal noise ratio are presented.

This note describes a cellular array that computes the product of two arbitrary elements of the Galois field GF(2m). The regularity of this array should make it attractive for LSI fabrication.

This paper treats the problem of detecting a signal of known form in the presence of additive noise whose distribution function may not be known. Two detection schemes are considered, one for a signal which is known exactly and one for a signal with unknown phase. Using the criterion of asymptotic relative efficiency (ARE), the performance of each detector is compared with that of the corresponding detector which is optimum for Gaussian noise. It is shown that for detecting a known signal, the ARE of the nonparametric detector relative to the correlation detector is equal to one if the noise is Gaussian, and is strictly greater than one for any continuous non-Gaussian noise distribution. An identical result holds for the ARE of the nonparametric detector for a signal with unknown phase relative to the standard envelope detector.

In this paper, a particular approach to the problem of communicating in certain random or unknown channels is considered. A particular set of signals is chosen, each member of which is partitioned into a known reference or sounding signal and a message signal. The channel is assumed to be linear, and is divided into a multiplicative and an additive portion. The multiplicative portion is assumed to have the same response to both message and reference components, while the additive noises associated with these components are assumed to be independent. The channel outputs and the additive noises are further assumed to be Gaussian. Under these circumstances, the optimum receiver is shown to cross-correlate the message portion of the received signal with a filtered version of the posterior mean of the channel output, which is merely a filtered combination of the prior mean and the perturbed reference signal. This is an interesting extension of the optimum receiver for known signals in additive Gaussian noise. Several special cases are considered which yield additional insight into the operation of the optimum receiver.