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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

Performance of convolution coding concatenated with MFSK modulation in a Gaussian channel

The improvement in db due to concatenation over conventional M-ary coding is studied to reduce the probability of a bit error and to increase the available bit rate for the same system parameters of error rate, transmitter power, and range. The results of calculations for orthogonal modulation with noncoherent detection and Q-level correlator quantization are presented. It is shown that the correlator outputs are quantized to one of the Q levels, and the receiver output is a vector consisting of a list of the M correlator quantum levels. The channel has Q(M) possible outputs and M possible inputs. Optimum output is approached by increasing fine quantization

Choudhury, A. K.↗
NASA NTRS1972

Recent results in convolution feedback systems.

Survey of recent results obtained by the authors concerning certain types of multiinput, multioutput feedback systems. The discrete-time case as well as the continuous-time case are considered. In each case three theorems are shown. These give insight into the nature of the relationship between the open-loop operator and the closed-loop operator of the system, as well as necessary and sufficient conditions for stability of the closed-loop system when 'unstable' poles are present in their open-loop transfer function.

Desoer, C. A.↗
NASA NTRS1972

Convolution feedback systems.

Linear time-invariant feedback systems with multiple inputs and multiple outputs are examined. It is demonstrated that no loss of generality takes place considering the feedback to be unity. Necessary and sufficient conditions are derived for the closed-loop impulse response to be stable in a prescribed sense.

Desoer, C. A.↗
NASA NTRS1973

Enhancement of spectra by digital convolution.

A method is presented for convolving digitally scanned spectra by means of computerized filtering. This method is an effective tool for improving the quality of noisy spectra and for deconvolving high-quality spectra by removing instrument signature.

Lorre, J. J.↗
NASA NTRS1974

Convolutional codes. II - Maximum-likelihood decoding. III - Sequential decoding

Maximum-likelihood decoding is characterized as the determination of the shortest path through a topological structure called a trellis. Aspects of code structure are discussed along with questions regarding maximum-likelihood decoding on memoryless channels. A general bounding technique is introduced. The technique is used to obtain asymptotic bounds on the probability of error for maximum-likelihood decoding and list-of-2 decoding. The basic features of sequential algorithms are discussed along with a stack algorithm, questions of computational distribution, and the martingale approach to computational bounds.

Forney, G. D., Jr.↗
NASA NTRS1975

A recommended R equals 1/2, K equals 32, Quick-Look-In convolutional code for NASA use

A new R = 1/2 K = 32 quick-look-in code is described and compared to the R = 1/2 K = 32 Massey-Costello code now used in some NASA systems. The new code, has the optimum distance profile property. This new code is shown, by comparison of Fano sequential decoding performance on a simulated Gaussian noise channel, to be computationally superior to the Massey-Costello code. The new code is also shown to be superior to the Massey-Costello code according to several analytical code criteria.

Massey, J. L.↗
NASA NTRS1977

Implementation of a maximum likelihood convolutional decoder in the DSN

The development status of the decoder and the factors which were considered in defining the specific functional requirements are described. The design is discussed to the block diagram level. A description of the detailed design is provided, along with a description of the test software developed and a brief summary of the performance evaluation testing completed so far.

Alberda, M. E.↗
NASA NTRS1977

High bit rate convolutional channel encoder/decoder

A detailed description of the design approach and tradeoffs encountered during the development of the 50 MBPS decoder system is presented. A functional analysis of each of the major logical functions is given, and the system's major components are listed.

Source record↗
NASA NTRS1980

Convolution of a Doppler line by a Gaussian instrument function

A simple and direct method is obtained for assessing the distortion of a Doppler line by a Gaussian instrument function. It is suggested that a close approximation to the width of a Gaussian instrument function, or an almost Gaussian function, may be obtained by measuring a line with a Doppler absorption coefficient. The method is applicable to diode laser measurements, and may be used whenever a Gaussian instrument function is a reasonable approximation to real conditions

Fridovich, B.↗
NASA NTRS1985

Convolutionally-Coded Unbalanced QPSK Systems

Report discusses error-rate performance for three convolutionallycoded unbalanced quadriphase-shift-keying (UQPSK) communication systems with noisy carriers that introduce crosstalk. Systems analyzed unbalanced in sense that each transmits two data streams with different bit rates and (in some cases) different powers.

Divsalar, D.↗
NASA NTRS1985

Simplified Convolution Codes

Simple recursive algorithm efficiently calculates minimum-weight error vectors using Diophantine equations. Recursive algorithm uses general solution of polynomial linear Diophantine equation to determine minimum-weight error polynomial vector in equation in polynomial space.

Truong, T. K.↗
NASA NTRS1984

Performance Simulation for Unit-memory Convolutional Codes with Byte-oriented Viterbi Decoding Algorithm

A software package developed to simulate the performance of the byte-oriented Viterbi decoding algorithm for unit-memory (UM) codes on both 3-bit and 4-bit quantized AWGN channels is described. The simulation is shown to require negligible memory and less time than that for the RTMBEP algorith, although they both provide similar performance in terms of symbol-error probability. This makes it possible to compute the symbol-error probability of large codes and to determine the signal-to-noise ratio required to achieve a bit error rate (BER) of 0.000001 for corresponding concatenated systems. A (7, 10/48) UM code, 10-bit Reed-Solomon code combination achieves the required BER at 1.08 dB for a 3-bit quantized channel and at 0.91 dB for a 4-bit quantized channel.

Vo, Q. D.↗
NASA NTRS1986

Effects of quantization on symbol stream combining in a convolutionally coded system

Symbol stream combining has been proposed as a method for arraying signals at different antennas. If the received symbol streams are recorded on tape, it is desirable to limit the required storage without significantly affecting the performance. It is shown that 4-bit quantized symbols introduce an E sub b/N sub o penalty of only 0.05 dB.

Pollara, F.↗
NASA NTRS1988

Convolution-controlled rotation and scale invariance in optical correlation

A method is presented for evoking a controlled, continuously-variable degree of rotation- and scale-invariance in optical correlation; the method is suitable for off-line computation of filters, though not for real-time computation. While a closed-form solution for the blur kernels has thus far evaded solution, a digital approximation method has been presented. A simulated correlation run with real, frame-grabbed imagery has indicated the method's desired performance. These Gaussian blur kernels can be replaced with box-car kernels or other blur kernels suitable for the given correlation-task.

Juday, Richard D.↗
NASA NTRS1989

Quantization effects in Viterbi decoding rate 1/n convolutional codes

A Viterbi decoder's performance loss due to quantizing data from the additive white Gaussian noise (AWGN) channel is studied. An optimal quantization scheme and branch metric calculation method are presented. The uniformly quantized channel capacity C(sub u)(q) is used to determine the smallest number of quantization bits q that does not cause a significant loss. The quantizer stepsize which maximizes C(sub u)(q) almost minimizes the decoder bit error rate (BER). However, a slightly larger stepsize is better, like the value that minimizes the Bhattacharyya bound. The range and renormalization of state metrics is analyzed, in particular for K = 15 decoders such as the Big Viterbi Decoder (BVD) for the Galileo mission. These results are required to design reduced hardware complexity Viterbi decoders with a negligible quantization loss.

Onyszchuk, I. M.↗