Engineering topics
Dolinar, S.
Publications and source records attributed to Dolinar, S..
Onboard Science Processing and Buffer Management for Intelligent Deep Space Communications
We present an integrated system for the intelligent progressive transmission of data for deep space communications.
Turbo Codes and Turbo Decoders for Deep Space Communication
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Turbo Code Performance as a Function of Code Block Size
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Turbo Codes and Space Communications
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Transfer function bounds on the performance of turbo codes
In this article we apply transfer function bounding techniques to obtain upper bounds on the bit-error rate for maximum likelihood decoding of turbo codes constructed with random permutations. These techniques are applied to two turbo codes with constraint length 3 and later extended to other codes. The performance predicted by these bounds is compared with simulation results. The bounds are useful in estimating the 'error floor' that is difficult to measure by simulation, and they provide insight on how to lower this floor. More refined bounds are needed for accurate performance measures at lower signal-to-noise ratios.
Weight distributions for turbo codes using random and nonrandom permutations
This article takes a preliminary look at the weight distributions achievable for turbo codes using random, nonrandom, and semirandom permutations. Due to the recursiveness of the encoders, it is important to distinguish between self-terminating and non-self-terminating input sequences. The non-self-terminating sequences have little effect on decoder performance, because they accumulate high encoded weight until they are artificially terminated at the end of the block. From probabilistic arguments based on selecting the permutations randomly, it is concluded that the self-terminating weight-2 data sequences are the most important consideration in the design of constituent codes; higher-weight self-terminating sequences have successively decreasing importance. Also, increasing the number of codes and, correspondingly, the number of permutations makes it more and more likely that the bad input sequences will be broken up by one or more of the permuters. It is possible to design nonrandom permutations that ensure that the minimum distance due to weight-2 input sequences grows roughly as the square root of (2N), where N is the block length. However, these nonrandom permutations amplify the bad effects of higher-weight inputs, and as a result they are inferior in performance to randomly selected permutations. But there are 'semirandom' permutations that perform nearly as well as the designed nonrandom permutations with respect to weight-2 input sequences and are not as susceptible to being foiled by higher-weight inputs.
Enhanced decoding for the Galileo low-gain antenna mission: Viterbi redecoding with four decoding stages
The Galileo low-gain antenna mission will be supported by a coding system that uses a (14,1/4) inner convolutional code concatenated with Reed-Solomon codes of four different redundancies. Decoding for this code is designed to proceed in four distinct stages of Viterbi decoding followed by Reed-Solomon decoding. In each successive stage, the Reed-Solomon decoder only tries to decode the highest redundancy codewords not yet decoded in previous stages, and the Viterbi decoder redecodes its data utilizing the known symbols from previously decoded Reed-Solomon codewords. A previous article analyzed a two-stage decoding option that was not selected by Galileo. The present article analyzes the four-stage decoding scheme and derives the near-optimum set of redundancies selected for use by Galileo. The performance improvements relative to one- and two-stage decoding systems are evaluated.
Testing the performance of feedback concatenated decoder with a nonideal receiver
One of the inherent problems in testing the feedback concatenated decoder (FCD) at our operating symbol signal-to-noise ratio (SSNR) is that the bit-error rate is so low that we cannot measure it directly through simulations in a reasonable time period. This article proposes a test procedure that will give a reasonable estimate of the expected losses even though the number of frames tested is much smaller than needed for a direct measurement. This test procedure provides an organized robust methodology for extrapolating small amounts of test data to give reasonable estimates of FCD loss increments at unmeasurable miniscule error rates. Using this test procedure, we have run some preliminary tests on the FCD to quantify the losses due to the fact that the input signal contains multiplicative non-white non-Gaussian noises resulting from the buffered telemetry demodulator (BTD). Besides the losses in the BTD, we have observed additional loss increments of 0.3 to 0.4 dB at the output of the FCD for several test cases with loop signal-to-noise ratios (SNR's) lower than 20 dB. In contrast, these loss increments were less than 0.1 dB for a test case with the subcarrier loop SNR at about 28 dB. This test procedure can be applied to more extensive test data to determine thresholds on the loop SNRs above which the FCD will not suffer substantial loss increments.
Minimal trellises for linear block codes and their duals
We consider the problem of finding a trellis for a linear block code that minimizes one or more measures of trellis complexity for a fixed permutation of the code. We examine constraints on trellises, including relationships between the minimal trellis of a code and that of the dual code. We identify the primitive structures that can appear in a minimal trellis and relate this to those for the minimal trellis of the dual code.
Trellis complexity bounds for decoding linear block codes
We consider the problem of finding a trellis for a linear block code that minimizes one or more measures of trellis complexity. The domain of optimization may be different permutations of the same code or different codes with the same parameters. Constraints on trellises, including relationships between the minimal trellis of a code and that of the dual code, are used to derive bounds on complexity. We define a partial ordering on trellises: If a trellis is optimum with respect to this partial ordering, if has the desirable property that it simultaneously minimizes all of the complexity measures examined. We examine properties of such optimal trellises and give examples of optimal permutations of codes, most notably the (48,24,12) quadratic residue code.
Trellis Decoding Complexity of Linear Block Codes
We consider the problem of finding a trellis for a linear block code that minimizes one or more measures of trellis complexity. The domain of optimization may be different permutations of the same code, or different codes with the same parameters. Constraints on trellises, including relationships between the minimal trellis of a code and that of the dual code, are used to derive bounds on complexity. We define a partial ordering on trellises: if a trellis is optimum with respect to this partial ordering, it has the desirable property that it simultaneously minimizes all of the complexity measures examined. We examine properties of such optimal trellises and give examples of optimal permutations of codes, most notably the (48,24,12) quadratic residue code.
(abstract) Improvements on Probability of Error Bounds for Block Codes on the Gaussian Channel
We are interested in the problem of finding good codes for the Gaussian channel at low signal-to-noise ratio, and in estimating as accurately as possible their probability of error.
Enhanced Decoding for the Galileo Low-Gain Antenna Mission
Due to a malfunctioning high-gain antenna, the Galileo spacecraft is transmitting all its data through a low-gain antenna, and the data rate will seldom exceed 100 bits per second during its two-year tour of Jupiter's satellites.
Enhanced Decoding for the Galileo Low-Gain Antenna Mission
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Maximum-likelihood soft-decision decoding of block codes using the A* algorithm
The A* algorithm finds the path in a finite depth binary tree that optimizes a function. Here, it is applied to maximum-likelihood soft-decision decoding of block codes where the function optimized over the codewords is the likelihood function of the received sequence given each codeword. The algorithm considers codewords one bit at a time, making use of the most reliable received symbols first and pursuing only the partially expanded codewords that might be maximally likely. A version of the A* algorithm for maximum-likelihood decoding of block codes has been implemented for block codes up to 64 bits in length. The efficiency of this algorithm makes simulations of codes up to length 64 feasible. This article details the implementation currently in use, compares the decoding complexity with that of exhaustive search and Viterbi decoding algorithms, and presents performance curves obtained with this implementation of the A* algorithm for several codes.
Integer cosine transform compression for Galileo at Jupiter: A preliminary look
The Galileo low-gain antenna mission has a severely rate-constrained channel over which we wish to send large amounts of information. Because of this link pressure, compression techniques for image and other data are being selected. The compression technique that will be used for images is the integer cosine transform (ICT). This article investigates the compression performance of Galileo's ICT algorithm as applied to Galileo images taken during the early portion of the mission and to images that simulate those expected from the encounter at Jupiter.