4 research outputs found
Multi-Class Source-Channel Coding
This paper studies an almost-lossless
source-channel coding scheme in which source messages
are assigned to different classes and encoded with a channel
code that depends on the class index. The code performance
is analyzed by means of random-coding error exponents and
validated by simulation of a low-complexity implementation
using existing source and channel codes. While each class
code can be seen as a concatenation of a source code and a
channel code, the overall performance improves on that of
separate source-channel coding and approaches that of joint
source-channel coding when the number of classes increase
BEAST decoding for block codes
BEAST is a Bidirectional Efficient Algorithm for Searching code Trees. In this paper, it is used for decoding block codes over a binary-input memoryless channel. If no constraints are imposed on the decoding complexity (in terms of the number of visited nodes during the search), BEAST performs maximum-likelihood (ML) decoding. At the cost of a negligible performance degradation, BEAST can be constrained to perform almost-ML decoding with significantly reduced complexity. The benchmark for the complexity assessment is the number of nodes visited by the Viterbi algorithm operating on the minimal trellis of the code. The decoding complexity depends on the trellis structure of a given code, which is illustrated by three different forms of the generator matrix for the (24, 12, 8) Golay code. Simulation results that assess the error-rate performance and the decoding complexity of BEAST are presented for two longer codes
BEAST decoding for block codes
BEAST is a Bidirectional Efficient Algorithm for Searching code Trees. In this paper, it is used for decoding block codes over the additive white Gaussian noise (AWGN) channel. If no constraints are imposed on the decoding complexity (in terms of the number of visited nodes during the search), BEAST performs maximum-likelihood (ML) decoding. At the cost of a negligible performance degradation, BEAST can be constrained to perform almost-ML decoding with significantly reduced complexity. The benchmark for the complexity assessment is the number of nodes visited by the Viterbi algorithm operating on the minimal trellis of the code. The decoding complexity depends on the trellis structure of a given code, which is illustrated by three different forms of the generator matrix for the (24, 12, 8) Golay code. Simulation results are also presented for two other code