Constant Composition Distribution Matching

By Patrick Schulte, Georg Böcherer. IEEE Transactions on Information Theory, 2015

584 citations recorded by the source corpus.

Distribution matching transforms independent and Bernoulli(1/2) distributed input bits into a sequence of output symbols with a desired distribution. Fixed-to-fixed length, invertible, and low complexity encoders and decoders based on constant composition and arithmetic coding are presented. The encoder achieves the maximum rate, namely, the entropy of the desired distribution, asymptotically in the blocklength. Furthermore, the normalized divergence of the encoder output and the desired distribution goes to zero in the blocklength.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9fef1ddebb5b177722d9a247e6b113f3f717d8c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

On Probabilistic Shaping of Quadrature Amplitude Modulation for the Nonlinear Fiber Channel (2016, background): “64QAM symbols are generated with the constant-composition distribution matcher [24] and”
Bandwidth Efficient and Rate-Matched Low-Density Parity-Check Coded Modulation (2015, methodology): “For binary output (|A| = 2), an algorithm similar to [11] was proposed in [65].”
Rate Adaptation and Reach Increase by Probabilistically Shaped 64-QAM: An Experimental Demonstration (2016, methodology): “We use Constant Composition Distribution Matching (CCDM) as proposed in [12].”
Probabilistic Constellation Shaping for Optical Fiber Communications (2019, methodology): “This is fulﬁlled by CCDM, as the block length n s → ∞ .”