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Divergence Scaling of Fixed-Length, Binary-Output, One-to-One Distribution Matching
- Source :
- Proc. IEEE Int. Symp. on Information Theory 2017, pp. 3075-3079
- Publication Year :
- 2017
-
Abstract
- Distribution matching is the process of invertibly mapping a uniformly distributed input sequence onto sequences that approximate the output of a desired discrete memoryless source. The special case of a binary output alphabet and one-to-one mapping is studied. A fixed-length distribution matcher is proposed that is optimal in the sense of minimizing the unnormalized informational divergence between its output distribution and a binary memoryless target distribution. Upper and lower bounds on the unnormalized divergence are computed that increase logarithmically in the output block length $n$. It follows that a recently proposed constant composition distribution matcher performs within a constant gap of the minimal achievable informational divergence.<br />Comment: 5 pages, 1 figure; Lemma 6 updated; This work will be presented at ISIT 2017
- Subjects :
- Computer Science - Information Theory
Subjects
Details
- Database :
- arXiv
- Journal :
- Proc. IEEE Int. Symp. on Information Theory 2017, pp. 3075-3079
- Publication Type :
- Report
- Accession number :
- edsarx.1701.07371
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/ISIT.2017.8007095