Title
Parallel-Amplitude Architecture and Subset Ranking for Fast Distribution Matching
Abstract
A distribution matcher (DM) maps a binary input sequence into a block of nonuniformly distributed symbols. To facilitate the implementation of shaped signaling, fast DM solutions with high throughput and low serialism are required. We propose a novel DM architecture with parallel amplitudes (PA-DM) for which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m-1$ </tex-math></inline-formula> component DMs, each with a different binary output alphabet, are operated in parallel in order to generate a shaped sequence with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> amplitudes. With negligible rate loss compared to a single nonbinary DM, PA-DM has a parallelization factor that grows linearly with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> , and the component DMs have reduced output lengths. For such binary-output DMs, a novel constant-composition DM (CCDM) algorithm based on subset ranking (SR) is proposed. We present SR-CCDM algorithms that are serial in the minimum number of occurrences of either binary symbol for mapping, and fully parallel for demapping. For distributions that are optimized for the additive white Gaussian noise (AWGN) channel, we numerically show that PA-DM combined with SR-CCDM can reduce the number of sequential processing steps by more than an order of magnitude, while having a rate loss that is comparable to conventional nonbinary CCDM with arithmetic coding.
Year
DOI
Venue
2020
10.1109/TCOMM.2020.2966693
IEEE Transactions on Communications
Keywords
DocType
Volume
Probabilistic logic,Modulation,AWGN channels,Throughput,Encoding,Forward error correction,Decoding
Journal
68
Issue
ISSN
Citations 
4
0090-6778
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Tobias Fehenberger1528.59
David S. Millar297.73
Toshiaki Koike-Akino361067.09
Keisuke Kojima41613.05
Kieran Parsons53813.60