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Hierarchical Distribution Matching: a Versatile Tool for Probabilistic\n Shaping

2019/11/19 by Stella Civelli, Civelli, Stella, Marco Secondini +1
Computer Science · Engineering · Mathematics · #Artificial intelligence #Computer science #Distribution (mathematics) #FOS: Computer and information sciences #FOS: Electrical engineering #Human Motion and Animation #Information Theory (cs.IT) #Matching (statistics) #Mathematics #Probabilistic logic #Signal Processing (eess.SP) #Statistics #Theoretical computer science #Time Series Analysis and Forecasting #Video Analysis and Summarization #cs.IT #eess.SP #electronic engineering #information engineering #math.IT

paper · pdf · doi:10.48550/arxiv.1911.08243

published in arXiv (Cornell University) (Cornell University) · The manuscript has been submitted to The Optical Networking and Communication (OFC) Conference 2020

openalex publication_date 2019/11/19 · arxiv created 2020/02/19 · arxiv updated 2020/02/20 · openalex created_date 2022/07/26 · openalex updated_date 2026/08/04

Abstract

The hierarchical distribution matching (Hi-DM) approach for probabilistic\nshaping is described. The potential of Hi-DM in terms of trade-off between\nperformance,complexity, and memory is illustrated through three case studies.\n

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