End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping

被引：0
作者：
Aref, Vahid [1 ]
Chagnon, Mathieu [1 ]
机构：
[1] Nokia, Magirusstr 8, D-70469 Stuttgart, Germany
来源：
2022 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC) | 2022年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We present a novel autoencoder-based learning of joint geometric and probabilistic constellation shaping for coded-modulation systems. It can maximize either the mutual information (for symbol-metric decoding) or the generalized mutual information (for bit-metric decoding). (C) 2022 The Author(s)
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页数：3
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