Multilevel MIMO Detection with Deep Learning

被引：0

作者：

Corlay, Vincent ^{[1
,3
]}

Boutros, Joseph J. ^{[2
]}

Ciblat, Philippe ^{[1
]}

Brunel, Loic ^{[3
]}

机构：

[1] Telecom ParisTech, 46 Rue Barra.ult, F-75013 Paris, France

[2] Texas A&M Univ, Doha, Qatar

[3] Mitsubishi Elect R&D, Rennes, France

来源：

2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2018年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A quasi-static flat multiple-antenna channel is considered. We show how real multilevel modulation symbols can be detected via deep neural networks. A multi-plateau sigmoid function is introduced. Then, after showing the DNN architecture for detection, we propose a twin-network neural stricture. Batch size and training statistics for efficient learning are investigated. Near-Maximum-Likelihood performance with a relatively reasonable number of parameters is achieved.

引用

页码：1805 / 1809

页数：5

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