A Deep Constrained and Synchronous Training Framework for Hybrid Precoding

To reduce the computational complexity and improve the achievable rate of hybrid precoding, we propose a deep constrained and synchronous training framework, which enables effective learning from the fully digital precoders and approaches the upper bound performance. The key innovation focuses on in...

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Vydané v:	IEEE communications letters Ročník 26; číslo 6; s. 1288 - 1292
Hlavní autori:	Liu, Zijian, Luo, Yang, Xu, Yan, Luo, Chunbo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Array signal processing efficiency fully digital precoder Hybrid power systems Hybrid precoding Neural networks Precoding Propagation losses Radio frequency residual precoding network spectral synchronous training Training Upper bounds
ISSN:	1089-7798, 1558-2558
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