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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Vydáno v:IEEE communications letters Ročník 26; číslo 6; s. 1288 - 1292
Hlavní autoři: Liu, Zijian, Luo, Yang, Xu, Yan, Luo, Chunbo
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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 integrating a constrained precoding network with synchronous loss to predict hybrid precoders that approximate the corresponding fully digital precoders, where the constrained neural network guarantees the unit module and total transmission power while the synchronous loss maximizes the achievable rate. Experimental results show that the hybrid precoder generated from the proposed framework outperforms the benchmark hybrid precoders and could save more than half the inference time.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3162211