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A Deep Constrained and Synchronous Training Framework for Hybrid Precoding
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DOI：10.1109/LCOMM.2022.3162211.png)
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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.
Keyword:
Precoding
Radio frequency
Training
Hybrid power systems
Neural networks
Array signal processing
Propagation losses
Hybrid precoding
synchronous training
residual precoding network
spectral
efficiency
fully digital precoder
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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