4 citations · 4 across the 4 of their papers we have counts for
4 papers
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test
Jing Zhang, Yunfeng He, Yu-Wen Li +2
Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. We…
Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding
Yunfeng He, Hengtao, He +2
Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power cons…
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
Yunfeng He, Jing Zhang, Shi Jin +2
This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori (…
TurboNet: A Model-driven DNN Decoder Based on Max-Log-MAP Algorithm for Turbo Code
Yunfeng He, Jing Zhang, Chao-Kai Wen +1
This paper presents TurboNet, a novel model-driven deep learning (DL) architecture for turbo decoding that combines DL with the traditional max-log-maximum a posteriori (MAP) algor…