37 citations · 45 across the 5 of their papers we have counts for
5 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 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…
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
Jing Zhang, Hengtao He, Chao-Kai Wen +2
Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learn…
Artificial Intelligence-aided Receiver for A CP-Free OFDM System: Design, Simulation, and Experimental Test
Jing Zhang, Chao-Kai Wen, Shi Jin +1
Orthogonal frequency division multiplexing (OFDM), usually with sufficient cyclic prefix (CP), has been widely applied in various communication systems. The CP in OFDM consumes add…