scholarly journals DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems

2020 ◽  
pp. 1-1
Author(s):  
Yi Zhang ◽  
Akash Doshi ◽  
Rob Liston ◽  
Wai-tian Tan ◽  
Xiaoqing Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Receiver Design ◽  
Ieee 802.11Ax

scholarly journals Receiver Design for Faster-Than-Nyquist Signaling: Deep-Learning-Based Architectures

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 68866-68873
Author(s):  
Peiyang Song ◽  
Fengkui Gong ◽  
Qiang Li ◽  
Guo Li ◽  
Haiyang Ding
Keyword(s):  
Deep Learning ◽  
Receiver Design

scholarly journals Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design

2019 ◽  
Vol 8 (1) ◽  
pp. 177-180 ◽  
Author(s):  
Songyan Xue ◽  
Yi Ma ◽  
Na Yi ◽  
Rahim Tafazolli
Keyword(s):  
Deep Learning ◽  
Receiver Design ◽  
Unsupervised Deep Learning

An adaptive deep learning-based UAV receiver design for coded MIMO with correlated noise

2021 ◽  
pp. 101365
Author(s):  
Zizhi Wang ◽  
Wenqi Zhou ◽  
Lunyuan Chen ◽  
Fasheng Zhou ◽  
Fusheng Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Receiver Design ◽  
Correlated Noise

Deep Learning Noncoherent UWB Receiver Design

2021 ◽  
pp. 1-1
Author(s):  
Sanjeev Sharma ◽  
Kuntal Deka ◽  
Manish Mandloi
Keyword(s):  
Deep Learning ◽  
Receiver Design ◽  
Uwb Receiver

scholarly journals Deep Learning for Joint MIMO Detection and Channel Decoding

2018 ◽  
Author(s):  
Taotao Wang ◽  
Lihao Zhang ◽  
Soung Chang Liew
Keyword(s):  
Deep Learning ◽  
Current Model ◽  
Optimal Solution ◽  
Receiver Design ◽  
Maximum Likelihood Decoding ◽  
Mimo Detection ◽  
Channel Decoding ◽  
Iterative Receivers ◽  
Model Based ◽  
Mimo Receivers

We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. However, due to the complex MIMO signal model, the optimal solution to the joint MIMO detection and channel decoding problem (i.e., the maximum likelihood decoding of the transmitted codewords from the received MIMO signals) is computationally infeasible. As a practical measure, the current model-based MIMO receivers all use suboptimal MIMO decoding methods with affordable computational complexities. This work applies the latest advances in deep learning for the design of MIMO receivers. In particular, we leverage deep neural networks (DNN) with supervised training to solve the joint MIMO detection and channel decoding problem. We show that DNN can be trained to give much better decoding performance than conventional MIMO receivers do. Our simulations show that a DNN implementation consisting of seven hidden layers can outperform conventional model-based linear or iterative receivers. This performance improvement points to a new direction for future MIMO receiver design.

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