Deep learning for wireless physical layer: Opportunities and challenges

T Wang, CK Wen, H Wang, F Gao… - China …, 2017 - ieeexplore.ieee.org
Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …
被引用次数：632 相关文章

[引用][C] Deep learning for wireless physical layer: Opportunities and challenges

T Wang, CK Wen, H Wang, F Gao, T Jiang… - China Communications, 2017 - cir.nii.ac.jp
Deep learning for wireless physical layer: Opportunities and challenges | CiNii Research CiNii
国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索フォームへ移動 論文・データを …

Deep Learning for Wireless Physical Layer: Opportunities and Challenges

T Wang, CK Wen, H Wang, F Gao, T Jiang… - arXiv e …, 2017 - ui.adsabs.harvard.edu
Abstract Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …
[PDF] cic-chinacommunications.cn

[PDF][PDF] Deep Learning for Wireless Physical Layer: Opportunities and Challenges

T Wang, CK Wen, H Wang, F Gao… - China …, 2017 - cic-chinacommunications.cn
Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Deep Learning for Wireless Physical Layer: Opportunities and Challenges

T Wang, CK Wen, H Wang, F Gao, T Jiang… - arXiv preprint arXiv …, 2017 - arxiv.org
Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …
[PDF] researchgate.net

[PDF][PDF] Deep Learning for Wireless Physical Layer: Opportunities and Challenges

T Wang, CK Wen, H Wang, F Gao, T Jiang, S Jin - researchgate.net
Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …