5 citations · 9 across the 8 of their papers we have counts for
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cs.IT2023
Improving the Performance of R17 Type-II Codebook with Deep Learning
Ke Ma, Yiliang Sang, Yang Ming +3
The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for…
cs.IT2023★ 1 cited
Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback
Ke Ma, Yiliang Sang, Yang Ming +3
Deep learning based channel state information (CSI) feedback in frequency division duplex systems has drawn much attention in both academia and industry. In this paper, we focus on…
cs.IT2021★ 5 cited
Deep Learning for Beam-Management: State-of-the-Art, Opportunities and Challenges
Ke Ma, Zhaocheng Wang, Wenqiang Tian +2
Benefiting from huge bandwidth resources, millimeter-wave (mmWave) communications provide one of the most promising technologies for next-generation wireless networks. To compensat…