Ke Ma
Tsinghua University
19 Papers
2 Citations
Ke Ma is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Overhead (computing). The author has an hindex of 2, co-authored 8 publications.
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Papers
Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems
TL;DR: In this paper, a wide beam-based training approach is proposed to calibrate the beam direction according to the channel power leakage, and three deep learning assisted calibrated beam training schemes are proposed.
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Deep Learning Assisted Beam Prediction Using Out-of-Band Information
Ke Ma,Peiyao Zhao,Zhaocheng Wang +2 more
- 25 May 2020
TL;DR: A deep learning assisted beam prediction scheme using out-of-band information extracted from low-frequency channel state information (CSI) to facilitate the initial beam training in mmWave communications with over 94% accuracy in line- of-sight scenarios, which can reduce the overhead of beam training significantly.
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Virtual Angular-Domain Channel Estimation for FDD Based Massive MIMO Systems With Partial Orthogonal Pilot Design
TL;DR: Simulation results demonstrate that the proposed virtual angular-domain channel estimation scheme provides excellent MSE performance with much reduced pilot overhead and, consequently, enjoys much larger per-user achievable rate in comparison to the conventional schemes.
Deep Learning for mmWave Beam-Management: State-of-the-Art, Opportunities and Challenges
TL;DR: In this article , a review of the current state-of-the-art for beamforming in mmWave networks is presented, followed by the associated challenges and future research opportunities.
Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach
TL;DR: In this article , the authors proposed to fuse sub-6 GHz channel information and mmWave low-overhead measurement to predict the optimal mmWave beam in heterogeneous networks (HetNets) and reduce the overhead of both mmWave BS selection and beam training.
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