5 citations · 9 across the 8 of their papers we have counts for
8 papers
HyperCEUNet: Parameter-Aware Hypernetwork-Driven UNet for Channel Estimation
Ke Ma, Feng Wang, Lihui Lei +1
Deep learning-based channel estimation has been recognized as a promising technique for sixth-generation wireless systems. However, most existing approaches rely solely on least-sq…
TypeII-CsiNet: CSI Feedback with TypeII Codebook
Yiliang Sang, Ke Ma, Yang Ming +2
The latest TypeII codebook selects partial strongest angular-delay ports for the feedback of downlink channel state information (CSI), whereas its performance is limited due to the…
Near-Field Communications with Block-Dominant Compressed Sensing: Fundamentals, Approaches, and Future Directions
Liyang Lu, Ke Ma, Yue Wang +1
In the context of extremely large-scale antenna arrays deployed in sixth-generation (6G) mobile networks, near-field (NF) communications have gained considerable attention. Unlike…
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…
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…
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…
Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems
Ke Ma, Dongxuan He, Hancun Sun +2
Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. To address this issue, in this paper, we propose a wide beam bas…
Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information
Ke Ma, Dongxuan He, Hancun Sun +1
Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods ar…