Improving the performance of R17 Type-II codebook with deep learning

[PDF] arxiv.org

Neural Codebook Design for Network Beam Management

RM Dreifuerst, RW Heath Jr - arXiv preprint arXiv:2403.03053, 2024 - arxiv.org
Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for
large antenna systems. Mobile systems like 5G use a beam management framework that …
被引用次数：2 相关文章 所有 2 个版本

Dual-Band Super-Resolution Channel Prediction in High-Mobility MIMO Systems

Y Sang, K Ma, Z Wang, S Chen - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
For multiple-input multiple-output systems, channel prediction is crucial for mitigating
channel aging in mobile scenarios. The existing channel prediction schemes typically …
相关文章 所有 2 个版本

Deep Learning Empowered CSI Acquisition and Feedback for B5G Wireless Systems

K Ma, Y Sang, Y Ming, J Lian, C Tian… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Deep learning based channel state information (CSI) acquisition and feedback in frequency
division duplex systems have drawn much attention in the beyond fifth-generation (B5G) …
被引用次数：1 相关文章
[PDF] arxiv.org

TypeII-CsiNet: CSI Feedback with TypeII Codebook

Y Sang, K Ma, Y Ming, J Lian, Z Wang - arXiv preprint arXiv:2405.12569, 2024 - arxiv.org
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 …
相关文章 所有 2 个版本