.png){kind=icon}
返回
Spatio-Temporal Electromagnetic Kernel Learning for Channel Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/TWC.2025.3635412.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate channel prediction is essential for addressing channel aging caused by user mobility. However, the actual channel variations over time are highly complex in high-mobility scenarios, which makes it difficult for existing predictors to obtain future channels accurately. The low accuracy of channel predictors leads to difficulties in supporting reliable communication. To overcome this challenge, we propose a channel predictor based on spatio-temporal electromagnetic (EM) kernel learning (STEM-KL). Specifically, inspired by recent advancements in electromagnetic information theory (EIT), the STEM kernel function is derived. The velocity and the concentration kernel parameters are designed to reflect the time-varying propagation of the wireless signal. We obtain the parameters through kernel learning. Then, the future channels are predicted by computing their Bayesian posterior, with the STEM kernel acting as the prior. To further improve the stability and model expressibility, we propose a grid-based EM mixed kernel learning (GEM-KL) scheme. We design the mixed kernel to be a convex combination of multiple sub-kernels, where each sub-kernel corresponds to a grid point in the set of pre-selected parameters. This approach transforms the non-convex STEM kernel learning problem into a convex grid-based problem that can be easily solved by weight optimization. Finally, simulation results verify that the proposed STEM-KL and GEM-KL schemes can achieve more accurate channel prediction. This indicates that EIT can improve the performance of wireless systems efficiently.
Keyword:
Channel prediction
electromagnetic information theory (EIT)
spatio-temporal electromagnetic kernel learning (STEM-KL)
grid-based electromagnetic mixed kernel learning (GEM-KL)
multi-input multi-output (MIMO)
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
Opening the black box: interpretability of machine learning algorithms in electrocardiography打开黑匣子: 心电图中机器学习算法的可解释性.png)
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
OAAI
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCESIF3.7
OAAI分享收藏Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD SystemsCHINA COMMUNICATIONSOAAI
CHINA COMMUNICATIONSIF3.1
OAAI分享收藏Electromagnetic information theory-based statistical channel model for improved channel estimation基于电磁信息理论的统计信道模型以提高信道估计性能
IF0
PREAI
分享收藏Horse (Electromagnetics) is More Important Than Horseman (Information) for Wireless Transmission
PREAI
分享收藏Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning信号处理、通信和机器学习中的主要最小化算法
PREAI
分享收藏
查看更多内容