.png){kind=icon}
返回
Robust Online CSI Estimation in a Complex Environment
.png){kind=icon}
PRE
AI
DOI：10.1109/TWC.2022.3165588.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Channel state information (CSI) estimation is one of the key techniques for improving the performance of wireless communication systems. Meanwhile, the fifth generation wireless communication systems require higher accuracy and lower latency for CSI estimation. In this paper, the methods of noise modeling and online learning are combined to improve the accuracy and reduce the latency. The complex noise environment (considering noise and interference together) is modeled as a specific mixture of Gaussian (MoG) distribution because of its widely approximation capability to any continuous distribution. The MoG CSI estimation (MoG-CE) model and expectation maximization (EM) algorithm are introduced as one of the baseline methods. Further, the parameters of the model can be updated in real time based on the prior knowledge of historical information. Therefore, the online MoG CSI estimation (O-MoG-CE) model and online MoG dynamic CSI estimation (O-MoG-D-CE) model are proposed for time-invariant and time-varying CSI estimations, respectively. The above models can not only self-adapt to various complex communication scenarios robustly but also achieve online and dynamic CSI estimation to improve the accuracy and reduce the latency significantly. In addition, the proposed models can be formulated as standard maximum a posteriori estimations and efficient online expectation maximization (OEM) algorithms are applied for the estimations in a pure machine learning fashion. Comparing with baseline methods, the simulation results demonstrate the superiority of the proposed methods in terms of the accuracy, latency and computation consumption.
Keyword:
Estimation
Wireless communication
Computational modeling
Real-time systems
MIMO communication
Interference
Complexity theory
CSI estimation
noise modeling
online learning
mixture of Gaussian
maximum a posteriori
online expectation maximization
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
MMWAVE MASSIVE-MIMO-BASED WIRELESS BACKHAUL FOR THE 5G ULTRA-DENSE NETWORK5g超密集网络中基于MMWAVE massive-mimo的无线回传
OAAI
OAAI分享收藏Pazopanib, a novel multi-kinase inhibitor, shows potent antitumor activity in colon cancer through PUMA-mediated apoptosis.png)
Oncotarget
OAAI
OncotargetIF0
OAAI分享收藏Photoinduced reactions of PtCl62–with saturated hydrocarbons and other C–H containing compounds
PREAI
分享收藏
查看更多内容