.png){kind=icon}
返回
Channel Gain Map Tracking via Distributed Kriging
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2011.2113195.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A collaborative algorithm is developed to estimate the channel gains of wireless links in a geographical area. The spatiotemporal evolution of shadow fading is characterized by judiciously extending an experimentally verified spatial-loss field model. Kriged Kalman filtering (KKF), which is a tool with widely appreciated merits in spatial statistics and geosciences, is adopted and implemented in a distributed fashion to track the time-varying shadowing field using a network of radiometers. The novel distributed KKF requires only local message passing yet achieves a global view of the radio frequency environment through consensus iterations. Numerical tests demonstrate superior tracking accuracy of the collaborative algorithm compared with its noncollaborative counterpart. Furthermore, the efficacy of the global channel gain knowledge obtained is showcased in the context of cognitive radio resource allocation.
Keyword:
Channel tracking
cognitive radio
distributed algorithms
kriging
shadow fading
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
暂无机构信息
引用论文
Impaired B Cell Development and Proliferation in Absence of Phosphoinositide 3-Kinase p85α.png)
Science
ScienceIF0
PREAI
分享收藏
查看更多内容