.png){kind=icon}
返回
Density Estimation in Randomly Distributed Wireless Networks
.png){kind=icon}
OA
AI
DOI：10.1109/TWC.2022.3151918.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Networks of randomly distributed nodes appear in various fields, including forestry and wireless communications, and can often be modeled, using stochastic geometry theory, as Poisson point processs (PPPs). In these contexts, estimation of nodes density is important for monitoring and optimizing the network. Originally, this problem has been addressed in forestry where the trees are the nodes and, assuming these are distributed according to an infinite two-dimensional homogeneous PPP, the spatial density can be estimated by measuring the distances from one reference tree to its neighbors. However, in many other scenarios, nodes could result invisible with some probability, for example depending on distance. In this paper, we derive the Cramer-Rao bounds and new estimators for the node spatial density, taking into account a limited capability in sensing neighbors. As an example, we provide estimators of the spatial density of transmitting devices in wireless networks with links affected by thermal noise, path loss, and shadowing.
Keyword:
Wireless communication
Wireless networks
Maximum likelihood estimation
Vegetation
Wireless sensor networks
Forestry
Distance measurement
Spatial density estimation
Poisson point processes
Cramer-Rao bounds
maximum likelihood estimation
stochastic geometry
wireless networks
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
分享收藏The influence of habitat management on the breeding success of the Great Bittern Botaurus stellaris in Britain.png)
Ibis
IbisIF0
PREAI
分享收藏Impact of Node Density on Throughput and Delay Scaling in Multi-Hop Wireless Networks多跳无线网络中节点密度对吞吐量和时延缩放的影响
PREAI
分享收藏
查看更多内容