返回
Fixed Rank Kriging for Cellular Coverage Analysis
DOI:10.1109/TVT.2016.2599842.png)
摘要
En 中文
Coverage planning and optimization is one of the most crucial tasks for a radio network operator. Efficient coverage optimization requires accurate coverage estimation. This estimation relies on geo-located field measurements that are gathered today during highly expensive drive tests (DT) and will be reported in the near future by users' mobile devices thanks to the Third-Generation Partnership Project (3GPP) minimization of drive tests (MDT) feature. This feature consists of an automatic reporting of the radio measurements associated with the geographic location of the user's mobile device. Such a solution is still costly in terms of battery consumption and signaling overhead. Therefore, predicting the coverage on a location where no measurements are available remains a key and challenging task. This paper describes a powerful tool that gives an accurate coverage prediction on the whole area of interest: It builds a coverage map by spatially interpolating geo-located measurements using the Kriging technique. This paper focuses on the reduction of the computational complexity of the Kriging algorithm by applying fixed rank Kriging (FRK). The performance evaluation of the FRK algorithm both on simulated measurements and real field measurements shows a good tradeoff between prediction efficiency and computational complexity. In order to go a step further toward the operational application of the proposed algorithm, a multicellular use case is studied. Simulation results show good performance in terms of coverage prediction and detection of the best serving cell.
Keyword:
Coverage map
expectation-maximization (EM) algorithm
fixed rank Kriging (FRK)
radio environment map (REM)
spatial statistics
wireless network
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
引用论文
Enabling breakthroughs in Parkinson’s disease with wearable technologies and big data analytics
mHealth
IF0

