Entropy Field Decomposition Based Outage Detection for Ultra-Dense Networks

Ambitious quality of experience expectations from 5G mobile cellular networks have spurred the research towards ultra-dense heterogeneous networks (UDHNs). However, due to coverage limitations of millimeter wave cells and lack of coverage data in UDHNs, discovering coverage lapses in such 5G networks may become a major challenge. Recently, numerous studies have explored machine learning-based techniques to detect coverage holes and cell outages in legacy networks. Majority of these techniques are susceptible to noise in the coverage data and only characterize outages in the spatial domain. Thus, the temporal impact of an outage, i.e., the duration of its presence remains unidentified. In this paper, for the first time, we present an outage detection solution that characterizes outages in both space and time while also being robust to noise in the coverage data. We do so by employing entropy field decomposition (EFD) which is a combination of information field theory and entropy spectrum pathways theory. We demonstrate that compared to other techniques such as independent component analysis and k-means clustering, EFD returns accurate detection results for outage detection even in the presence of heavy shadowing in received signal strength data which makes it ideal for practical implementation in emerging mobile cellular networks.