Outage Detection for Millimeter Wave Ultra-Dense HetNets in High Fading Environments.

Millimeter wave spectrum utilization and network densification are two of the fundamental technologies that will enable high user quality of experience required in 5th Generation mobile cellular networks. However, user sparsity in ultra-dense heterogeneous networks and coverage limitations of millimeter wave cells means reliability of such networks will become a key operational challenge. Recent studies have explored the use of machine learning techniques for outage detection in legacy and heterogeneous mobile cellular networks. However, machine learning techniques are highly susceptible to noise in the training data which can affect their outage detection accuracy. To counter these challenges, we present a novel outage detection method based on entropy field decomposition technique first introduced in [1]. The proposed method is able to detect cell outages with at least 96% accuracy even as the level of shadowing in the network is increased which makes it ideal for practical implementation in emerging ultra-dense heterogeneous networks with millimeter wave cells. The proposed solution is compared against k-means clustering for outage detection with results showing that not only does entropy field decomposition return higher true positive results, it also returns fewer false positive results compared to k-means clustering.