Towards the use of Unsupervised Causal Learning in Wireless Networks Operation

Abstract

The current paradigm in Mobile Wireless Networks (MWNs) operation is being defied by the increasing importance of Machine Learning (ML) and Artificial Intelligence (AI). Nevertheless, another paradigm shift is rising with recent developments in causal inference and causal discovery, which, although having the potential to be applied to MWNs, have been relatively unexplored. This paper aims to develop a data-driven methodology using unsupervised ML and Conditional Independence Tests (CITs), typically used in causal discovery tasks, to identify distinct network performance patterns and pinpoint causal factors to explain them. The proposed methodology was first evaluated with crowdsourcing data from User Equipments (UEs). Afterwards, a dataset from a Long-Term Evolution (LTE) network, composed of a set of arbitrary performance indicators and configuration parameters, was considered. The crowdsourcing dataset, containing multiple network speed tests, revealed that the measured uplink throughput contributed the most to the observed performance patterns due to the used Radio Access Technologies (RATs). Furthermore, the LTE dataset revealed a causal relationship between the number of reserved signalling resources in the Physical Uplink Control Channel (PUCCH) and the UE uplink throughput. Notwithstanding, the key contribution of this paper is the consideration of causal-based concepts and methods for network operations enhancement.