A Machine Learning Based Scheme for Indoor/Outdoor Classification in Wireless Communication Networks

Fifth generation (5G) New Radio (NR), was developed to offer more flexibility to meet new service requirements. Meanwhile, machine learning (ML) has proven successful in a variety of tasks, such as natural language processing, computer vision, and pattern recognition, in particular, which is proven to have a performance that is proportional to the total amount of available data. In NR, the capability to locate users is still one of the critical obstacles when mobile operator is planning and optimizing the cellular networks. Developing the technique to distinguish indoor from outdoor users' traffic pattern can achieve higher efficiency in terms of resource management and which results in larger economic benefit. In this paper, we present a pattern classifier based on decision tree to solve the indoor/outdoor classification problem. More specifically, rules for classification of indoor/outdoor users are generated by repeatedly splitting the features from cellular network key performance indicators (KPIs) which utilize the measurement criteria of entropy from the information theory community.