Identification and Analysis of a Unique Cell Selection Phenomenon in Public Unlicensed Cellular Networks Through Machine Learning

Abstract

Cellular operators deploy 4G License Assisted Access (LAA) and 5G NR-U base stations in the unlicensed spectrum to enhance overall network capacity. This work highlights a unique phenomenon related to Physical Cell Id (PCI) that is observed in public LAA operator deployments. Notably, the licensed and unlicensed carriers of a device may have the same PCI or different PCIs. The phenomenon is triggered by the combined effect of unlicensed deployment architectures and cell selection mechanisms. Consequently, the phenomenon will intensify in the 5G NR-U, whose public deployment will soon begin. Unfortunately, the impact of this phenomenon on coexistence network performance is unexplored. It is also desirable to accurately identify the PCI scenarios at the device for improved cell selection and network performance. However, the data imbalance makes the classification problem challenging. This work addresses these problems through the following approach. Operator data from three LAA cellular providers is gathered and analyzed using machine learning algorithms. The impact of the phenomenon on LTE, LAA, and Wi-Fi components is demonstrated in three steps: First, the variation in network performance prediction accuracy in the PCI scenarios is examined. Second, the efficacy of numerosity reduction techniques used in data-driven cell selection is evaluated in both PCI scenarios. The third step entails a comparison of operator data analysis with network measurements. On-site experiments are conducted at the same PCI and different PCI sites to study differences in real-time network performance. A controlled LTE-WiFi coexistence environment is created and multiple traffic categories are considered. Finally, a class-weight-based solution is proposed for PCI scenario identification. F-score of 0.75 and AUC-ROC of 0.84 is achieved for LAA, with a minimalist feature set consisting of SINR and Throughput.

Keywords

• Unlicensed networks
• cell selection
• machine learning
• imbalanced classification
• LAA
• NR-U