Search for: All records

5G and future 6G networks deploy cells with diverse combinations of access technologies, architectures, and radio frequency bands/channels. Cellular operators also employ carrier aggregation for higher data access speeds. We investigate the fundamental question of how to intelligently and dynamically configure and reconfigure a user equipment's serving cells to deliver the best network performance. Through comprehensive measurements across 12 cities in 5 countries, we experimentally show the wide availability, heterogeneity, and untapped performance gains of today's cell deployments. We then present a principled, performance-driven connectivity management framework, dubbed OPCM. It is a centralized solution deployed at the base station, allowing it to coordinate multiple UEs, enforce operator policies, and facilitate user fairness. Extensive evaluations show that OPCM improves the application QoE by up to 65.2%. 

Support for connected and autonomous vehicles (CAVs) is a major use case of 5G networks. Due to their large from factors, CAVs can be equipped with multiple radio antennas, cameras, LiDAR and other sensors. In other words, they are "giant" mobile integrated communications and sensing devices. The data collected can not only facilitate edge-assisted autonomous driving, but also enable intelligent radio resource allocation by cellular networks. In this paper we conduct an initial study to assess the feasibility of delivering multi-modal sensory data collected by vehicles over emerging commercial 5G networks. We carried out an "in-the-wild" drive test and data collection campaign between Minneapolis and Chicago using a vehicle equipped with a 360° camera, a LiDAR device, multiple smart phones and a professional 5G network measurement tool. Using the collected multi-modal data, we conduct trace-driven experiments in a local streaming testbed to analyze the requirements and performance of streaming multi-modal sensor data over existing 4G/5G networks. We reveal several notable findings and point out future research directions.