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Abstract To deliver on the promise of 5G, network providers and application developers need to understand the factors impacting millimetre wave (mmWave) 5G throughput. Missing data, however, pose significant challenges for modelling throughput. Even in controlled settings, signal strength data may be only intermittently observed when a device’s connection is weak, leading to missing predictor values in model training. In addition, users may choose not to share their data once the model is deployed, meaning that key predictors may be missing when we want to predict throughput for their devices. To address these challenges, we introduce boosted additive model for data with missing observation (BAMMO), a novel additive model estimator obtained via a componentwise boosting algorithm that naturally incorporates data with missing values in model fitting. We validate BAMMO’s approach to handling missing data by comparing it with competing methods on real 5G network data with a high proportion of missing values and in simulations, finding that it delivers more accurate predictions and takes less time to compute. To identify key predictors of mmWave 5G throughput, we develop a novel extension of sparsity oriented importance learning for BAMMO, giving us a measure of variable importance based on the entire boosting solution path rather than a single selected model. 

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%. 

In this paper we develop a novel disruptionresilient approach for real-time, high-resolution sensor data delivery over multiple wireless channels for military autonomous systems such as drones, autonomous vehicles and robots. We design two innovative neural multiple description codecs (neural MDCs) which compress and encode images into multiple independently decodable and mutually refineable streams. Our approach not only achieves high compression efficiency, but also enables the effective use of multiple diverse radio channels for real-time delivery of high-resolution sensor data while ensuring disruption resiliency. Using benchmark image/video sensor datasets as well as real-world 5G traces, we evaluate and demonstrate the efficacy of both neural MDC codecs for highresolution sensor data streaming over multiple radio channels under various jamming scenarios.