Machine learning for 5G System Control and Automation

In comparison to 4G, there is a plethora of new applications envisioned for 5G, ranging from traditional efficient broadband communication links to more strictly reliable communication links suitable for critical communication in industrial applications or massive communication that is typically associated to the Internet of Things.

Telecommunication mast TV antennas wireless technology with blue sky

iStock/12521104In this project we develop solutions to increase the autonomy of 5G networks with the goal to improve network performance, reduce operation complexity, and increase resilience.

Employing probabilistic machine learning models in the project is a ground for an optimal decision making, in particular to satisfy the ultra-reliability requirements where uncertainty quantifications are crucial. The project aims to use probabilistic classification and regression frameworks to guarantee flexibility and autonomy of 5G components, and also use spatial and temporal models in order to take into account dynamic aspects such as device velocity, device movement path or daily/monthly variations of the network workload.

To autonomously adapt to various environmental changes and disturbances, the project aims to embed online learning components into our methods. Efficiency and scalability are challenges in the efficient development of the autonomous 5G environment, and development of appropriate big data processing methods is yet another aim of the project.

Researchers

External partner

Ericsson AB

Publications

Caroline Svahn, Oleg Sysoev, Mirsad Čirkić, Fredrik Gunnarsson and Joel Berglund, Inter-Frequency Radio Signal Quality Prediction for Handover, Evaluated in 3GPP LTE, 2019 IEEE 89th Vehicular Technology Conference.

Caroline Svahn and Oleg Sysoev (2022), Selective Imputation of Covariates in High Dimensional Censored Data, Journal of Computational and Graphical Statistics.

Research

Machine learning

Bayesian Learning for Spatiotemporal Processes in Transportation

The rapid deployment of streaming sensors have made spatiotemporal data increasingly common. In this project we develop probabilistic models for spatiotemporal data with applications in the field of transportation.

Probabilistic models and deep learning – bridging the gap

Probabilistic models and deep learning are two successful branches of machine learning. In this project, we will develop theory and methods related to the interplay between these technologies, enabling us to take advantage of the strengths of both.

WASP

WASP Machine learning

Division of Statistics and Machine Learning develops methods that autonomous systems use when learning from data. Our research focuses on probabilistic modeling and efficient algorithms for learning, prediction and decision making under uncertainty.

WASP - Wallenberg AI, Autonomous Systems and Software Program

The fourth industrial revolution is upon us, as automation becomes autonomy. LiU conducts outstanding research in several of the fields that are central to the Wallenberg AI Autonomous Systems and Software Program, WASP.