Master Thesis: Aggregated ML Models in Radio Networks

## Join our Team About this opportunity: As mobile networks are becoming increasingly complex, one way of increasing performance and making the mobile network more self-autonomous is to apply machine learning techniques. For example, machine learning can be used to predict signal strength for certain frequencies or cells given a position in the radio environment. The scope of such prediction models can be on different aggregation levels, enabling possibility of batch predictions that can be used to optimize prediction time or to optimize the utilization of hardware resources. This thesis will focus around comparing model aggregation on cell, frequency, sector, node or even network level. Another aspect of aggregated models is how to handle the lifecycle of such models, for example deciding when a model needs to be retrained. The data set will consist of measurements already collected by Ericsson from a real radio access network. What you will do: * Investigate and compare different aggregation levels for coverage models. * Evaluate performance of the coverage models when doing batch predictions. * The thesis will be concluded with a result presentation for the Ericsson development team. The skills you bring: * Master´s student in computer science, computer engineering or statistics and machine learning. Why join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next. What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more. Primary country and city: Sweden (SE) || Linköping Req ID: 792029