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.…