Secondary Carrier Prediction Under the Lens of Multi-Task Learning Paradigm

Secondary Carrier Prediction (SCP) is a data-driven Radio Access Network (RAN) feature that ensures the best possible cell and frequency carrier assignment to a User Equipment (UE), without the UE needing to perform an excessive amount of inter-frequency measurements and reporting. In this paper, we investigate if the performance of SCP can be further enhanced by jointly training it with other data-driven RAN tasks at the network edge in a Multi-Task Learning (MTL) fashion. We evaluate the trained SCP model performance when it runs a Mobility Load Balancing (MLB) downstream task under different task groupings. Results show that for the simulation scenario considered in this work, about 29.6% improvement in load balancing index is achieved when SCP is used in MTL mode over Single Task Learning (STL) mode during inference.