  The realization of data-driven AI-native architecture envisioned for 6G and
beyond networks can eventually lead to multiple machine learning (ML) workloads
distributed at the network edges driving downstream tasks like secondary
carrier prediction, positioning, channel prediction etc. The independent
life-cycle management of these edge-distributed independent multiple workloads
sharing a resource-constrained compute node e.g., base station (BS) is a
challenge that will scale with denser deployments. This study explores the
effectiveness of multi-task learning (MTL) approaches in facilitating a
general-purpose AI native Radio Access Network (RAN). The investigation focuses
on four RAN tasks: (i) secondary carrier prediction, (ii) user location
prediction, (iii) indoor link classification, and (iv) line-of-sight link
classification. We validate the performance using realistic simulations
considering multi-faceted design aspects of MTL including model architecture,
loss and gradient balancing strategies, distributed learning topology, data
sparsity and task groupings. The quantification and insights from simulations
reveal that for the four RAN tasks considered (i) adoption of customized gate
control-based expert architecture with uncertainty-based weighting makes MTL
perform either best among all or at par with single task learning (STL) (ii)
LoS classification task in MTL setting helps other tasks but its own
performance is degraded (iii) for sparse training data, training a single
global MTL model is helpful but MTL performance is on par with STL (iv) optimal
set of group pairing exists for each task and (v) partial federation is much
better than full model federation in MTL setting.
