Multi-Task Learning as enabler for General-Purpose AI-native RAN
It addresses scalability issues for AI-native networks in telecommunications, but the work is incremental as it applies existing MTL techniques to a specific domain.
This study tackles the challenge of managing multiple independent machine learning workloads on resource-constrained base stations in AI-native 6G networks by exploring multi-task learning (MTL) for four Radio Access Network tasks, finding that customized MTL architectures can perform as well as or better than single-task learning in simulations.
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.