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.

通过实现面向 6G 及更高版本网络的基于数据驱动的 AI 原生架构，可以最终实现在网络边缘分布多个机器学习工作负载，驱动次级载波预测、定位、信道预测等下游任务。该研究探讨了多任务学习在构建通用 AI 原生无线接入网络方面的有效性，重点研究了次级载波预测、用户位置预测、室内链路分类和视线链路分类四项任务，在真实模拟中验证了性能，并得出了关于模型架构、损失和梯度平衡策略、分布式学习拓扑结构、数据稀疏性和任务分组等多方面设计要点的定量分析和见解，结果显示在四项考虑的任务中，采用基于定制门控的专家架构和基于不确定性的权重调节的多任务学习方法在性能上要么超过单任务学习，要么与其持平；在多任务学习设置下，视线链路分类任务有助于其他任务，但自身性能下降；对于稀疏训练数据，训练单一全局的多任务学习模型有帮助，但性能与单任务学习持平；每个任务存在一组最佳的任务组配对；多任务学习设置下的部分联邦要好于全模型联邦。