Multi-Task Learning as enabler for General-Purpose AI-native RAN

TL;DR

This paper investigates multi-task learning (MTL) for AI-native RAN in 6G networks, demonstrating how MTL can efficiently handle multiple tasks like prediction and classification at the network edge, with insights from realistic simulations.

Contribution

It explores the effectiveness of MTL in a general-purpose AI-native RAN, analyzing various design aspects and proposing strategies for optimal task grouping and federated learning.

Findings

Customized gate control-based expert architecture improves MTL performance.

LoS classification aids other tasks but degrades its own performance.

Partial federation outperforms full model federation in MTL.

Abstract

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.