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

arXiv:2404.15197v1 Announce Type: cross
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 …

abstract ai-native architecture arxiv beyond cs.lg cs.ni data data-driven distributed driving edge etc eventually general independent life machine machine learning management multiple multi-task learning network networks prediction ran tasks type workloads