Curated Collaborative AI Edge with Network Data Analytics for B5G/6G Radio Access Networks

TL;DR

This paper introduces a novel collaborative learning framework and resource pooling scheme to significantly reduce energy consumption and operational costs in 5G RANs by leveraging accurate network predictions and dynamic resource management.

Contribution

It proposes the Curated Collaborative Learning framework for superior traffic prediction and the Distributed Unit Pooling Scheme for energy-efficient resource allocation in 5G networks.

Findings

CCL outperforms state-of-the-art prediction methods by up to 43.9%.

DUPS reduces energy consumption by up to 89%.

Integrated approach enhances 5G RAN efficiency and cost savings.

Abstract

Despite advancements, Radio Access Networks (RAN) still account for over 50\% of the total power consumption in 5G networks. Existing RAN split options do not fully harness data potential, presenting an opportunity to reduce operational expenditures. This paper addresses this opportunity through a twofold approach. First, highly accurate network traffic and user predictions are achieved using the proposed Curated Collaborative Learning (CCL) framework, which selectively collaborates with relevant correlated data for traffic forecasting. CCL optimally determines whom, when, and what to collaborate with, significantly outperforming state-of-the-art approaches, including global, federated, personalized federated, and cyclic institutional incremental learnings by 43.9%, 39.1%, 40.8%, and 31.35%, respectively. Second, the Distributed Unit Pooling Scheme (DUPS) is proposed, leveraging deep reinforcement learning and prediction inferences from CCL to reduce the number of active DU servers efficiently. DUPS dynamically redirects traffic from underutilized DU servers to optimize resource use, improving energy efficiency by up to 89% over conventional strategies, translating into substantial monetary benefits for operators. By integrating CCL-driven predictions with DUPS, this paper demonstrates a transformative approach for minimizing energy consumption and operational costs in 5G RANs, significantly enhancing efficiency and cost-effectiveness.