Curated Collaborative AI Edge with Network Data Analytics for B5G/6G Radio Access Networks
Journal:
arXiv
Published Date:
Jun 30, 2025
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.
