From Static to Adaptive: Dynamic Cost Function Weight Adaptation in Hierarchical Reinforcement Learning for Sustainable 6G Radio Access Networks

The rapid growth of mobile network traffic and the densification required for 6G networks significantly increase energy consumption, with base stations (BS) accounting for up to 70% of total network energy use. Energy-efficient BS switching has therefore become a critical research focus. Traditional solutions rely on static thresholds or fixed cost function weights, limiting adaptability in dynamic environments. This thesis investigates how cost function design and weight adaptation influence the trade-off between energy consumption and Quality of Service (QoS) degradation in Deep Reinforcement Learning (DRL)-based BS switching. Using a realistic spatio-temporal dataset, we show that static cost weights lead to suboptimal performance under varying traffic conditions. To address this, we propose a Hierarchical Reinforcement Learning (HRL) architecture in which a high-level controller dynamically selects low-level policies trained with different cost function weights. Experimental results demonstrate that the proposed HRL approach achieves up to 64% energy reduction—improving by 5% over the static DRL baseline—while maintaining acceptable QoS levels. These findings highlight the potential of hierarchical control and adaptive weighting in achieving scalable, sustainable 6G Radio Access Networks operations.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Viana Fonseca Abreu, Jefferson

Chair dc:contributor.committeechair

• Kibilda, Jacek

Committee members dc:contributor.committeemember

• Midkiff, Scott F.
• Pereira da Silva, Luiz Antonio

• Energy efficiency
• Base Station Switching
• Hierarchical Reinforcement Learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:45243

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/140536