Enhancing Sustainability in HAPS-Assisted 6G Networks: Load Estimation Aware Cell Switching
Maryam Salamatmoghadasi, Metin Ozturk, Halim Yanikomeroglu

TL;DR
This paper tackles the challenge of traffic load estimation in HAPS-assisted 6G networks, proposing spatial and temporal prediction methods to improve energy-efficient cell switching in heterogeneous networks.
Contribution
It introduces novel spatial and temporal load estimation techniques, including multi-level clustering and LSTM, to enhance accuracy in sleep mode base stations.
Findings
MLC and LSTM methods outperform other approaches in load estimation accuracy.
Spatial and temporal estimation significantly improve energy-saving strategies.
Real-world data validates the effectiveness of proposed methods.
Abstract
This study introduces and addresses the critical challenge of traffic load estimation in cell switching within vertical heterogeneous networks. The effectiveness of cell switching is significantly limited by the lack of accurate traffic load data for small base stations (SBSs) in sleep mode, making many load-dependent energy-saving approaches impractical, as they assume perfect knowledge of traffic loads, an assumption that is unrealistic when SBSs are inactive. In other words, when SBSs are in sleep mode, their traffic loads cannot be directly known and can only be estimated, inevitably with corresponding errors. Rather than proposing a new switching algorithm, we focus on eliminating this foundational barrier by exploring effective prediction techniques. A novel vertical heterogeneous network model is considered, integrating a high-altitude platform station (HAPS) as a super macro…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · IoT and Edge/Fog Computing · IoT Networks and Protocols
