Addressing the Load Estimation Problem: Cell Switching in HAPS-Assisted Sustainable 6G Networks

TL;DR

This paper investigates traffic load estimation challenges in cell switching for vHetNets with HAPS integration, proposing spatial interpolation schemes and demonstrating the effectiveness of multi-level clustering in improving energy efficiency.

Contribution

It introduces a novel focus on load estimation errors in cell switching within HAPS-assisted vHetNets and evaluates three spatial interpolation schemes using real data.

Findings

Multi-level clustering achieves 0.8% accuracy in power consumption estimation.

Spatial interpolation schemes effectively estimate traffic loads.

MLC significantly enhances energy efficiency in vHetNets.

Abstract

This study aims to introduce and address the problem of traffic load estimation in the cell switching concept within the evolving landscape of vertical heterogeneous networks (vHetNets). The problem is that the practice of cell switching faces a significant challenge due to the lack of accurate data on the traffic load of sleeping small base stations (SBSs). This problem makes the majority of the studies in the literature, particularly those employing load-dependent approaches, impractical due to their basic assumption of perfect knowledge of the traffic loads of sleeping SBSs for the next time slot. Rather than developing another advanced cell switching algorithm, this study investigates the impacts of estimation errors and explores possible solutions through established methodologies in a novel vHetNet environment that includes the integration of a high altitude platform (HAPS) as a super macro base station (SMBS) into the terrestrial network. In other words, this study adopts a more foundational perspective, focusing on eliminating a significant obstacle for the application of advanced cell switching algorithms. To this end, we explore the potential of three distinct spatial interpolation-based estimation schemes: random neighboring selection, distance-based selection, and clustering-based selection. Utilizing a real dataset for empirical validations, we evaluate the efficacy of our proposed traffic load estimation schemes. Our results demonstrate that the multi-level clustering (MLC) algorithm performs exceptionally well, with an insignificant difference (i.e., 0.8%) observed between its estimated and actual network power consumption, highlighting its potential to significantly improve energy efficiency in vHetNets.