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

Maryam Salamatmoghadasi, Metin Ozturk, Halim Yanikomeroglu

TL;DR

This work tackles the practical barrier of estimating traffic loads for sleeping SBSs to enable effective cell switching in HAPS-assisted vHetNets. It assesses three spatial interpolation strategies—distance-based, random selection, and clustering-based—with a focus on multi-level clustering (MLC) and validates them against the Milan CDR dataset, showing that MLC can reduce estimation error and approach near-optimal power performance. The study provides a mathematical framework linking load estimation accuracy to network power consumption, including error scenarios for over- and under-estimation and how these affect switching decisions. The findings suggest that accurate load estimation is crucial for realizing energy efficiency gains in sustainable 6G networks and demonstrates practical pathways to achieve this via data-driven spatial interpolation techniques.

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.

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

TL;DR

This work tackles the practical barrier of estimating traffic loads for sleeping SBSs to enable effective cell switching in HAPS-assisted vHetNets. It assesses three spatial interpolation strategies—distance-based, random selection, and clustering-based—with a focus on multi-level clustering (MLC) and validates them against the Milan CDR dataset, showing that MLC can reduce estimation error and approach near-optimal power performance. The study provides a mathematical framework linking load estimation accuracy to network power consumption, including error scenarios for over- and under-estimation and how these affect switching decisions. The findings suggest that accurate load estimation is crucial for realizing energy efficiency gains in sustainable 6G networks and demonstrates practical pathways to achieve this via data-driven spatial interpolation techniques.

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.
Paper Structure (21 sections, 5 theorems, 13 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 21 sections, 5 theorems, 13 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Model
  4. Network Power Consumption
  5. Data Set and Data Processing
  6. Problem Formulation
  7. Optimization of the Total Power Consumption
  8. Power Consumption with the Erroneous Load Estimations
  9. Spatial Interpolation for Traffic Load Estimation
  10. Geographical Distance-Based Traffic Load Estimation
  11. Distance-Based without Weighting
  12. Distance-Based with Weighting
  13. Random Cell Selection Traffic Load Estimation
  14. Random Selection without Weighting
  15. Random Selection with Weighting
  16. ...and 6 more sections

Key Result

Theorem 1

Errors in estimating the traffic load of SBSs in a vHetNet can lead to changes in the optimal state vector, thereby affecting the total power consumption of the network.

Figures (5)

  • Figure 1: A vHetNet model with an MBS, multiple SBSs, and a HAPS-SMBS.
  • Figure 2: Estimation error for distance-based cell selection with weighting.
  • Figure 3: The estimation error for different methods. Two $x$- and two $y$-axes are considered: the blue one is for the clustering-based while the black one is for the rest of the approaches.
  • Figure 4: Network power consumption for different number of layers in MLC.
  • Figure 5: Decision change for different number of layers in MLC.

Theorems & Definitions (10)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Proposition 1
  • proof
  • Corollary 2.1
  • proof
  • Lemma 1
  • proof