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Two-Phase Cell Switching in 6G vHetNets: Sleeping-Cell Load Estimation and Renewable-Aware Switching Toward NES

Maryam Salamatmoghadasi, Metin Ozturk, Halim Yanikomeroglu

Abstract

This paper proposes a two phase framework to improve the sustainability in vertical heterogeneous networks that integrate various types of base stations~(BSs), including terrestrial macro BSs~(MBSs), small BSs~(SBSs), and a high altitude platform station super MBS (HAPS SMBS). In Phase I, we address the critical and often overlooked challenge of estimating the traffic load of sleeping SBSs, a prerequisite for practical cell switching, by introducing three methods with varying data dependencies: (i) a distance based estimator (no historical data), (ii) a multi level clustering (MLC) estimator (limited historical data), and (iii) a long short term memory~(LSTM) based temporal predictor (full historical data). In Phase II, we incorporate the most accurate estimation results from Phase I into a renewable energy aware cell switching strategy, explicitly modeling solar powered SBSs in three operational scenarios that reflect realistic hybrid grid renewable deployments. This flexible design allows the framework to adapt switching strategies based on renewable availability and storage conditions, making it more practical and robust for real world networks. Using a real call detail record dataset from Milan, simulation results show that the LSTM method achieves a mean absolute percentage error (MAPE) below 1% in Phase I, while in Phase II, the threshold based solar integration scenario achieves up to 23% network energy saving (NES) relative to conventional cell switching. Overall, the proposed framework bridges the gap between theoretical cell switching models and practical, sustainable 6G radio access network~(RAN) operation, enabling significant energy saving without compromising quality of service.

Two-Phase Cell Switching in 6G vHetNets: Sleeping-Cell Load Estimation and Renewable-Aware Switching Toward NES

Abstract

This paper proposes a two phase framework to improve the sustainability in vertical heterogeneous networks that integrate various types of base stations~(BSs), including terrestrial macro BSs~(MBSs), small BSs~(SBSs), and a high altitude platform station super MBS (HAPS SMBS). In Phase I, we address the critical and often overlooked challenge of estimating the traffic load of sleeping SBSs, a prerequisite for practical cell switching, by introducing three methods with varying data dependencies: (i) a distance based estimator (no historical data), (ii) a multi level clustering (MLC) estimator (limited historical data), and (iii) a long short term memory~(LSTM) based temporal predictor (full historical data). In Phase II, we incorporate the most accurate estimation results from Phase I into a renewable energy aware cell switching strategy, explicitly modeling solar powered SBSs in three operational scenarios that reflect realistic hybrid grid renewable deployments. This flexible design allows the framework to adapt switching strategies based on renewable availability and storage conditions, making it more practical and robust for real world networks. Using a real call detail record dataset from Milan, simulation results show that the LSTM method achieves a mean absolute percentage error (MAPE) below 1% in Phase I, while in Phase II, the threshold based solar integration scenario achieves up to 23% network energy saving (NES) relative to conventional cell switching. Overall, the proposed framework bridges the gap between theoretical cell switching models and practical, sustainable 6G radio access network~(RAN) operation, enabling significant energy saving without compromising quality of service.
Paper Structure (27 sections, 1 theorem, 17 equations, 10 figures, 2 tables, 2 algorithms)

This paper contains 27 sections, 1 theorem, 17 equations, 10 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contributions
  4. System Model
  5. Network Model
  6. Motivation for a HAPS Tier
  7. Network Power Consumption
  8. Renewable Energy Model
  9. Two-Phase Framework for Power Consumption Optimization
  10. Two-Phase Framework Overview
  11. Cell Switching Problem Formulation under Imperfect Load Estimation
  12. Power Consumption with the Erroneous Load Estimations
  13. Phase I: Multi-Dimensional Traffic Load Estimation-Spatial and Temporal Perspectives
  14. Geographical Distance-Based Traffic Load Estimation
  15. Clustering-Based Traffic Load Estimation
  16. ...and 12 more sections

Key Result

Theorem 3.1

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 (10)

  • Figure 1: Capacity via $C=mB\log_2(1+\gamma)$ versus SINR degradation relative to a terrestrial baseline (dashed); $B=20$ MHz, baseline $\gamma_0=20$ dB. Curves for $m\in\{1,1.5,2,3,4\}$ (interpreted as bandwidth scaling). Larger $m$ offsets SINR loss, providing additional capacity for HAPS offloading.
  • Figure 2: The proposed two-phase optimization framework for a vHetNet. Phase I estimates the traffic load of sleeping SBSs using spatial and temporal data. Phase II uses these estimates along with solar availability to optimize SBS ON/OFF switching, minimizing network power consumption. The system model shows the vHetNet topology with an MBS, multiple SBSs (some solar-powered), and a HAPS enabling vertical offloading.
  • Figure 3: Taxonomy of Phase I traffic load estimation methods, categorized by historical-data dependency (no/light/heavy) and the associated input, storage, and computational requirements. Any of the three can be used depending on data availability and deployment constraints.
  • Figure 4: Estimation error comparison of spatial methods. Two $x$- and two $y$-axes are used: the blue axis corresponds to the clustering-based method, and the black axis corresponds to the distance-based method.
  • Figure 5: MAPE of LSTM-based traffic load estimation for different window sizes and LSTM units.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Theorem 3.1
  • proof