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Energy Efficiency and Load Optimization in Heterogeneous Networks through Dynamic Sleep Strategies: A Constraint-Based Optimization Approach

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  • Amna Shabbir

    (Department of Electronic Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan)

  • Muhammad Faizan Shirazi

    (Department of Electronic Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan
    Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA)

  • Safdar Rizvi

    (Department of Computer Science, Bahria University, Karachi Campus, Karachi 75000, Pakistan)

  • Sadique Ahmad

    (EIAS Data Science and Block Chain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Abdelhamied A. Ateya

    (EIAS Data Science and Block Chain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt)

Abstract

This research endeavors to advance energy efficiency (EE) within heterogeneous networks (HetNets) through a comprehensive approach. Initially, we establish a foundational framework by implementing a two-tier network architecture based on Poisson process distribution from stochastic geometry. Through this deployment, we develop a tailored EE model, meticulously analyzing the implications of random base station and user distributions on energy efficiency. We formulate joint base station and user densities that are optimized for EE while adhering to stringent quality-of-service (QoS) requirements. Subsequently, we introduce a novel dynamically distributed opportunistic sleep strategy (D-DOSS) to optimize EE. This strategy strategically clusters base stations throughout the network and dynamically adjusts their sleep patterns based on real-time traffic load thresholds. Employing Monte Carlo simulations with MATLAB, we rigorously evaluate the efficacy of the D-DOSS approach, quantifying improvements in critical QoS parameters, such as coverage probability, energy utilization efficiency (EUE), success probability, and data throughput. In conclusion, our research represents a significant step toward optimizing EE in HetNets, simultaneously addressing network architecture optimization and proposing an innovative sleep management strategy, offering practical solutions to maximize energy efficiency in future wireless networks.

Suggested Citation

  • Amna Shabbir & Muhammad Faizan Shirazi & Safdar Rizvi & Sadique Ahmad & Abdelhamied A. Ateya, 2024. "Energy Efficiency and Load Optimization in Heterogeneous Networks through Dynamic Sleep Strategies: A Constraint-Based Optimization Approach," Future Internet, MDPI, vol. 16(8), pages 1-19, July.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:8:p:262-:d:1442407
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    References listed on IDEAS

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    1. Mohammed H. Alsharif & Rosdiadee Nordin, 2017. "Evolution towards fifth generation (5G) wireless networks: Current trends and challenges in the deployment of millimetre wave, massive MIMO, and small cells," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(4), pages 617-637, April.
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