Stochastic Modeling of a Base Station in 5G Wireless Networks for Energy Aspects Using Advanced Sleep Mechanism

Anisha Aggarwal (), Priyanka Kalita () and Dharmaraja Selvamuthu ()
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Anisha Aggarwal: IIT Delhi
Priyanka Kalita: Bhattadev University
Dharmaraja Selvamuthu: IIT Delhi

Methodology and Computing in Applied Probability, 2025, vol. 27, issue 3, 1-31

Abstract: Abstract Energy saving in the base stations (BSs) is one of the important issues as huge network capacity, higher data speeds, more availability, and a more uniform user experience is promised by 5G cellular networks. Advanced sleep mechanism (ASM) is one of the efficient techniques for saving energy in the base station. This paper introduces three stochastic models for ASM based on system arrivals and user requests (URs): the Markov model, the semi-Markov model, and the Markov regenerative process model for the base station. Closed-form solutions for steady-state system size probabilities are derived for each model. Additionally, performance metrics such as power consumption, power saving factor, and throughput are evaluated. Finally, a sensitivity analysis is conducted to compare the results obtained from the three different proposed models.

Keywords: Markov model; Semi-Markov model; Markov generative process; Power consumption; Power saving factor; Throughput (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11009-025-10187-1

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