E2CA‐SM: an energy‐efficient channel allocation with sleep mode for base station in fifth‐generation‐based cellular network systems. Issue 2 (1st March 2020)

• Record Type: Journal Article
• Authors: Venkata Krishna, Parimala; Saritha, Vankadara; Obaidat, Mohammad Salameh
• Is Part Of: IET networks. Volume 9:Issue 2(2020)
• Page Start: 74
• Page End: 82
• Publication Date: 2020-03-01
• Subjects: cellular radio -- channel allocation -- Markov processes -- 5G mobile communication -- power consumption -- telecommunication power management; energy consumption -- fifth‐generation network characteristics -- fifth‐generation‐based cellular network -- bandwidth utilisation -- energy‐efficient channel allocation -- base station -- sensor networks -- E2CA‐SM algorithm -- learning automata‐based reservation -- single‐dimension Markov model; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2019.0045
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : In today's world, power is one of the resources that needs effective utilisation similar to the bandwidth utilisation. As the battery life of the nodes in the cellular networks is not a problem, there is not much extensive research toward energy consumption in the cellular networks. Nevertheless, currently the focus is toward conserving the power being utilised by various components in the cellular network. Hence, an energy‐efficient channel allocation (E2CA) procedure for cellular networks with fifth‐generation network characteristics is proposed in this study, where the energy consumption is reduced with the concept of sleep mode for base station (E2CA‐SM). The SM is more popular in sensor networks when compared with cellular networks. The proposed E2CA‐SM algorithm shows low‐energy consumption during peak and non‐peak hours of cellular traffic. It is shown that the energy consumption per packet is 35.5% less when compared with multi‐traffic, with queue, learning automata‐based reservation, 32% when compared with E2CA. The system is modelled using single‐dimension Markov's model. There will be a trade‐off between blocking, dropping probability, and energy consumption, which is tackled in this study.