A novel power consumption optimization framework in 5G heterogeneous networks

The fifth-generation (5G) mobile networks have the capacity to handle the dynamic traffic demands of the user equipment (UE). One approach is the dense deployment of small base stations (s-BSs), which can control the dynamic traffic demands. We consider s-BSs which are interconnected through the mmW...

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Veröffentlicht in:Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2023-01, Vol.220, p.109487, Article 109487
Hauptverfasser: Venkateswararao, Kuna, Swain, Pravati, Jha, Shashi Shekhar, Ioannou, Iacovos, Pitsillides, Andreas
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Sprache:eng
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Zusammenfassung:The fifth-generation (5G) mobile networks have the capacity to handle the dynamic traffic demands of the user equipment (UE). One approach is the dense deployment of small base stations (s-BSs), which can control the dynamic traffic demands. We consider s-BSs which are interconnected through the mmWave backhaul (BH) link to transfer traffic from the s-BS to the core network through the macro base station (MBS). In this setting, the network power consumption is affected by the way UEs are connected to the base stations and the traffic routes through the BH links. The main objective of this paper is to minimize the power consumption of the heterogeneous network (HetNet) with the intelligent backhauling and s-BS/BH link sleeping (IBSBS) framework. The proposed framework provides the minimized power consumption in HetNets through UE association, backhauling, and sleep s-BS/BH links. The UE association and backhauling uses a heuristic function based intelligent backhauling algorithm to assess the minimum power consumption from the s-BS to the MBS while considering the power and capacity constraints of the network. The load sharing based s-BS sleeping algorithm dynamically changes the states (active/sleep) of the s-BSs according to their loads without compromising UE demands. Different distributions of UEs and different data rates over the HetNets architecture are considered for performance evaluation. The evaluation results of the proposed framework outperform the state-of-the-art algorithms in terms of network energy efficiency, power consumption, and the number of active s-BSs/BH links.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2022.109487