Jeta Dobruna, Zana L. Fazliu, Hena Maloku, Mojca Volk
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引用次数: 0
Abstract
The ultra-dense deployment of pico cells in 5G heterogeneous networks (HetNets) has raised serious concerns regarding interference and energy consumption. Both industry and academia are focusing on enhancing network energy efficiency (EE) while maintaining satisfactory quality of service (QoS) levels. However, finding an optimal solution to NEE is very challenging, especially in ultra-dense HetNets. Here, a user association and power management algorithm is presented that follows a heuristic approach and aims to maximize EE while satisfying other network requirements. The proposed algorithm associates users based on criteria that consider the users’ EE and minimizes energy consumption by intermittently switching into sleep mode base stations with the highest impact on overall network EE. The performance of this solution is evaluated in a realistic multi-cell two-tier scenario considering both co-tier and cross-tier interference by comparing it with two other solutions: a heuristic approach based on standardized eICIC, and an optimization approach based on Lagrangian dual decomposition. The simulation results show that our solution outperforms benchmarking solutions in terms of EE, average user rate, and network throughput while minimizing energy consumption.
期刊介绍:
IET Communications covers the fundamental and generic research for a better understanding of communication technologies to harness the signals for better performing communication systems using various wired and/or wireless media. This Journal is particularly interested in research papers reporting novel solutions to the dominating problems of noise, interference, timing and errors for reduction systems deficiencies such as wasting scarce resources such as spectra, energy and bandwidth.
Topics include, but are not limited to:
Coding and Communication Theory;
Modulation and Signal Design;
Wired, Wireless and Optical Communication;
Communication System
Special Issues. Current Call for Papers:
Cognitive and AI-enabled Wireless and Mobile - https://digital-library.theiet.org/files/IET_COM_CFP_CAWM.pdf
UAV-Enabled Mobile Edge Computing - https://digital-library.theiet.org/files/IET_COM_CFP_UAV.pdf