Open AccessEnergy efficient ultra‐dense networks based on multi‐objective optimisation framework
Author(s) -
Salem Ahmed Abdelaziz,
ElRabaie Sayed,
Shokair Mona
Publication year - 2018
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
ISSN - 2047-4962
DOI - 10.1049/iet-net.2017.0215
Subject(s) - computer science , efficient energy use , energy consumption , base station , spectral efficiency , key (lock) , quality of service , reuse , network performance , telecommunications link , power (physics) , monte carlo method , channel (broadcasting) , mathematical optimization , distributed computing , reliability engineering , computer network , engineering , mathematics , statistics , physics , computer security , quantum mechanics , electrical engineering , waste management
Energy efficiency (EE) is considered as one of the pivotal uplink (UL) performance metrics for 5G dense networks. It consists of two conflicting objectives that are recognised as benefit–cost ratio: spectral efficiency and energy consumption. Accordingly, network design tradeoff is the key challenge for future networks. In this paper, we aim to jointly maximise network spectral efficiency and minimise power consumption with respect to the following design parameters: base station (BS) density, users' number, equipped antennas' number, and signal‐to‐noise power ratio without losing service quality. The performance of the ultra‐dense network is characterised on the basis of the Pareto optimality concept through the following benchmarks: (i) studying impact of exhausted power on the deployed hardware elements. (ii) Validating the total EE performance through Monte‐Carlo simulation within a low cost of processing time. (iii) The proposed approach is compared against single objective scheme to show the significance of the design tradeoff. Furthermore, we will introduce a detailed mathematical analysis for UL power policy and channel estimation for reliable pilot reusing. Simulation results will show that our proposed solution guarantee remarkable EE performance via reducing the number of deployed BSs without scarificing service quality.