Open AccessEnergy efficient ultra‐dense networks (UDNs) based on joint optimisation evolutionary algorithm
Author(s) -
Salem Ahmed Abdelaziz,
ElRabaie Sayed,
Shokair Mona
Publication year - 2019
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2018.5613
Subject(s) - computer science , mathematical optimization , base station , bandwidth (computing) , energy consumption , monte carlo method , efficient energy use , estimator , convergence (economics) , wireless network , quality of service , wireless , algorithm , telecommunications , mathematics , ecology , statistics , economic growth , electrical engineering , economics , biology , engineering
In future cellular 5G, the network operators have recognised the spectrum scarcity as one of the major problems. Therefore, expanding or even re‐utilising the current bandwidth requires an advanced technique to accommodate the enormous communicating devices. In this paper, energy efficiency (EE) maximization is jointly carried out with respect to the following design parameters: base station density, number of users, number of attached antennas, and power control coefficient based on analytical channel estimates and feasible pilot reuse. Although dense network performance is restricted to the accumulation of both user and inter‐cell interferences, the reasons of mutual selection among the optimal design parameters will be clearly discussed to discover their effects on mitigating interference. Simulation results validate that the diversity of the design parameters achieves the maximum EE given a predefined quality of service constraints. Also, the study of hardware distortion will expose the relevant influence on the optimised EE. Moreover, the results will be extended to study the energy consumption in terms of optimised design variables. Finally, our results will be confirmed through a fair comparison with the alternating optimisation based on Monte Carlo simulation, wherein, our proposal shows a reality and fast convergence.