Open AccessExploiting heterogeneity for cost efficient 5G base station deployment using meta‐heuristics
Author(s) -
Aondoakaa David,
Comas John,
Swift Stephen
Publication year - 2020
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/iet-net.2019.0111
Subject(s) - computer science , heuristics , base station , key (lock) , network planning and design , cellular network , radio access network , heterogeneous network , exploit , heuristic , software deployment , distributed computing , network architecture , computer network , wireless network , wireless , telecommunications , mobile station , computer security , artificial intelligence , operating system
A key concern in the design of 5G is the radio access network, which is expected to be significantly denser and more advanced, with considerably higher infrastructure and power consumption cost than that of conventional mobile network standards. Novel algorithms/approaches for optimal planning of the radio access network are required for tackling the additional complexity of the problem of cost‐efficient radio access planning in 5G, which cannot be properly handled by conventional approaches. This study proposes a novel optimisation framework for the cost‐efficient deployment and configuration of 5G base stations. The main idea of the proposed optimisation framework is to exploit heterogeneity in three key 5G technologies, heterogeneous base station architecture, cell range extension and multiple‐input–multiple‐output spatial multiplexing, by jointly optimising their configurations during network design. In addition, the proposed optimisation framework includes generic steps for applying meta‐heuristic algorithms to the problem, which are necessary to overcome the problem's complexity, especially for large problem instances. The authors’ results show that their novel optimisation framework improves the cost efficiency of the network planning both in terms of power and infrastructural cost to operators.