Adaptive FH optimization in MEC-assisted 5G environments
- PDF / 2,893,635 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 93 Downloads / 195 Views
ORIGINAL PAPER
Adaptive FH optimization in MEC‑assisted 5G environments Viktoria‑Maria Alevizaki1 · Markos Anastasopoulos2 · Anna Tzanakaki1,2 · Dimitra Simeonidou2 Received: 18 December 2019 / Accepted: 14 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract To address the limitations of current radio access networks (RANs), centralized RANs adopting the concept of flexible splits of the BBU functions between radio units (RUs) and the central unit have been proposed. This concept can be implemented combining both the Mobile Edge Computing model and relatively large-scale centralized Data Centers. This architecture requires high-bandwidth/low-latency optical transport networks interconnecting RUs and compute resources adopting SDN control. This paper proposes a novel mathematical model based on Evolutionary Game Theory that allows to dynamically identify the optimal split option with the objective to unilaterally minimize the infrastructure operational costs in terms of power consumption. Optimal placement of the SDN controllers is determined by a heuristic algorithm in such a way that guarantees the stability of the whole system. Finally, multi-agent learning methods were investigated in order to expand the model to more sophisticated scenarios where many RUs with limited information are interacting. Keywords Cloud · C-RAN · Evolutionary Game Theory · Functional splits · MEC · Multi-agent reinforcement learning · Replicator equation · SDN
1 Introduction The digital evolution observed in the modern world renders existing technologies inefficient, since an increased number of devices need to connect to the network, thus creating huge demands in terms of capacity and latency [1]. In view of this, 5G network technologies are aiming at a new open and flexible network paradigm that will satisfy the needs of various vertical operators (transport, media, automotive, manufacturing, healthcare, etc.) in a cost- and energy-efficient manner [2, 3]. The huge increase in data traffic that is foreseen in the next years introduces the need for higher network densification, a concept that is being implemented employing a large number of cells with limited coverage, also known as small cells. This way, network’s capacity can be increased, while the end-to-end delay decreases [4]. However, this solution comes at the expense of increased Capital and Operational * Viktoria‑Maria Alevizaki [email protected] 1
Department of Physics, National and Kapodistrian University of Athens, Athens, Greece
Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
2
Expenditures (CAPEX and OPEX) as a large number of new Base Stations (BSs) need to be adopted and operated contributing also to the increase in the CO2 footprint of the infrastructure. One way to overcome the aforementioned limitations is by decoupling the computational resources (the baseband units—BBUs) from the BSs (the remote units—RUs) and placing them in a co-location facility (the central
Data Loading...
