Application of a Bi-Objective EA for RAN Resources Optimization in a Dynamic Scenario

The high demand for energy in communication technologies is posing a major challenge in many future applications. Highly dynamic systems, such as mobile networks and renewable energy sources, are interconnected and subject to constant change, requiring situation-aware optimization. Alternative solut...

Full description

Saved in:
Bibliographic Details
Published in2024 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors Rothkotter, Markus, Kluge, Niklas, Mostaghim, Sanaz
Format Conference Proceeding
LanguageEnglish
Published IEEE 30.06.2024
Subjects
Dynamic scheduling
energy efficiency
evolutionary algorithm
Evolutionary computation
Heuristic algorithms
multi-objective
Performance gain
Power demand
Quality of service
Renewable energy sources
Online AccessGet full text

Cover

More Information
Summary:The high demand for energy in communication technologies is posing a major challenge in many future applications. Highly dynamic systems, such as mobile networks and renewable energy sources, are interconnected and subject to constant change, requiring situation-aware optimization. Alternative solutions necessary for decision-making based on temporary requirements can be obtained by multi-objective optimization. This paper considers a resource allocation problem related to the beamforming technology of the upcoming 6G standard and explores the feasibility of using an evolutionary algorithm (EA) in a dynamic network scenario to optimize power consumption and quality of service (QoS) simultaneously while assigning a user equipment (UE) to a base station (BS) beam. The proposed approach includes an information carry-over mechanism within the optimization process, enabling convergence despite the constantly changing network topology. The evaluation focuses on three factors that may limit applicability: network utilization, portion of moving users, and movement speed. It is conducted in a simulation environment in comparison to a heuristic approach that only takes QoS into account. The results indicate that, even in a fully dynamic network instance, the EA outperforms the heuristic approach in all experimental instances, although the performance gain decreases depending on certain combinations of influencing factors.
DOI:10.1109/CEC60901.2024.10611983