A takeover time-driven adaptive evolutionary algorithm for mobile user tracking in pre-5G cellular networks

• Published in: Applied soft computing Vol. 116; p. 107992
• Main Authors: Dahi, Zakaria Abdelmoiz, Alba, Enrique, Luque, Gabriel
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2022
• Subjects: Adaptation; Cellular networks; Evolutionary algorithms; Cellular networks; Evolutionary algorithms; Adaptation
• ISSN: 1568-4946; 1872-9681; 1872-9681
• DOI: 10.1016/j.asoc.2021.107992

Cellular networks are one of today’s most popular means of communication. This fact has made the mobile phone industry subject to a huge scientific and economic competition, where the quality of service is key. Such a quality is measured on the basis of reliability, speed and accuracy when delivering a service to a user no matter his location or behaviour are. This fact has placed the users’ tracking process among the most difficult and determining issues in cellular network design. In this paper, we present an adaptive bi-phased evolutionary algorithm based on the takeover time to solve this problem. The proposal is thoroughly assessed by tackling twenty-five real-world instances of different sizes. Twenty-eight of the state-of-the-art techniques devised to address the users’ mobility problem have been taken as the comparison basis, and several statistical tests have been also conducted. Experiments have demonstrated that our solver outperforms most of the top-ranked algorithms. •Adaptive metaheuristics to solve the mobile user tracking problem in cellular networks.•Adaptation is dynamic and based on the future behaviour of the algorithm.•Uses a mathematical model to forecast the future behaviour of the algorithm.•Tests over 25 realistic networks and comparison against 28 top-ranked algorithms.•Experiments showed that the proposed approach is more efficient.