Self-optimized network: When machine learning meets optimization

Nacef, Abdelhakim; Bagaa, Miloud; Aklouf, Youcef; Kaci, Abdellah; Dutra, Diego Leonel Cadette; Ksentini, Adlen
GLOBECOM 2021, IEEE Global Communications Conference, 7-11 December 2021, Madrid, Spain

The fifth generation of the mobile network aims to revolutionize mobile communication by offering both unparalleled performance and broader service offerings. 5G technology responds to the growing demand for higher bandwidth and lower latency, caused by a significant increase of connected resources. Leveraging on Software-Defined Networking (SDN) and artificial intelligence (AI) technologies, the 6G system can autonomously adapt to user requirements. This paper proposes a framework, named intelligent optimization framework (IoF), that leverages both network optimization and machine learning techniques for achieving the best performance results. The IoF framework allows for finding an exemplary resource allocation configuration of the mobile network by leveraging SDN technology. This work aims to configure the SDN-enabled switches and Open vSwitchs (OVSs) to enable an optimized data plane that reduces the overall operational expense (OPEX) cost and capital expense (CAPEX) cost within an optimized execution time. The evaluation results show our proposed framework's efficiency for delivering optimal configurations by reducing the number of allocated OVSs in a reasonable execution time.

Systèmes de Communication
Eurecom Ref:
© 2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
See also: