Optimization-Enhanced CSI-Aware Mobility Management for 5G and 6G Networks: A Structured Review

DOI

10.2991/978-94-6239-791-0_38How to use a DOI?

Keywords

Mobility management; Handover optimization; Channel state information (CSI); Quasi-Newton methods; L-BFGS; 5G/6G networks; Federated learning; Software-defined networking

Abstract

The mobility of current wireless networks (e.g. 5G and next-gen 6G) is far more complex than ever before with the combination of ultra-dense deployments, heterogeneous radio access technologies and strict quality of service (QoS) requirements. Many machines learning (ML) methods for predicting handover and channel state information (CSI) based decisions have been proposed. However, the optimization method of these learning methods is rarely evaluated with respect to the underlying problem structure. It is well known that most previous works rely on standard gradient approaches, and their analysis is not quite optimization-aware. Here we present a systematic optimization-aware work of CSI-based mobility management. It is shown that CSI-driven mean squared error (MSE) objectives are normally smooth, twice differentiable and locally quadratic. The latter makes them very well suited to curvature-aware methods. We compare first-order, Newton-type, and limited memory quasi-Newton optimization with respect to convergence speed, computational complexity, and numerical stability. We identify a long-standing gap between learning-oriented mobility intelligence and programmable SDN control and advanced optimization techniques. We propose L-BFGS-type methods as a principled approach for CSI-based handover learning and outline future research directions in scalable, secure, and efficient 6G mobility management.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pratyush Prakash Urade
AU  - Ashok Kanthe
PY  - 2026
DA  - 2026/10/09
TI  - Optimization-Enhanced CSI-Aware Mobility Management for 5G and 6G Networks: A Structured Review
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 506
EP  - 523
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_38
DO  - 10.2991/978-94-6239-791-0_38
ID  - Urade2026
ER  -