Work

Reducing Mobility-Related Signaling With Network Sum Throughput Maximization in 5G

Year: 2025

Type: article

Abstract: Signal quality fluctuates significantly due to blockages of Line of Sight, shadowing, and user mobility. This renders mobility management in 5G quite challenging. To improve it, 3GPP introduced Conditional Handover (CHO), which reduces handover failures by preparing target Base Stations (BSs) in advance. CHO adapts to the varying channel conditions and constantly prepares/releases cells, which leads to an increased exchange of control messages between the user and BSs. Connecting to the BS with the strongest signal is not always beneficial because the available resources and other users’ channels should be considered for a successful network operation. Hence, the need to carefully decide when to hand over, and when that happens, to select the best target BS. In this paper, we first formulate an optimization problem that minimizes network signaling by reducing the number of unprepared handovers and wasted cell preparations while providing a minimum rate to everyone. As the problem is NP-hard, we relax it and obtain a lower bound. Then, we propose a Cost-Efficient CHO (CECHO) algorithm with performance guarantees. Using 5G datasets, we compare CECHO with two baselines and show that it outperforms them by at least 45% while being near-optimal. However, reducing the signaling decreases the total throughput, which is an important metric for the network operator. Thus, we expand our initial problem into a Multi-Objective (MO) optimization, where we additionally maximize the network sum throughput. Results show that CECHO-MO increases the sum throughput more than$3\times $with only a 4% increase in signaling.

Source: IEEE Transactions on Network and Service Management

DOI: 10.1109/tnsm.2025.3599203

Authors: Anna Prado, Fidan Mehmeti, Wolfgang Kellerer

Institution: Technical University of Munich

Language: English

Topic: IPv6, Mobility, Handover, Networks, Security

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,645