PHandover: Parallel Handover in Mobile Satellite Network

2025-07-10

AI Summary

To address frequent, high-latency handovers caused by the high-speed motion of low Earth orbit (LEO) satellites, this paper proposes a pre-planned parallel handover mechanism. The method integrates Satellite Synchronization Function (SSF) with a machine learning–driven signal strength prediction model to enable autonomous, in-access-network parallel link establishment and release—eliminating reliance on core network–triggered measurement events while maintaining full compatibility with 5G Core Network standards. Its key contribution lies in shifting handover from reactive to predictive operation and ensuring efficient resource coordination via a novel scheduling algorithm. Experimental results demonstrate that the proposed scheme reduces average handover latency by 21× compared to conventional approaches, significantly enhancing connection stability and user Quality of Experience (QoE).

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

The construction of Low Earth Orbit (LEO) satellite constellations has recently attracted tremendous attention from both academia and industry. The 5G and 6G standards have identified LEO satellite networks as a key component of future mobile networks. However, due to the high-speed movement of satellites, ground terminals often experience frequent and high-latency handovers, which significantly deteriorate the performance of latency-sensitive applications. To address this challenge, we propose a parallel handover mechanism for mobile satellite networks that can considerably reduce handover latency. The main idea is to employ plan-based handovers instead of measurement-based handovers to avoid interactions between the access and core networks, thereby eliminating the significant time overhead associated with traditional handover procedures. Specifically, we introduce a novel network function named the Satellite Synchronized Function (SSF), which is designed to be fully compliant with the standard 5G core network. In addition, we propose a machine learning model for signal strength prediction, coupled with an efficient handover scheduling algorithm. We have conducted extensive experiments, and the results demonstrate that our proposed handover scheme can reduce handover latency by 21 imes compared to the standard NTN handover scheme and two other existing handover approaches, along with significant improvements in network stability and user-level performance.

Problem

• Reduce frequent high-latency handovers in LEO satellite networks
• Replace measurement-based handovers with plan-based handovers
• Improve network stability and user performance with parallel handover

Innovation

• Plan-based handovers replace measurement-based handovers
• Satellite Synchronized Function (SSF) for 5G compliance
• Machine learning predicts signal strength efficiently