PHandover: Parallel Handover in Mobile Satellite Network

TL;DR

This paper introduces PHandover, a parallel handover mechanism for LEO satellite networks that significantly reduces latency by using plan-based handovers, a novel network function, and machine learning for signal prediction.

Contribution

It proposes a novel parallel handover scheme with a new network function and machine learning-based signal prediction to improve satellite network performance.

Findings

Reduces handover latency by 21 times compared to standard schemes.

Improves network stability and user experience.

Demonstrates effectiveness through extensive experiments.

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\times compared to the standard NTN handover scheme and two other existing handover approaches, along with significant improvements in network stability and user-level performance.