On Dual Connectivity in 6G Leo Constellations

TL;DR

This paper develops a mathematical framework to analyze packet loss in dual connectivity scenarios in 6G Leo constellations, addressing challenges like delay disparities and proposing optimal policies for traffic management.

Contribution

It introduces a novel Markov chain-based model to quantify end-to-end packet loss in dual connectivity, aiding in designing efficient traffic scheduling techniques.

Findings

Provides a mathematical method for calculating packet loss in dual connectivity

Enables comparison of optimal policies with machine learning models

Addresses packet reordering and congestion issues in satellite-terrestrial networks

Abstract

Dual connectivity (DC) has garnered significant attention in 5G evolution, allowing for enhancing throughput and reliability by leveraging the channel conditions of two paths. However, when the paths exhibit different delays, such as in terrestrial and non-terrestrial integrated networks with multi-orbit topologies or in networks characterized by frequent topology changes, like Low Earth Orbit (LEO) satellite constellations with different elevation angles, traffic delivery may experience packet reordering or triggering congestion control mechanisms. Additionally, real-time traffic may experience packet drops if their arrival exceeds a play-out threshold. Different techniques have been proposed to address these issues, such as packet duplication, packet switching, and network coding for traffic scheduling in DC. However, if not accurately designed, these techniques can lead to resource waste, encoding/decoding delays, and computational overhead, undermining DC's intended benefits. This paper provides a mathematical framework for calculating the average end-to-end packet loss in case of a loss process modeled with a Discrete Markov Chain - typical of a wireless channel - when combining packet duplication and packet switching or when network coding is employed in DC. Such metrics help derive optimal policies with full knowledge of the underlying loss process to be compared to empirical models learned through Machine Learning algorithms.