On Dual Connectivity in 6G Leo Constellation
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🤖 AI Summary
This study addresses packet reordering, spurious congestion control triggering, and real-time traffic loss in dual-connectivity scenarios within 6G low Earth orbit (LEO) satellite constellations, caused by heterogeneous path propagation delays. The authors develop a theoretical analysis framework based on a discrete Markov channel loss model to systematically evaluate the average end-to-end packet loss rate of dual-connectivity scheduling mechanisms, including packet duplication, packet switching, and network coding. For the first time, this work provides a computable packet loss performance metric for dual connectivity in 6G multi-orbit heterogeneous networks, derives the optimal scheduling policy, and establishes a theoretical foundation for designing efficient, low-overhead dual-connectivity schemes. The framework also enables comparative validation against machine learning–based empirical models.
Application Category
📝 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-toend 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.
Problem
Research questions and friction points this paper is trying to address.
Dual Connectivity
LEO satellite constellations
packet reordering
end-to-end packet loss
network coding
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual Connectivity
LEO Satellite Constellations
Markov Chain Modeling
Packet Loss Analysis
Network Coding
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