On Dual Connectivity in 6G Leo Constellations
Achilles Machumilane, Alberto Gotta
TL;DR
This work studies end-to-end packet loss in dual connectivity over 6G non-terrestrial networks with heterogeneous path delays, particularly LEO-based backhauls. It develops a mathematical framework that models the loss process as a two-state discrete-time Markov chain, with per-path loss $p_{loss_i}$ and stationary probabilities $\pi_G$ and $\pi_B$, and analyzes three techniques—Packet Duplication, Packet Splitting, and Random Linear Network Coding (RLNC)—to mitigate losses. The authors derive analytical expressions for $P^{(PD)}_{loss}$, $P^{(PS)}_{loss}(l|N)$, and $P^{(NC)}_{loss}(l|N)$, along with $P^{(NC)}_{rec}(N,K,q)$ and $P^{(NC)}_{dec}(N,K,q)$, enabling comparison to machine-learning-based models. Applying the framework to ITU-referenced earth-space channels for LEO satellites, the results show that network coding can substantially reduce end-to-end packet loss under challenging delay conditions, though at the expense of encoding/decoding complexity and latency, underscoring its role as a design tool for policy optimization in DC-enabled NTN architectures.
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.