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.

翻译：双连接（DC）技术在5G演进中获得了广泛关注，它通过利用两条路径的信道条件来提升吞吐量与可靠性。然而，当两条路径存在不同延迟时——例如在多轨道拓扑的地面与非地面综合网络中，或在具有频繁拓扑变化的网络（如具有不同仰角的低地球轨道（LEO）卫星星座）中——业务传输可能会遭遇数据包重排序或触发拥塞控制机制。此外，实时业务若其到达时间超过播放阈值，则可能发生丢包。为应对这些问题，业界已提出多种技术，例如在DC中进行业务调度时采用数据包复制、数据包交换和网络编码。然而，若设计不当，这些技术可能导致资源浪费、编码/解码延迟及计算开销，从而削弱DC的预期优势。本文提出了一个数学框架，用于计算在采用数据包复制与数据包交换结合或使用网络编码的DC场景下，当丢包过程由离散马尔可夫链（无线信道的典型模型）建模时的平均端到端丢包率。此类度量有助于在完全了解底层丢包过程的情况下推导最优策略，进而与通过机器学习算法学习的经验模型进行比较。