QoS Identifier and Slice Mapping in 5G and Non-Terrestrial Network Interconnected Systems

TL;DR

The paper addresses the limited expressiveness of 5QI for heterogeneous NTN backhaul by introducing an NTN QoS Identifier ($NQI$) and NTN slices to group traffic by QoS and destination. It proposes a three-phase optimization framework—$5QI \rightarrow NQI$ mapping, NTN traffic-to-slice mapping, and slice-level flow/routing optimization—formulated as a MILP to minimize a joint flow and latency cost $J$. Through detailed simulations on a realistic 5G–NTN topology, it shows that preserving the original $PDB$ order while using a moderate number of NQIs improves latency satisfaction at the cost of higher computation time, revealing a trade-off between performance and efficiency. The work enables unified end-to-end QoS control for large numbers of 5G flows over NTN backhaul and informs mapping design for robust 5G–NTN integration.

Abstract

The interconnection of 5G and non-terrestrial networks (NTNs) has been actively studied to expand connectivity beyond conventional terrestrial infrastructure. In the 3GPP standardization of 5G systems, the 5G Quality of Service (QoS) Identifier (5QI) is defined to characterize the QoS requirements of different traffic requirements. However, it falls short in capturing the diverse latency, capacity, and reliability profiles of NTN environments, particularly when NTNs are used as backhaul. Furthermore, it is difficult to manage individual traffic flows and perform efficient resource allocation and routing when a large number of 5G traffic flows are present in NTN systems. To address these challenges, we propose an optimization framework that enhances QoS handling by introducing an NTN QoS Identifier (NQI) and grouping 5G traffic into NTN slices based on similar requirements. This enables unified resource control and routing for a large number of 5G flows in NTN systems. In this paper, we present the detailed procedure of the proposed framework, which consists of 5QI to NQI mapping, NTN traffic to NTN slice mapping, and slice-level flow and routing optimization. We evaluate the framework by comparing multiple mapping schemes through numerical simulations and analyze their impact on overall network performance.

Figures (4)

• Figure 1: Proposed optimization framework for 5G-NTN interconnected systems. The equations shown in this figure will be introduced in Section \ref{['section:optimization_framework']}.
• Figure 2: User, traffic, and network configuration in the simulation.
• Figure 3: Results of the user-averaged satisfaction ratio of the flow $\bar{s}_{f}$ and latency $\bar{s}_{\ell}$ with the different conditions of the mapping (Conditions 1 to 6) and weights in the cost function.
• Figure 4: Result of the calculation time required for solving the optimization problem with $(w_{f},w_{\ell})=(0.5,0.5)$.