Enhancing Resource Utilization of Non-terrestrial Networks Using Temporal Graph-based Deterministic Routing

TL;DR

The paper addresses deterministic routing in non-terrestrial networks (NTNs) where traffic must satisfy an end-to-end delay bound $B_f$ despite dynamic topology. It introduces a time-expanded graph (TEG) to model time-slotted NTN resources and formulates the routing problem, then linearizes it to an ILP for a robust upper bound; to reduce complexity, it extends to an extended time-expanded graph (ETEG) with virtual nodes/edges and presents a polynomial-time hop-by-hop routing algorithm that computes time-featured paths. The approach jointly utilizes link capacity and node storage across cycles, enabling cross-cycle propagation and caching to meet $B_f$ while keeping complexity practical for large NTNs. Simulations on a partial Starlink-like NTN show substantial improvements in traffic acceptance over SPR, STR, and CGR, with runtimes far lower than the ILP-based upper-bound method, demonstrating practical impact for scalable deterministic routing in NTNs.

Abstract

Deterministic routing has emerged as a promising technology for future non-terrestrial networks (NTNs), offering the potential to enhance service performance and optimize resource utilization. However, the dynamic nature of network topology and resources poses challenges in establishing deterministic routing. These challenges encompass the intricacy of jointly scheduling transmission links and cycles, as well as the difficulty of maintaining stable end-to-end (E2E) routing paths. To tackle these challenges, our work introduces an efficient temporal graph-based deterministic routing strategy. Initially, we utilize a time-expanded graph (TEG) to represent the heterogeneous resources of an NTN in a time-slotted manner. With TEG, we meticulously define each necessary constraint and formulate the deterministic routing problem. Subsequently, we transform this nonlinear problem equivalently into solvable integer linear programming (ILP), providing a robust yet time-consuming performance upper bound. To address the considered problem with reduced complexity, we extend TEG by introducing virtual nodes and edges. This extension facilitates a uniform representation of heterogeneous network resources and traffic transmission requirements. Consequently, we propose a polynomial-time complexity algorithm, enabling the dynamic selection of optimal transmission links and cycles on a hop-by-hop basis. Simulation results validate that the proposed algorithm yields significant performance gains in traffic acceptance, justifying its additional complexity compared to existing routing strategies.

Figures (6)

• Figure 1: Modeling a typical NTN using a TEG.
• Figure 2: An ETEG model.
• Figure 3: An application of the ETEG-based deterministic routing algorithm.
• Figure 4: Evaluation of traffic acceptance.
• Figure 5: Evaluation of average running time.

Theorems & Definitions (5)

• Definition 1
• Theorem 1
• proof
• Theorem 2
• proof