Jun 2026· Siberian Aerospace Journal· 0 citations· 19 references
TL;DR
The article concludes that this concept can provide a theoretical basis for routing in hybrid communication systems and can naturally extend to lossy transmission models, dynamic network scenarios, and integrated network-control problems.
Abstract
This article develops mathematical routing models for hybrid communication systems that integrate ground, stratospheric, and space segments. The study addresses networks where topology, demand matrices, link capacities, loss levels, and delay characteristics vary at the same time. The paper aims to formulate a multilevel mathematical routing concept that combines three core ideas: fractional multicommodity flow, path-limited routing, and delay minimization. The study uses multicommodity flow models on directed graphs, path-based formulations with a bounded number of routes per demand, convex delay-aware objectives, and an analysis of modern approximation algorithms. The results show that fractional multicommodity flow defines the upper level for estimating throughput, fairness, and priority-aware service; path-limited formulations translate this solution into engineering policies that a routing plane can install and maintain; and delay-oriented models account for quality-of-service requirements and temporal dynamics. The paper also shows how this concept links routing with radio-resource allocation, structural adaptation of the network, and routing-information dissemination. The results support a multistage routing logic in which a fractional formulation estimates the theoretical upper bound, a path-limited model compresses this solution into an installable routing policy, and a delay-oriented stage refines the decision for hybrid-network operation. The proposed concept applies to the design and control of communication systems that link spacecraft, airborne platforms, and terrestrial infrastructure. The article concludes that this concept can provide a theoretical basis for routing in hybrid communication systems and can naturally extend to lossy transmission models, dynamic network scenarios, and integrated network-control problems.
A duality-based characterization of implementability of dynamic edge flows for the multi-source, multi-destination case and a non-trivial proof that this assumption is always fulfilled for finitely supported edge flows with costs representing weighted travel times are provided.
This research bridges the gap between theoretical queuing models and practical routing strategies, contributing to the development of more efficient routing protocols for MANETs and demonstrates significant improvements in delay, throughput, routing overhead and node lifetime under realistic traffic conditions.
Time Sensitive Networking (TSN) is a key technology for deterministic communication in industrial control systems. Its Cyclic Queuing and Forwarding (CQF) mechanism can provide transmission guarantees with low latency and low jitter. However, existing studies often treat routing and scheduling as separate problems, overlooking their strong coupling relationship. Meanwhile, the injection of CQF with time slot offsets may cause some flows to exceed their deadlines and become unschedulable. This paper proposes a Genetic Algorithm-based Joint Routing and Scheduling Algorithm for CQF (Gene-JRSC). The algorithm aims to maximize the number of schedulable flows and establishes an Integer Linear Programming (ILP) model. A genetic algorithm (GA) is employed to solve the model, achieving joint optimization of routing strategy and schedulability. Simulation experiments demonstrate that, compared to existing algorithms, Gene-JRSC significantly improves the scheduling success rate and queue resource utilization under different network scales and traffic loads.
This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.
A new Enhanced Intelligent-based Energy and Mobility, and Obstacle-aware Clustering (EIEMOC) protocol to control the network congestion while meeting End-to-End Delay (E2D) constraints in delay-constrained FANET applications.
J. Rajeswari, R. Kousalya· International Journal of Ele...· 0 citations
This article addresses the planning and allocation of spectral resource blocks for unicast (UC) and Multicast-Broadcast Single Frequency Network (MB-SFN) transmissions in dense Sixth-Generation (6G) cellular networks, where the choice of transmission mode directly influences spectral efficiency and Quality of Service (QoS). The objective is to identify the conditions under which the intercellular cooperation inherent to MB-SFN becomes more efficient than the UC mode for spectral resource block utilization under QoS constraints. To this end, we conduct a comparative performance analysis based on: i) Monte Carlo (MC) simulations, used as a numerical benchmark to accurately capture complex radio interactions, and ii) a fluid analytical framework, based on a continuous approximation of the network in which the discrete structure of base stations is replaced by a homogeneous surface density. Within this framework, we derive analytical expressions for the Signal-to-Interference-plus-Noise Ratio (SINR), enabling a tractable characterization of aggregate interference. Resource block allocation expressions are then proposed for both modes, incorporating SINR and outage probability as QoS constraints. The main contribution of this paper lies in deriving, using the fluid framework, an explicit analytical expression for the critical user threshold that characterizes the switch from UC mode to MB-SFN mode, beyond which the latter becomes more spectrum-efficient. The switching decision highlights the duality between the two modes: MB-SFN is constrained by the minimum SINR with resource consumption independent of the number of users, whereas UC mode depends on the average SINR and consumption proportional to the number of users. An in-depth analysis of the combined effect of network parameters is also conducted, highlighting their interactions and their influence on the switching threshold. Finally, the strong agreement with MC simulations validates the accuracy of the fluid framework, providing an effective analytical tool for optimizing adaptive transmission strategies.
M. Younes, C. Perrine· IEEE Open Journal of the Com...· 0 citations