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.
We consider dynamic network flows and study the following question: Which dynamic edge flows can be implemented as tolled dynamic equilibrium flows? We study this question for the heterogeneous-user model, where the flow particles are partitioned into populations characterized by their own source,destination-pairs and a cost function associating with any walk and departure time some costs. As our two main results, we first provide a duality-based characterization of implementability of dynamic edge flows for the multi-source, multi-destination case. Secondly, we derive both, a combinatorial and duality-based characterization of implementability of dynamic edge flows for the multi-source, single-destination case. Both results are derived under a fairly general network loading model. For the proof, we make several technical contributions: We formulate a novel infinite dimensional optimization problem, where the goal is to minimize the aggregated costs of the particles with respect to the fixed network loading induced by the given edge flow. This requires the recently introduced concept of autonomous network loadings for which we show several new structural insights. In particular, we give an alternative (tighter) characterization of the existence of autonomous network loadings for our setting by deriving a generalization of a result of M.A. Zarecki\u{\i} on the Lusin $N^{-1}$ property of absolutely continuous monotone functions which may also be of independent interest. These insights allow us to prove the stated characterizations under the assumption of strong duality. Finally, for the case of a single-destination, we are able to provide a non-trivial proof that this assumption is always fulfilled for finitely supported edge flows with costs representing weighted travel times.
In this work, we present a model for multipath routing in Mobile Ad Hoc Networks (MANETs) that considers both bounded and unbounded buffer sizes at each Mobile Node (MN). Traditional multipath routing approaches primarily focus on traffic distribution and path optimization but often overlook the impact of queuing dynamics in practical network scenarios. Existing methods typically assume either infinite buffer capacity or use simplistic delay models that fail to capture the queuing effects caused by buffer constraints at intermediate nodes. As a result, they may not accurately estimate end-to-end latency, leading to suboptimal routing decisions. To address this gap, we analyze the delay characteristics of multipath routing using M/M/1/R, M/M/m, and M/M/m/R queuing networks, which allow for a more precise evaluation of network performance under varying buffer sizes and service capacities. Unlike previous studies, which predominantly rely on simplified queuing assumptions, our model explicitly incorporates both finite and infinite buffer constraints at each MN to assess their impact on delay. The analysis is based on Burke’s Theorem for traffic distribution and Little’s Theorem for latency estimation, enabling optimal path selection based on real-time queuing behavior. Simulation results validate the effectiveness of our approach, demonstrating significant improvements in selecting the best path based on realistic queuing effects. The model is also benchmarked against AOMDV and demonstrates significant improvements in delay, throughput, routing overhead and node lifetime under realistic traffic conditions. This research bridges the gap between theoretical queuing models and practical routing strategies, contributing to the development of more efficient routing protocols for MANETs.
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.
Increases in the use of UAVs for communication have led to the widespread emergence of Flying Ad hoc Networks (FANET). Conversely, UAV’s mobility and environmental obstacles affect communication links, resulting in link unreliability and inefficient routing. To combat these challenges, an Energy and Mobility-Aware Stable and Safe clustering (EMASS) protocol has been developed, which prevents obstacles in the routing path and minimizes the influence of high mobility on data transfer. However, it does not address the congestion issue in FANET routing, which degrades the data transfer in delay-constrained applications. Hence, this manuscript proposes 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. The main optimization objectives of this protocol are the cumulative rates over the connections and various factors that influence the E2D for 1-hop communication. First, a dispersed delay-aware congestion control scheme is developed that integrates a 1-hop delay constraint to obtain the best solution. Then, a delay support factor is introduced for every connection, and the 1-hop delay constraint is updated by conjointly merging the cumulative arriving flow and the probability of data being rejected at a specific connection. Thus, this protocol maximizes the system reliability and reduces the E2D in a dispersed manner. Finally, extensive simulations establish that the EIEMOC achieves higher network performance compared to the classical protocols in FANETs.
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