Search Results (22)

The convergence of optical burst switching (OBS) and elastic optical networks (EON) offers a promising pathway for 6G IoT backhaul. However, existing solutions treat OBS and EON separately and rely on heuristic resource allocation that fails to meet stringent QoS demands. This paper proposes a fully AI-driven converged OBS/EON architecture integrating a hybrid switching fabric, a multi-agent deep reinforcement learning (DRL) orchestrator, and a federated learning (FL) plane for autonomous, QoS-aware resource provisioning. The control plane implements multi-agent Proximal Policy Optimization (PPO) for joint burst scheduling, routing, modulation selection, and spectrum allocation. The orchestration plane employs q-fair FL for privacy-preserving cross-domain traffic prediction. Mathematical formulations of the optimization problem with spectrum, GSNR, and delay constraints are provided, along with pseudo-algorithms. Simulations over a 14-node NSFNET topology demonstrate a 78% reduction in blocking probability, a 42% improvement in spectral efficiency, sub-millisecond URLLC delays, and a Jain’s fairness index of 0.92, while preserving data privacy. The framework builds upon SDN principles for seamless integration with optical transport infrastructures. Full article

Satellite–terrestrial integrated networks (STINs) can extend remote sensor telemetry, remote Internet of Things (IoT), and emergency communication services beyond terrestrial coverage, but their evaluation is complicated by heterogeneous mobility, channel, resource, and control-plane dynamics. This study presents a software-based modular testbed and performance-emulation framework for STINs. The framework integrates scenario generation, model-driven data processing, replaceable algorithm engines, scheduler-based execution control, and a Kafka-style message interface. It models terrestrial, unmanned aerial vehicle, and low-Earth-orbit satellite entities and provides link-budget abstraction, access control, mobility-aware handover, traffic generation, scheduling, load balancing, adaptive routing, and multi-mode transmission for mixed sensing and communication traffic. The representative strategies are evaluated using a lightweight emulation model parameterized by standards-informed NTN and link-budget assumptions. Representative results reveal tradeoffs between access, handover, routing, and scheduling strategies, together with sensitivity to workload, mobility, outage, demand, and selected model parameters. The proposed framework therefore supports traceable STIN strategy evaluation for remote sensor networks, sensing-data backhaul, and remote-IoT service scenarios under explicit emulation assumptions. Full article

The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, CubeSats, and terrestrial infrastructures that comprise the framework of Space–Aerial–Ground Integrated Networks (SAGINs) as vital enablers of the International Mobile Telecommunications (IMT)-2030 standards. This paper examines the role of UAVs in providing flexible and quickly deployable airborne connectivity. It also discusses how CubeSats enhance global coverage through low-latency relaying and resilient backhaul links from low Earth orbit (LEO). Additionally, the paper highlights how terrestrial systems contribute high-capacity, densely concentrated communication layers that support various end-user applications. By examining their interoperability and coordinated resource allocation, the paper underscores that the seamless interaction of SAGIN nodes is essential for achieving the ultra-reliable, intelligent, and pervasive communication capabilities envisioned by IMT-2030. As 6G aims for ultra-low latency, high reliability, and massive connectivity, UAVs and CubeSats emerge as key enablers for extending coverage and capacity, particularly in remote and dense urban regions. Furthermore, the role of large language models (LLMs) is explored for intelligent network management and real-time data optimization, while quantum communication is analyzed for ensuring security and minimizing latency. The integration of LLMs into quantum-enhanced edge intelligence for SAGINs represents an emerging research frontier for adaptive, high-throughput, and context-aware decision-making. By exploiting quantum-assisted parallelism and entanglement-based optimization, LLMs enhance the processing efficiency of multimodal data across space, aerial, and terrestrial nodes. This paper further investigates distributed quantum inference and multimodal sensor data fusion to enable resilient, self-optimizing communication systems comprising a high volume of data traffic, which is a critical bottleneck in the global connectivity transition. LLMs are envisioned as cognitive control centers capable of generating semantic representations for mission-critical communications that enhance energy efficiency, reliability, and adaptive learning at the edge. The findings of the survey reveal that quantum-enhanced LLMs overcome challenges pertaining to bandwidth allocation, dynamic routing, and interoperability in existing classical communication systems. Overall, quantum-empowered LLMs significantly assist intelligent, autonomous, and immersive communications in SAGIN, while enabling secure, privacy-preserving communication. Full article

Rural freight mobility and logistics face persistent challenges, including inadequate road infrastructure, high transportation costs, safety risks, tolls at link access points, and dispersed demand. Traditional inventory routing models often fail to address these complexities, especially in rural contexts where alternative routing options and integrated in-haul/back-haul operations are essential for improving efficiency and reducing empty miles. This study proposes a bi-objective mathematical model for the inventory routing problem in rural logistics, incorporating multiple routing attributes (transportation costs, risks, link-access tolls, and distances) and inventory dynamics (integrated in-haul and back-haul visits). The model aims to minimize total logistics costs and accident risk while balancing operational expenses and safety considerations. Risk estimation is derived from crash data along rural road links connecting distribution nodes. A real-world case study involving Walmart distribution centers in Macclenny, Baker County, Florida, and several rural Supercenters is conducted to validate the model. A modified Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is developed and compared with CPLEX for solution efficiency across small and large-scale problem instances. Results indicate that the proposed approach outperforms classical methods, improves routing decisions in rural logistics systems, and achieves cost savings of up to 17% for the evaluated objectives, emphasizing the importance of using multi-attribute, multi-route network structures in rural logistics optimization. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

Low-Earth-Orbit (LEO) satellite networks have the advantage of global internet coverage and low latency, and they have enjoyed great success in the past few years. In LEO satellite networks, laser-based inter-satellite links (ISLs) are widely employed to achieve on-board data relay, and further to provide high-capacity backhaul worldwide. However, ISLs are prone to break due to the outage of the ISL capturing, tracking, and aiming systems. Meanwhile, breaks caused by different reasons can last from milliseconds to hours. The hybrid ISL fault leads to the on-board routing protocol to flap frequently, thus causing high routing overheads, low convergence speed, and degraded service consistency. In this work, we propose a hybrid fault detection mechanism to identify transient and long-term ISL outage. Further, for transient link outage, the segment routing-based loop-free backup path is adopted to provide real-time transmission recovery, and precise global route convergence is adopted to restore the long-term routing failure. For the inconsistent routing table switch between the phase from transient to long-term fault, we propose a dual timer mechanism to make sure the path can be smoothly switched without micro-loops. Simulation results validate the feasibility and efficiency of the proposed scheme. Full article

This paper provides a study of the different alternatives that are being considered in the 5G-ROUTES project to establish seamless 5G connectivity in a maritime environment both from an architectural point of view and also from the definition of field trials to evaluate the performance and dependability of the proposed solution. As expected, the main challenge in providing 5G connectivity on the sea is to provide coverage over large areas of open water. Thus, as a starting point, this paper presents a measurement campaign that was conducted to assess the current coverage in the Baltic Sea, which concluded that the current terrestrial networks cannot guarantee sufficient coverage. Next, the solution architecture and trials proposed by 5G-ROUTES are described, which are based on the integration of satellite and leading-edge multi-hop connectivity in 5G networks. Utilizing satellite backhaul can potentially overcome the connectivity challenge from the terrestrial domain to the maritime domain, while multi-hop connectivity ensures that coverage is extended among the different ships that are navigating the sea. Furthermore, this paper describes how the project will evaluate, in field trials tailored to this maritime environment, common connectivity key performance indicators (KPIs) such as latency, throughput, availability and reliability. This paper concludes by providing a vision for applying the obtained results and insights to maritime transportation and other remote areas where the deployment of a suitable 5G infrastructure may be challenging or costly. The findings will be used to guide the design of future 5G networks for marine applications and to identify the most effective methods for providing secure and dependable communication in a maritime setting. Full article

5G wireless networks have as one of the main characteristics the large-scale deployment of small cells (microcells, picocells, etc.), which is expected to bring several advantages in what concerns the high speed and low latency connectivity of the users. This large-scale deployment of small cells also raises several technical challenges, provisioning the backhaul connectivity being one of them. The paper considers the situations when small cells are deployed temporarily or are deployed in a vehicle transporting many passengers, situations when the traditional wired or wireless backhaul solutions could be too costly to be used. The paper proposes, as an alternative solution, the use as backhaul connections of the wireless links set up in the macro cells which cover the location of the small cell. The paper proposes several Game Theory (GT)-based Load-Balancing (LB) algorithms for distributing the traffic of the small cell users over the macro cell links. The proposed LB algorithms are evaluated by computer simulations and are compared with “classical” LB algorithms considered as references. The performed computer simulations show that the auction-based algorithms have the best performance in terms of delay suffered by the transmitted data packets, while the selfish routing type algorithm has weaker performance, even behaving poorly than some of the reference non-GT-based algorithms. The paper also considers the situation when several small cell APs are deployed in a limited area or a vehicle and the user groups that attach to different APs should be identified. The paper proposes two GT-based user clustering algorithms, and the performance of these algorithms are evaluated by computer simulations. These simulations show that even simple clustering algorithms could improve the distribution of the traffic over the neighbor small cell APs and reduce the delay experienced by the data packets in the transmission system. Full article

In small spaces in which typical cell towers cannot be constructed, distributed antenna systems (DASs) are the preferable approach for increasing network coverage, as they are a superior solution for congested, high-volume areas. However, due to their high cost and complexity in backhaul routing, reconfigurable intelligent surfaces (RISs) are a promising solution to overcome the major drawbacks of DAS systems while improving network coverage. Thus, this work investigated a correlation of execution in an RIS-aided system cell framework and DAS-aided system cell framework where a simple and precise structure for the performance measurement of area spectral efficiency (ASE) and energy efficiency (EE) under realistic channel presumptions was introduced. The analysis started with the downlink ergodic capacity with regards to the RIS framework and DAS framework under a generalized Nakagami-m fading channel with the presence of path-loss attenuation and interference from co-channel base stations (BSs), and was simplified further by utilizing a moment-generating function (MGF)-based approach. From the computed expression, the effects of traffic activity, EE and ASE were derived and analyzed for both systems in the presence of co-channel interference. The results were then verified by comparing them with Monte Carlo simulations, and the findings show that the two outcomes generally match. Based on these, it is demonstrated that the ASE performance of the RIS-assisted system in various traffic activity conditions outperforms the DAS-aided system; however, in high signal-to-noise (SNR) regions with full traffic activity, the ASEs are highest for both systems; by only 0.005 bits/s/Hz/km². Full article

Urban pickup and delivery (PUD) activities are important for logistics operations. Real operations for general freight involve a high degree of complexity due to daily variability. Discrete-event simulation (DES) is a method that can mimic real operations and include stochastic parameters. However, realistic vehicle routing is difficult to build in DES models. The objective is to create a DES model for realistic freight routing, which considers the driver’s routing decisions. Realistic models need to predict the delivery route (including time and distance) for variable consignment address and backhaul pickup. Geographic information systems (GIS) and DES were combined to develop freight PUD models. GIS was used to process geographical data. Two DES models were developed and compared. The first was a simple suburb model, and the second an intersection-based model. Real industrial data were applied including one-year consignment data and global positioning system (GPS) data. A case study of one delivery tour is shown, with results validated with actual GPS data. The DES results were also compared with conventional GIS models. The result shows the intersection-based model is adequate to mimic actual PUD routing. This work provides a method for combining GIS and DES to build freight operation models for urban PUD. This has the potential to help industry logistics practitioners better understand their current operations and experiment with different scenarios. Full article

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO₂ emission is mandatory to confront global climate change, we need energy efficient network management for such denser small-cell heterogeneous networks (HetNets) that already suffer from observable power consumption. We establish a dual-objective optimization model that minimizes energy consumption by switching off unused small cells while maximizing user throughput, which is a mixed integer linear problem (MILP). Recently, the deep reinforcement learning (DRL) algorithm has been applied to many NP-hard problems of the wireless networking field, such as radio resource allocation, association and power saving, which can induce a near-optimal solution with fast inference time as an online solution. In this paper, we investigate the feasibility of the DRL algorithm for a dual-objective problem, energy efficient routing and throughput maximization, which has not been explored before. We propose a proximal policy (PPO)-based multi-objective algorithm using the actor-critic model that is realized as an optimistic linear support framework in which the PPO algorithm searches for feasible solutions iteratively. Experimental results show that our algorithm can achieve throughput and energy savings comparable to the CPLEX. Full article

In recent years, China’s influence as the dominant importer of waste products has reshaped global waste trade through restrictive programs such as Operation Green Fence in 2013 and National Sword in 2017. These restrictions have greatly affected not only China’s import of waste products but also the international trade and global logistics of these products. China’s import restrictions in 2017 decreased the country’s import of waste plastic by 92% and used paper by 56%. It also increased the unit value of these two categories of waste by 27% and 13%, respectively, showing an improvement in the quality of imported waste. Most of these impacts originate from intensive margins. The restrictions diverted the flow of waste mostly to the low- and middle-income countries of the East Asian and Pacific regions along with Europe and Central Asia, as their imports increased by 161% and 266% for waste plastic and 101% and 77% for used paper, respectively. Compared with Operation Green Fence, the impact of the 2017 National Sword has been much higher, with shipping companies faced with a lack of products on backhaul routes and forced to change their longstanding practices. Full article

With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, the diversification of user equipment (UE) in the 5G environment also makes heterogeneous networks (HetNets) more prevalent. HetNets allow UE of different network standards to access small cells, while the base stations of small cells access a macro base station (BS) to form a multihop wireless heterogeneous backhaul network. However, how to effectively combine these two technologies by efficiently allocating the antennas of each BS during the route construction process of heterogeneous wireless backhaul networks is still an important issue that is yet to be solved. In this paper, we propose an algorithm called preallocated sequential routing (PSR). Based on the links’ channel conditions and the available antennas and location of BSs, it builds a wireless heterogeneous network backhaul topology and adjusts each link’s transmitting and receiving antennas to maximize total utility. Simulation results showed that the proposed algorithm significantly improved the overall utility and the utility of the outer area of heterogeneous networks. Full article

In the dynamic vehicle routing problem with mixed backhauls (DVRPMB) both pick up orders and delivery orders, not related to each other, are served. The requests of the former arrive dynamically while the latter are known a priori. In this study, we focus on the case of limited fleet, which fulfills all delivery orders, but may not have enough capacity to serve all pick up orders within the available working horizon. The problem’s dynamic nature and the attention to customer service raise interesting considerations, especially related to the problem’s objectives. The problem is solved through periodic re-optimization, acknowledging the fact that this pseudo-dynamic approach may lead to some limitations. For the underlying (static) optimization problem we propose appropriate objective functions, which account for vehicle productivity and propose a branch-and-price (BP) approach to solve it to optimality. The results indicate how the performance of the various objectives is impacted by different re-optimization frequencies and policies in this practically relevant environment of dynamic demand served by a limited fleet. Specifically, extensive experimentation indicates that accounting for vehicle productivity within a typical periodic re-optimization solution framework may result to higher customer service under a range of operational settings, in comparison to conventional objectives. Full article

Ultra-Dense Network (UDN) deployment is considered a key element to achieve the requested capacity in future fifth-generation (5G) mobile networks. Backhaul networks in UDNs are formed by heterogeneous links with multi-hop connections and must handle massive traffic. Backhauling in future 5G networks may represent the capacity bottleneck. Therefore, there is the need for efficient and flexible routing schemes able to handle the dynamism of the traffic load in capacity-limited networks. Toward this goal, the emerging Software-Defined Network (SDN) paradigm provides an efficient solution, transferring the routing operation from the data plane switches to a central controller, thus achieving more flexibility, efficiency, and faster convergence time in comparison to conventional networks. This paper proposes and investigates an SDN-approach for an efficient routing in a capacity-limited backhaul network that carries data and control traffic of a heterogeneous UDN. The routing algorithm is centralized in the SDN controller and two different types of traffic flow are considered: data and control plane coordination traffic. The goal is to reduce or even to avoid the amount of traffic that the backhaul network is not able to support, distributing in a fair way the eventual lack of bandwidth among different access points. Simulation results show that with the considered approach the performance significantly improves, especially when there is an excess of traffic load in the network. Moreover, thanks to the SDN-based design, the network can reconfigure the traffic routing depending on the changing conditions. Full article