Recently, large-scale and mega low earth orbit (LEO) constellations have become the primary development trends in satellite networks. Considering the complex topology and limited processing capabilities of LEO satellites, traditional distributed routing algorithms with significant flooding overhead are not suitable for the era of mega constellations. In this context, centralized routing technology based on Software Defined Network (SDN) architecture has been widely applied. However, centralized routing technology exhibits a slower response to inter-satellite link (ISL) changes due to longer informing path, which induce that longer time is required to restore traffic in the event of ISL terminal or satellite /hardware faults. To this end, this paper proposes a lightweight Geographic-based Auxiliary Routing Scheme (GARS), adapted to Walker Delta constellation, as a traffic fast restoration strategy for SDN-based centralized routing scheme. This scheme has an ultralow computation complexity ofO(1), enabling the rapid calculation and establishment of temporary paths to restore service during the SDN controller in ground station rerouting. In addition, an optimized geometric routing algorithm with anti-loop mechanism is proposed to enhance the stability and reliability of geometric routing algorithm. Simulation results show that (1) compared with traditional centralized routing system, the routing scheme proposed in this paper shortens interruption time of traffic by at least 65.5% and reduces mean packet loss ratio by at least 67.2% without increasing link load. (2) Compared to other traditional geometric routing algorithms without advanced anti-loop mechanism, GARS with the optimized anti-loop mechanism also effectively ensures the continuity of service, reducing packet loss rate and interruption time by at most 96%.

Free space optical (FSO) communication is a promising solution for next-generation communication networks. Atmospheric turbulence, however, severely degrades its performance. We thus propose a novel turbulence-robust end-to-end FSO communication system (TRFSO) that performs joint training across the entire transmission process from source to transmission channel to destination. To introduce physical FSO channel impairments into the training loop, we developed a bidirectional long short-term memory (BiLSTM)-based FSO channel model. Through end-to-end joint training, the system achieved high quality and robust transmission under dynamic atmospheric turbulence. Trained on experimental data collected over a physical FSO link under varying turbulence conditions, the proposed model accurately reproduced real-world channel distortions, achieving a minimum Kullback–Leibler (KL) divergence of 0.0019 nats in amplitude distribution matching. Our experimental results revealed that TRFSO significantly outperformed conventional separate coding modulation schemes in FSO links. Moreover, under strong turbulence, TRFSO achieved a 3.5 dB gain in average multi-scale structural similarity (MS-SSIM) compared with the same network architecture trained without the channel model.

With the explosive increase in wireless devices, enabling sensing between incompatible radios has become critically beneficial. Integrating diverse IoT devices enhances sensing accuracy by providing richer data, while utilizing the diverse characteristics of heterogeneous signals meets sensing needs in complex environments. However, most existing wireless sensing methods primarily focus on homogeneous signals, while research on sensing with heterogeneous signals is still in its infancy. In this paper, we proposeCrossSense, a novel Cross-Technology Sensing (CTS) framework that enables sensing between incompatible WiFi and LoRa device.CrossSenserecovers the fine-grained trajectory of a WiFi transmitter based on its emulated LoRa signals. To decompose the motion feature components of WiFi transmitter, we develop a chirp difference vector model that utilizes the energy peak within each chirp window for sensing. We model the relationship between sampling frequency offsets and oscillation frequency offsets among heterogeneous devices to guide the extraction of motion features from the emulated signal. We also propose a greedy-based peak enhancement method to calculate the optimized LoRa phases, minimizing the impact of phase discontinuity caused by cyclic prefix (CP) errors. We implement a prototype ofCrossSenseon the USRP platform. The extensive experiments demonstrate thatCrossSensecan achieve an efficient Cross-Technology Sensing with$2.92cm$distance accuracy and$0.26cm/s$speed accuracy over a$120m$sensing range.

Digital twin (DT) techniques have been proposed for the autonomous operation and lifecycle management of next-generation optical networks. To fully utilize potential capacity and accommodate dynamic services, the DT must dynamically update in sync with deployed optical networks throughout their lifecycle, ensuring low-margin operation. This paper proposes a dynamic-updating DT for the lifecycle management of optical networks, employing a hybrid approach that integrates data-driven and physics-informed techniques for fiber channel modeling. This integration ensures both rapid calculation speed and high physics consistency in optical performance prediction while enabling the dynamic updating of critical physical parameters for DT. The lifecycle management of optical networks, covering accurate performance prediction at the network deployment and dynamic updating during network operation, is demonstrated through simulation in a large-scale network. Up to 100 times speedup in prediction is observed compared to classical numerical methods. In addition, the fiber Raman gain strength, amplifier frequency-dependent gain profile, and connector loss between fiber and amplifier on C and L bands can be simultaneously updated. Moreover, the dynamic-updating DT is verified on a field-trial C+L-band transmission link, achieving a maximum accuracy improvement of 1.4 dB for performance estimation post-device replacement. Overall, the dynamic-updating DT holds promise for driving the next-generation optical networks towards lifecycle autonomous management.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. However, for the downlink from WiFi to multiple IoT technologies, serially emulating and transmitting the data of each IoT technology has extremely low spectrum efficiency. In this paper, we propose WiCast, a parallel CTC that uses IEEE 802.11ax to emulate a composite signal that can be received by commodity BLE, ZigBee, and LoRa devices. By taking advantage of OFDMA in 802.11ax, WiCast uses a single Resource Unit (RU) for parallel CTC and sets other RUs free for high-rate WiFi users. But such a sophisticated composite signal is very easily distorted by emulation imperfections, dynamic channel noises, cyclic prefix, and center frequency offset. We propose a CTC link model that jointly models the emulation errors and channel distortions. Then we carve the emulated signal with elaborate compensations in both time and frequency domains. Based on the proposed CTC scheme, a unified Media Access Control approach is introduced to discover and synchronize the heterogeneous IoT devices. We implement a prototype of WiCast using USRP N210 platform along with commodity ZigBee, BLE, and LoRa devices. The extensive experiments demonstrate WiCast can achieve an efficient parallel transmission with the aggregated goodput up to 390.24kbps.

In the architecture of Digital Twin(DT) optical networks, model management is a key component to ensure efficient network operations. It involves not only precise digital mapping of physical network entities but also includes model integration, dynamic updates, and real-time inference. Considering the diversity and complexity of model types when building an optical network DT, this paper proposes a novel DT model management framework for Optical Transport Network (OTN). This framework enables uploading, training, evaluating, and deploying models to perform inference tasks. It supports both online and offline training to meet diverse business demands. Furthermore, to adapt to continuously changing data and environments, the system integrates an incremental learning technique, which enables continuous updates and optimization of models. Additionally, the framework supports the deployment and inference of mathematical models. Additionally, the components deployed by this framework are entirely managed through containerization technology. This approach ensures portability and enhances the system's fault tolerance and high availability. This framework provides an efficient, user-friendly, and scalable solution for comprehensive model management.

The SFC deployment problem has proved to be NP-hard and has attracted great research interests. At present, most existing studies focus on deploying SFC through virtual network functions (VNFs) and few studies involve hybrid VNF and physical network function (PNF) environment. However, in the transition to a comprehensive network function virtualization (NFV) network, hybrid VNF and PNF environment is possible and important. Currently, VNF and PNF both play an important role in today’s cloud networks. PNF is far superior in processing speed, but VNF has advantages in flexibility and cost. Using VNF or PNF alone may not meet user needs, affecting user experience and system performance. Hence, this paper studies the SFC deployment problem in hybrid VNF and PNF environment. A cost and latency customized dynamic SFC deployment (CL-SFCD) scheme is proposed. Instead of giving priority to the PNF or the VNF roughly, the CL-SFCD scheme maps network functions to VNFs or PNFs according to the SFC requester’s personalized demands to satisfy the demands of different users. It aims to minimize the weighted value of total cost and latency. The CL-SFCD scheme is formulated as a mixed integer linear programming (MILP) model for exact results. Heuristic algorithms are developed to provide an approximate optimal solution in large-scale cloud networks. Numerical results show that the CL-SFCD scheme achieves performance that balances cost and latency based on user requirements than the VNF-only,VNF-GA, and PNF-only deployment schemes.

Optical inter-satellite link (ISL) with tens/hundreds Gbps is applied into LEO constellation to overcome the insufficient bandwidth of microwave ISL. Moreover, compared with GEO/MEO constellation, LEO constellation suffers from frequent topology switching because of the lowest orbital-altitude. Topology switching between adjacent topology snapshots is generally done through ISL handover (remove or establish ISL). However, service interruption caused by ISL handover within topology switching is a vital issue to address. After ISL handover occurs, isolated topology and slow route convergence (derived from long ISL delay and forwarding the updated message among hundreds/thousands of satellites) may cause severe service interruption. But considering the fact that time-varying topology is predictable and all the ISL handover could be pre-scheduled for topology management purpose, service interruption problem is investigated from the aspect of ISL handover and routing strategy in this paper. Owing to long establishing time of optical ISL, grouped optical ISL handover scheme is raised to decrease route convergence time during topology switching. Furthermore, predictive-update routing scheme based on Open Shortest Path First (OSPF-PUR) is proposed to minimize route convergence time for each ISL handover. For predictable ISL handover, in OSPF-PUR scheme, forwarding table is refreshed according to locally stored ISL handover information but without flooding. Simulation results illustrate that grouped optical ISL handover scheme reduces route convergence time within topology switching while topology connectivity is guaranteed. The route convergence time of ISL handover is effectively decreased by OSPF-PUR scheme. Besides, for OSPF-PUR scheme, packet loss rate remains unchanged while topology switching frequency increases.

Following successful adoption of cloud computing, many service providers (SPs) are now using high-performance Virtual Machines (VMs) located in large datacenters owned by public cloud infrastructure providers to deploy their virtual network functions (VNFs). Since using these VMs has a cost depending on utilization time, a complex problem of VNF placement and scheduling (VPS) must be addressed to achieve satisfactory network performance (e.g., latency) while minimizing the cost paid to lease VMs. In this study, a cost-efficient VPS scheme (CE-VPS) is proposed to address the VPS problem in public cloud networks considering dynamic requests of ordered sequences of VNFs. Our CE-VPS scheme goes beyond existing solutions as it models some important practical aspects such as an additional latency incurred by booting a VM and installing a VNF instance. Also, CE-VPS considers that VNFs can be multi-threaded or single-threaded, and that their throughput as a function of allocated computing resources must be modeled differently. CE-VPS is formulated as a mixed inter linear program (MILP) and also as an efficient heuristic algorithm. CE-VPS achieves lower cost and latency than conventional Best-Availability and Cost-Efficient Proactive VNF Placement schemes, and a better trade-off between resource consumption and latency performance than a conventional Low-Latency scheme.

By using complementary rhombus resonator (CRR), a small‐size and low‐profile microstrip patch antenna (MPA) with broad bandwidth has been proposed. Parametric studies were conducted to illustrate the working principle of the proposed antenna. An additional resonance is introduced by the CRR to broaden the bandwidth. Compared with the MPA without the CRR, the bandwidth of the proposed antenna is increased by 200%. The measured results are in good agreements with the simulated ones, which demonstrate that this design provides a way to obtain the broadband antenna.

This paper addresses the minimum network cost problem for survivable virtual optical network mapping in flexible bandwidth optical networks. We develop an ILP model and the LBSD (the largest bandwidth requirement (LB) of virtual links versus the shortest distance (SD)) mapping approach to minimize the network cost for a given set of VONs, and we introduce two baseline mapping approaches, named LCLC (the largest computing resources' requirement versus the largest computing resources' provisioning (LC)) and LCSD (the largest computing resources' requirement versus shortest distance) mapping approaches, for comparison. Simulation results show that LBSD can achieve network cost near the ILP solutions in a 6-node network. Also, LBSD greatly reduces the cost, the spectrum usage, and the number of regenerators compared to LCLC and LCSD in the 6-node and NSFNET networks.

This paper addresses the problem of minimizing spectrum usage for shared-path protection with joint failure probability constraint in flexible bandwidth optical networks. To achieve this goal, we propose an integer linear programming (ILP) model for shared-path protection and a heuristic spectrum-aware shared protection (SASP) algorithm that considers joint failure probability. Simulation results show that the ILP model minimizes the total usage of frequency slots and average hops, but leads to high average joint failure probability. Moreover, the SASP algorithm achieves better trade-off between total usage of frequency slots and average joint failure probability compared to the ILP model and a conventional shared-path protection (CSPP) algorithm. As expected, in a 14-nodes network, the SASP algorithm performs better with respect to total spectrum usage and average hops, but results in much larger average joint failure probability compared to the CSPP algorithm.

Pre-configured protection cycles (p-cycles) have drawn many attentions due to the fully pre-connected cyclic structures, ring-like speed, mesh-like spare capacity efficiencies and fast switching characteristics. In this paper, we propose a novel removal of loop back (RLB) approach with standard protocol extension (the flooding based distributed cycle pre-configuration (F-DCPC)) to solve the resource inefficiency issue due to long restored paths for symmetric dynamic networks. Meanwhile, distributed cycle pre-configuration (DCPC) is a representative technique for automatic p-cycle configuration. But for the mesh network topology under asymmetric service distribution, it works especially with more network convergence time and larger amount of extra controlling overhead. So in our RLB approach, F-DCPC scheme using generalized multi-protocol label switching (GMPLS) protocol to solve the problems of configurations and reconfigurations with asymmetric digraph in high-speed optical mesh networks is presented. Additionally, to describe the basic idea of traffic splitting strategy in the unidirectional resource distribution, we present the mathematic model with a heuristic algorithm to accelerate resource configuration. We evaluate the performance of F-DCPC utilizing proposed RLB approach with various classical p-cycles enumeration algorithms and allocation strategies by experimental simulations implemented in OPNET modeler, and the simulation results show the effectiveness of the proposed scheme.