In this paper, we propose the energy-tier template insertion shaping (ETTIS) probabilistic shaping algorithm, which performs shaping and deshaping through preset templates and simple bit insertion/deletion operations, thereby significantly reducing the algorithmic complexity. The ETTIS algorithm effectively mitigates the Hamming distance shrinkage problem commonly observed in traditional probabilistic shaping algorithms and exhibits excellent compatibility, allowing seamless integration with standard forward error correction coding and interleaving algorithms. Moreover, the ETTIS framework utilizes predefined symbol components in the shaping templates to implicitly introduce pilot symbols, enabling real-time estimation of channel gain and noise variance without additional bandwidth overhead.. Experimental and simulation results show that, at the same bit rate, the proposed scheme achieves 0.4–0.6 dB performance gains under 16-quadrature amplitude modulation (QAM) and 64-QAM modulation formats, respectively. Compared with other state-ofthe- art algorithms, ETTIS attains comparable performance while maintaining significantly lower complexity. During decoding, the absolute deviation of the estimated noise bit error rate remains below 0.75%. These features make ETTIS a promising solution for high-throughput and cost-sensitive optical interconnect systems.

Low Earth Orbit (LEO) satellite networks, characterized by their low latency and extensive coverage, play a pivotal role in the development of future 6 G communication systems. However, due to the dynamic nature of network topology and the challenges associated with real-time perception of link states, the implementation of deterministic routing in LEO satellite networks presents significant difficulties. To address these difficulties, we propose a network calculus-based deterministic routing (NCDR) algorithm. Specifically, we develop a deterministic resource characterization model and design a traffic pre-transmission mechanism that utilizes network calculus theory to calculate the traffic backlog. Additionally, we propose an interruption feedback mechanism to deal with link interruptions. Finally, the NCDR algorithm makes routing decisions aimed at minimizing end-to-end transmission delay and balancing network load. Simulation results demonstrate that the NCDR algorithm significantly outperforms existing algorithms in terms of delay, throughput, and packet loss rate.

In recent years, Satellite-Terrestrial Networks (STNs) have garnered significant attention for extending network coverage to areas beyond the reach of traditional terrestrial networks. With the rapid expansion of STN applications, integrating Service Function Chaining (SFC) technology has become crucial for delivering differentiated services. However, the dynamic and complex structure of STNs presents significant challenges for SFC deployment. To address these, we propose a multimodal reinforcement learning algorithm that uses separate neural networks to process diverse STN data, enabling more effective SFC deployment decisions. Our approach includes a Graph Transformer for processing network states represented as graphs, capturing the relationships between nodes, links, and resource distributions. Additionally, two MLPs are used to handle QoS requests and global network information. Built on these components, the Proximal Policy Optimization (PPO)-based algorithm demonstrates superior performance over conventional AI methods, effectively learning optimal SFC deployment strategies.

With the advantages of low delay, wide coverage, and high throughput, low-Earth-orbit (LEO) satellite networks hold significant potential for establishing globally interconnected networks. However, the large spatial scale of satellite networks leads to lagging link state perception. Moreover, the perception overhead significantly increases with the growing number of satellites. These characteristics have brought great challenges to the routing design of large-scale LEO satellite networks. To address the above challenges, we propose a multi-region joint routing (MRJR) algorithm based on network calculus (NC) theory to achieve low-delay transmission without relying on traditional state perception. Firstly, a multi-region NC model is introduced to efficiently manage satellite networks and decouple the traffic transmission process. Then, we design the MRJR algorithm, which accurately derives link traffic backlogs to acquire real-time link congestion states, thereby calculating the lowest delay routing path. Additionally, an NC timeslot correction mechanism is proposed to ensure the accuracy of traffic backlog calculations. The simulation results demonstrate that the MRJR algorithm outperforms existing routing algorithms in terms of average delay, throughput, and packet loss.

This paper proposes a probabilistic amplitude shaping (PAS) scheme with systematic cascaded staircase and Hamming codes (CSHC). The integration of low-complexity concatenated codes within PAS and the use of the SIHO decoder are enabled by applying the inter-bit independence assumption. This ensures compatibility with high throughput transmission. In addition, the use of a reduced test pattern set in the SIHO decoder reduces the test pattern cardinality by 34.37%. The proposed scheme is experimentally demonstrated by self-homodyne coherent transmission over a 22.5 km 7-core fiber, with 21 wavelength division multiplexing (WDM) channels in the C-band is demonstrated. The total rate of the WDM-space division multiplexing (SDM) system is 21.77 Tbit/s. The experimental results show that the proposed PAS scheme with CSHC provides a gain of more than 8 dB in the back-to-back (BtB) scenario at the pre-FEC BER of 2.88E-2. Moreover, an average gain of 7.26 dB is achieved at the same threshold in WDM-SDM transmission.

In recent years, the integration of mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) has gained significant attention for its potential to reduce energy consumption and offloading latency in future wireless networks. While NOMA can enhance system capacity, accommodating multiple users on the same channel may lead to decoding inaccuracies and reduced offloading accuracy. To tackle these problems, this paper proposes a multi-user offloading model that combines NOMA and orthogonal multiple access (NOMA-OMA) to optimize resource allocation. Users are divided into groups based on their geographical locations, with each group further divided into subgroups. OMA is used within each subgroup, while NOMA is employed between different subgroups to achieve joint multi-user offloading. We divide the optimization problem into two sub-problems, namely power and time allocation between different subgroups and delay allocation within the same subgroup. Closed-form expressions for the two sub-problems are derived. The proposed method achieves optimal system energy consumption while increasing the number of users and maintaining low system complexity. Simulation results demonstrate the effectiveness of the proposed method.

With the ongoing advancement of information and communication technologies, the communication technologies for UAV swarm networks have undergone rapid development, especially in the context of large-scale UAV network deployments. In recent years, UAVs have found wide-ranging applications in both military and civilian domains. However, the inherent complexity and high dynamic nature of UAV swarm activities present substantial challenges to traditional routing algorithms, prompting the need for the design and implementation of efficient and sustainable routing solutions. To address these challenges, this paper introduces a UAV swarm routing algorithm based on evolutionary game theory, with a particular focus on energy efficiency and resource optimization. We leverage evolutionary game theory to enhance cooperation among nodes and adopt a strategy update rule that imitates the best-performing agents. In the proposed algorithm, nodes engage in continuous packet forwarding and participate in game interactions with neighboring nodes, adjusting their strategies based on accumulated gains. This strategy not only significantly enhances network lifetime and improves the packet delivery rate but also optimizes energy consumption and resource utilization, aligning with sustainable computing principles. To validate the effectiveness of the proposed routing method, we conduct extensive simulation experiments within a designed and implemented system model under different environmental contexts. The analysis confirmed the accuracy and effectiveness of the proposed routing method, highlighting its exceptional performance in terms of the network survival time, the number of successfully transmitted packets, and the adaptability in dynamic scenarios.

The integration of Software-Defined Networking (SDN) and Artificial Intelligence (AI) presents promising opportunities for managing and optimizing LEO satellite network routing. However, as the scale and coverage of satellite networks continue to expand, challenges are posed to both centralized and distributed architectures in terms of managing network information and coping with routing complexity. To overcome these challenges, leveraging distributed SDN technology, a stigmergy multi-agent hierarchical deep reinforcement learning routing algorithm is proposed in multi-domain collaborative satellite networks. A pheromone-based mechanism is incorporated to facilitate collaboration during independent training, and hierarchical control is employed to decouple the complexity of cross-domain routing decisions. Simulation results demonstrate that our proposed algorithm exhibits good scalability and performance in large-scale satellite networks.

Due to the scarcity of spectrum resources in Non-orthogonal Multiple Access (NOMA) systems and insufficient satellite-ground integration in satellite Internet of Things (IoT), this paper investigates its issue in spectrum resource management. We propose a resource allocation method based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for NOMA enabled satellite IoT. We formulate the spectrum allocation problem of the satellite-ground integrated network as a distributed optimization problem. Then we decouple the problem into two sub-problems. Firstly, a user grouping method based on matching coefficients is defined, and a Linear Programming (LP) method is utilized for obtaining solution. Secondly, the power allocation problem is transformed into a multi-agent problem, where MADDPG is employed to allocate the power. Through this approach, the system is capable of real-time user association and spectrum resource allocation optimization, achieving optimal user grouping while maximizing system transmission rate. Based on the simulation results, the MADDPG-based method demonstrates fast convergence within 100 training iterations. The proposed MADDPG-based resource management method also achieves increased system transmission rate with more effective matching outcomes over Deep Deterministic Policy Gradient (DDPG), Orthogonal Multiple Access (OMA), and random allocation baselines.

Mobile edge computing (MEC) and millimeter-wave (mmWave) communication are promising techniques for future cellular networks. MEC enables latency-critical tasks offloading at the network edge, while mmWave provides an abundant spectrum for gigabit-per-second data transmission. Dense deployment of remote radio units (RRUs) is necessary due to high mmWave signal path loss, and hence limiting the deployment cost becomes a prime network design factor. Our work considers that RRUs are deployed to provide mmWave access and to offload computation requests to edge servers (ESs) via fronthaul links. We propose an edge network (EN) deployment problem by jointly optimizing the mmWave access and fronthaul networks. Converged fiber and in-band mmWave techniques are utilized for flexible fronthaul links deployment and cost reduction. The deployed EN is expected to fulfill coverage, reliability and latency requirements of ultra-reliable low-latency (uRLLC) services. We formulate the optimization problem as an integer linear program (ILP) and propose a multi-objective evolutionary algorithm to solve the problem. The numerical results demonstrate that our proposed algorithm can achieve close-to-optimal solutions compared with the ILP formulation. We also comparatively evaluate the deployment costs under different EN settings and show that our algorithm provides up to 20.3% cost savings compared to non-converged solutions.

We demonstrate a probabilistic shaping (PS) four-dimensional (4D) modulation in self-homodyne coherent transmission system. The 4D modulation is based on inter-symbol amplitude translation (AT) to perform set partitioning. The distribution of constellation points after AT is optimized by de-DC. The parity bits produced by the AT are transmitted with the pilot tone by remapping. In addition, a soft decision for this 4D-PS signal is proposed. An experiment of self-homodyne coherent ultra-high order 4D signal transmission based on two cores of a 7-core fiber is demonstrated with a spectral efficiency of 16.37 bit/s/Hz. The 4D signals with soft decision can provide up to 0.78 bit/symbol and 1.85 bit/symbol gain compared to normal polarization division multiplexing signals and hard-decision 4D signals.

With the continuous growth of network traffic, optical wireless communication (OWC) technology based on orbital angular momentum (OAM) can meet the needs of large-capacity modern communication and is an effective way to substantially increase wireless information transmission capacity. However, the OAM beam distortion caused by atmospheric turbulence in the actual link and the limitations caused by phase singularities are major challenges faced by the OAM-OWC system. In this paper, we address these issues and propose a low-complexity nonprobe adaptive optics (AO) compensation technique based on Y-net which can achieve high-accuracy distortion compensation and OAM mode demodulation simultaneously. In this approach, only one CCD is required for the Y-net-aided AO (Y-net AO) technique without a traditional probe path while satisfyingly balancing OAM-based optical transmission system complexity and transmission performance. Extensive simulations show that the proposed Y-net-aided AO technique can indeed decontaminate distorted OAM beams in both single- and multiplexed-channel OAM links. Furthermore, a noise model is established to analyze the robustness of the Y-net AO technique. The Y-net AO technique exhibits less system complexity and better anti-noise performance than the ordinary convolutional neural network (CNN)-based AO scheme. In summary, Y-net AO technology for OAM-OWC systems with high correction accuracy and low structural complexity is considered for effectively improving the transmission performance in this paper. Key AO technologies for the high-quality and innovative development of large-capacity communications are also expected to be formed.

The fast and intelligent reconfigurability of recon-figurable add-drop multiplexers (ROADMs) in metropolitan area networks (MANs) has gained much attention recently due to technical advancements in artificial intelligence and fast optical switching. However, it is challenging to realize submillisecond-level automatic reconfiguration for MANs under fast time-varying traffic pattern, because of the latency of the wavelength scheduling and traffic cognition lag. On the one hand, the latency for wavelength scheduling takes tens of millisecond for the most-used ROADMs; On the other hand, the lag involved in the traffic cognition weakens the advantage of fast wavelength scheduling. To view of these problems, this article proposes a fast-reconfigurable MAN architecture with closed control plane targeted to the submillisecond-level reconfiguration. The proposed architecture reduces the reconfigurable latency for both the data plane and the control plane. Furthermore, we design a latency estimator based on graph neural network (GNN) for congestion awareness, and develop a fast-reconfigurable ROADM based on semiconductor optical amplifier. We evaluate the estimator and proposed architecture under various scenarios. The results show that the GNN-based estimator can achieve high precision in the latency estimation.

The satellite-assisted Internet of Things (IoT) communication is considered a key component of the 6G network, and low Earth orbit (LEO) satellite is the leading choice of IoT-related satellites due to its minimum delay. Hybrid requests, including immediate reservation (IR) and advanced reservation (AR) services in LEO satellite netoworks cause the occurrence of resource fragments (RFrags), which adversely affect the network performance. To alleviate the degradation of network performance caused by resource fragmentation, a routing, wavelength, and time-slots assignment algorithm, which is named elastic resource allocation algorithm based on the dispersion degree (ERA-DD), is proposed in this article. Possible resource fragmentation types are analyzed and a fragmentation description named dispersion degree (DD) is designed. In the DD, the number of free resource blocks is accurately described by the state jumps (SJs) of adjacent resource slots, and the numerical relationship between SJs and free resource blocks is proposed and proved. Besides, restrictions on the selection priority of candidate schemes in the ERA-DD algorithm are analyzed. Finally, the traffic blocking rate, the wavelength utilization, the average communication delay, and the average initial delay are evaluated by simulation. The results demonstrate that RFrags are more fully utilized compared with the maximum total link spectrum consecutiveness (MTLSC) algorithm. The traffic blocking rate can be reduced by 32.5% and wavelength utilization can be increased by 1.6%.

In this article, a distributed optical fiber sensing system for large infrastructure temperature monitoring is proposed. To meet the requirements of monitoring networks in terms of measurement accuracy, spatial resolution, and real-time or quasireal-time performance, a quaternion wavelet transform (QWT) image denoising algorithm is proposed to address the original edge node data for the structural monitoring networks of large infrastructures. A distributed Brillouin optical time-domain analysis (BOTDA) sensing system with a 40-km sensing fiber is established. The raw Brillouin gain spectrum (BGS) image is decomposed into one magnitude image and three phase images by QWT. The phase images of the OWT are distributed randomly and disorderly with respect to the noise, while the magnitude image of the quaternion wavelet is greatly affected by the noise. The useful message energy of the magnitude image is concentrated on a small number of coefficients with large amplitude, while the noise mainly corresponds to the coefficients with smaller amplitude. Then, the Bayes shrink threshold method is introduced to filter out noise in the magnitude image. The results indicate that the signal-to-noise ratio (SNR) and the frequency uncertainty have been improved significantly. The accuracy of the retrieved Brillouin frequency shift from denoised BGS images reaches 0.2 MHz, which corresponds to a temperature error of ±0.1 °C. Less than 4 s are required to process a BGS image with 50$\times $40 000 pixels by the QWT denoising technique. The uploaded data obtained from 40 M bytes of raw data are reduced to 0.08 M bytes for each measurement. We hope that with technological progress and algorithm optimization, the distributed optical fiber sensing system based on the QWT image denoising algorithm will have an important role in the real-time application of large-scale infrastructure structural health monitoring for the Internet of Things.

Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater-turbulence-induced scintillation will destroy the orthogonality of OAM modes, hence degrading the performance of the system. In this article, a random-amplitude-mask-based adaptive optics (AOs) technique is proposed for the sake of mitigating the turbulence effects in the OAM-based underwater wireless optical communication (UWOC) system. Combined with phase retrieval algorithms, the magnitudes of linear measurements obtained from the distorted OAM beams modulated with a series of random amplitude masks and focused by a lens are employed for the phase estimation. Furthermore, we present a comprehensive performance comparison against state-of-the-art phaseless wave-front sensing techniques. Moreover, the mixture exponential-generalized gamma (EGG) distribution is applied for characterizing the probability density function (PDF) of reference-channel irradiance of OAM beams coupled into a single-mode fiber (SMF). In the end, the performance metrics, such as the outage probability, the average bit-error-rate (BER), and the ergodic capacity are analyzed with the aid of PDF for both single-input-single-output (SISO) and multiinput-multioutput (MIMO) systems. In a nutshell, this article provides new insights for the applications of AO in the OAM-based UWOC system, which can serve as a candidate for supporting IoUT devices.

Adaptive optics (AO) can efficiently compensate for turbulence-induced distortion in orbital angular momentum (OAM)-based optical wireless communication (OWC) systems. In this paper, we design a modified phase diversity algorithm (MPDA)-based wavefront sensor to enhance the reconstruction accuracy of distorted OAM wavefront information. Aiming to further strike a compelling trade-off between AO system complexity and compensation accuracy, we first construct a novel AO system that applies a quickly and electronically controlled focus-tunable lens (FTL). It decontaminates distorted OAM signaling beams while having a low systemic complexity and superior convergence performance. Furthermore, we propose the 3-modified phase diversity algorithm (3-MPDA) AO scheme relying upon a Fourier intensity and two defocused intensities as the prior information, which beneficially balances the compensation effect and the number of defocused intensities and exhibits good noise robustness against charge-coupled device (CCD) detectors. In summary, this paper provides new insight for designing AO schemes with high compensation performance in communication links.

Traffic flows are often processed by a chain of Service Functions (SFs) (known as Service Function Chaining (SFC)) to satisfy service requirements. The deployed path for a SFC is called Service Function Path (SFP). SFs can be virtualized and migrated to datacenters, thanks to the evolution of Software Defined Network (SDN) and Network Function Virtualization (NFV). In such a scenario, provisioning of paths (i.e., SFPs) between virtualized network functions is an important problem. SFP provisioning becomes more complex in a multi-domain network topology. `Topology aggregation' helps to create a single-domain view of such a network by abstracting multi-domain networks. However, traditional `topology aggregation' methods are unable to abstract SF resources properly, which is required for SFP provisioning. In this paper, we propose an SFC-Oriented Topology Aggregation (SOTA) method to enable abstraction for SFs in multi-domain optical networks. This study explores the node and the link aggregation degree to evaluate information compression during the `Topology aggregation' process. Additionally, we also propose a new data structure named wheel matrix and related operations to store routing information in the aggregated topology. Based on SOTA, we propose two cross-domain SFP provisioning algorithms named Ordered Anchor Selection (OAS) and ${k}$ -paths OAS (K-OAS), and a benchmark named Global OAS (GOAS). Simulation results show that SOTA could aggregate large-scale multi-domain optical networks into a small network that contains only 6.9% of the nodes and 10.1% of the links. Both OAS and K-OAS can calculate SFPs efficiently and reduce blocking probability up to 52.10% compared to the benchmark.