Low Earth orbit (LEO) satellite networks, as a fundamental component of 6G networks, are designed to provide full coverage, low latency, and high quality of service (QoS) for satellite-terrestrial integrated networks (STIN). Topology representations and routing computation in dynamic LEO satellite networks have become key research focuses. However, balancing network dynamics with traffic load remains challenging due to inaccurate topology representation and inefficient routing in existing studies. To address this, we propose a timeslot-adaptive and traffic load-aware routing computation (TA-TLARC) scheme for two-layer LEO satellite networks. The two-layer LEO satellite networks consist of communication layer satellites (CLS) and relay and sensing layer satellites (RSLS). TA-TLARC adaptively adjusts timeslots based on traffic variations and utilizes distributed adjacency matrices for routing computation. Simulation results show that TA-TLARC achieves better performance than existing routing schemes in key QoS metrics such as routing success rate, delay, throughput, and packet loss rate. Although routing hops and power consumption increase within acceptable limits, the routing success rate of TA-TLARC remains 99.6% to 100%. The QoS performance, including delay, throughput, and packet loss rate, is improved by 10% to 40% compared to those of the comparative schemes under different traffic scenarios. The robustness of TA-TLARC is further analyzed and demonstrated to be acceptable under various failure conditions. The results demonstrate that the proposed TA-TLARC effectively addresses routing computation challenges and significantly improves QoS performance in two-layer LEO satellite networks.

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.

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, there has been significant attention in UAV Named Data Networking (UNDN) from both industry and academia. This network paradigm adopts a “request-reply” communication model that allows UAVs to access desired content without the need for specific information regarding the geographical location or IP address of the content producer. This IP-independent design is well-suited for dynamic UAV swarms, but it presents challenges in establishing matching policies between content consumers and producers. This is because that during the distributed decision-making process in content sharing, consumers cannot possess private information regarding producers, and producers may lack the motivation to distribute content. As a result, a revelation and incentive mechanism is needed to be formulated in the system. In this paper, a resource-efficient content-sharing mechanism is proposed to address the aforementioned challenges. First, we propose a contract-based mechanism to incentivize content producers to share content and reveal their private information at the same time. The problem of obtaining the optimal contract is discussed in both cases of information asymmetry and complete information. Then, the Gale-Shapley (GS) algorithm is adopted to make a stable many-to-one matching between content consumers and content producers. The simulation results verify the feasibility, effectiveness and energy efficiency of the proposed mechanism.

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.

To overcome the challenges of complex time-varying satellite channels and severe inter-user interference in non-orthogonal multiple access (NOMA), rational power allocation and accurate multi-user joint detection methods are essential. In this paper, a sparrow search algorithm-based resource allocation and deep learning-based joint detection scheme (SSA-DeepJD) in the satellite-terrestrial NOMA system is proposed. First, the NOMA-orthogonal frequency division multiplexing (OFDM) system model is constructed. Next, a convolutional neural network-based image super-resolution recovery network is proposed for offline training and online channel estimation, which incorporates densely connected convolutional layers and residual learning to model for handling complex non-linear channel fitting. Then, a multi-user signal detection based on an iterative deep neural network is proposed, which is iteratively retrained to improve the detection accuracy. Finally, due to the significant impact of the power allocation on the system error performance, the optimal power allocation is found within the power allocation factor threshold based on SSA. Simulation results show that the proposed SSA-DeepJD algorithm is well-suited for multi-user superposed NOMA systems and complex non-linear channel environments. Compared to the baseline algorithms, the SSA-DeepJD algorithm degrades the Bit Error Rate (BER) by 21.5 dB and 11.9 dB in the 2-user and 3-user NOMA systems, respectively.

Network slicing (NS) is a key technology to cost-effectively meet diverse service level agreement (SLA) demands of the Internet of Everything communication. Thanks to high-fidelity network modeling capabilities and flexible feedback optimization techniques, digital twins (DTs) and reinforcement learning (RL) have been applied to dynamic NS management. However, most existing DTs lack the ability of predictive uncertainty evaluations, and tend to be overconfident on the unknown network environment. For classical RL, it is exceedingly intractable to maintain high-stable NS performances in dynamic networks. To address those problems, we propose a DT-based low-risk NS (DT-LNS) framework and method using the safe RL. In the safe RL, a DT using deep neural networks with the data-model uncertainty analysis is adopted to predict NS performances and provide predictive uncertainties. Further, the RL is used to select low-risk NS configuration actions by preverifying the SLA violation risk of candidate actions from the RL and the reference action subspace via DTs. The proposed DT-LNS method can keep the high-SLA satisfaction rate (SSR), reduce the performance jitters, and improve the convergence speed. Compared with the six classic NS configuration methods, including round robin, deep Q network, advantage actor-critic, deep deterministic policy gradient, and advanced RL, assisted with the DT-based model pretraining and the state prediction, the average percentage gain of the proposed method is 7.84%, 93.58%, 65.63%, 84.20%, and 90.27%, regarding the performances of the average SSR, SSR jitter, delay jitter, data rate jitter, and the convergence speed, respectively.

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.

Elastic cognitive optical network(ECON) embeds artificial intelligence technology into network management to enable resource self-optimization ability, which has aroused the wide interest of researchers. However, realizing precise traffic prediction (TP) in optical networks has been a challenging problem due to channels’ complex variable bandwidth conditions. We provide an ECON architecture and propose a graph-convolutional-network-transformer (GCN-transformer) TP algorithm. The proposed algorithm has been evaluated and compared with the traditional schemes. We build a testbed for the proposed algorithm by OMNET++. The results show a prediction accuracy of 99.74%, which reduces the inaccuracy by 2.03% compared with other typical algorithms.

Cyclic queuing and forwarding (CQF), proposed in IEEE 802.1 Qch, is a practical mechanism for guaranteeing deterministic transmission for time-sensitive networks (TSNs). However, only the queue model and the workflow for terrestrial networks are defined in IEEE 802.1 Qch. To make TSNs practical for future 6G applications, a general scheduling model that maps time-sensitive flows (TSFs) to the underlying resources of low-Earth-orbit satellite-terrestrial integration networks (LEOSTINs) is urgently needed. The networking conditions of STINs are quite different from those of terrestrial networks due to the large-scale spatial coverage of STINs. Hence, in order to determine the feasibility of deploying TSNs in LEO-STINs, we evaluate the CQF performance for LEO-STINs in this paper. Then, a software-defined-network-based LEO-STIN architecture for the entire lifecycle of TSFs is designed. To address the drawbacks of the LEO-STIN scenario, we propose a cyclic priority and forwarding (CPF) mechanism to improve the performance of time-sensitive services. CPF removes the bandwidth limitation of CQF for TSFs, which makes TSNs practical for LEO-STINs. We perform a simulation of a Walker constellation to test the proposed algorithm and existing TSN techniques using OMNET ++. The results show that the proposed algorithm reduces the packet loss ratio by an order of magnitude and the service time-out ratio by 70% compared to existing mechanisms.

With the rapid development of mobile Internet technology and artificial intelligence technology, the digital publishing industry is in urgent need of using intelligent technology to change the current way of content production and service. Most of the e-book resources owned by publishing enterprises are in PDF format, which is not suitable for reading on mobile devices, and it is not convenient to directly extract key information and construct knowledge graph. With this in mind, this article designs a PDF automatic indexing scheme that can identify all the element information in PDF and output structured data automatically and then extract all the key information in it to generate a keyword library with tag weights. The scheme mainly involves two key technical points: parsing PDF based on text features and grammar rules and extracting keywords based on tag weights. The former visualizes the text block in PDF into a rectangular area, divides the elements by clustering algorithm, and, finally, outputs structured data containing all the information. The latter combines the tags and their weights in the structured data and extracts the keywords in it by the inter-word relation algorithm. The structured data and keywords database produced by this scheme can be used to produce intelligent e-book and build knowledge graph, thus helping publishing enterprises to transform from a content service provider to an intelligent knowledge service provider. This transformation can deeply excavate the core value of the content held by the publishing industry and promote the digitization and intelligentization process of the whole industry.

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%.

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.

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.