Low-Earth-orbit (LEO) satellites are one of the most promising technologies for allowing mobile communication systems to provide ubiquitous network service. However, an LEO constellation can hardly accommodate the kind of time-sensitive service widely required on various Internet-of-Things and industrial Internet-of-Things devices. The limited onboard resources and dynamic constellation topology significantly reduce the robustness of LEO networks and increase the risk of service disruptions. To realize LEO-based time-sensitive networks (TSNs), we introduce a network-layer-based latency-scheduling architecture for LEO satellite networks. The latency-scheduling architecture leverages multi-hop latency optimization instead of optimizing a single node. Based on the cyclic queuing and forwarding (CQF) mechanism usually used in terrestrial networks, we propose a time-sensitive scheduling algorithm (CoLT-TSA) that uses end-to-end collaborative latency tolerance for LEO constellations. CoLT-TSA tries to use the delay redundancy of time-sensitive traffic to improve the scheduling capabilities for TSN flows. The proposed algorithm improves network throughput without affecting the timeliness of time-sensitive services. Moreover, flow contention can be solved by CoLT-TSA based on the collaborative scheduling of satellites along the routing. We conduct simulations to evaluate the proposed algorithm, focusing on the latency, time-out ratio, packet loss and throughput. Various scenarios are simulated for CoLT-TSA and conventional techniques. These extensive simulations indicate that CoLT-TSA can decrease packet loss by more than 2.72% and prevent most of the packet loss of time-sensitive services, as well as reducing the scheduling time-out ratio by more than 4.5% compared to conventional algorithms

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.

In this paper, we propose an innovative channel-sensitive autoencoder (CSAE)-aided end-to-end deep learning (E2EDL) technique for joint geometric probabilistic shaping. The pretrained conditional generative adversarial network (CGAN) is introduced in the CSAE which performs differentiable substitution of the optical fiber channel model under variable input optical power (IOP) levels. This enables the CSAE-aided E2EDL to design optimal joint geometric probabilistic shaping schemes for optical fiber communication systems at varying IOPs. The results of the proposed CSAE-aided E2EDL technique show that for a dual-polarization 64-Gbaud signal with a transmission distance of 5 × 80 km, when the modulation format is a 64-quadrature amplitude modulation (QAM) or a 128-QAM, the maximum generalized mutual information (GMI) level learned via CSAE-aided E2EDL is 5.9826 or 6.8384 bits/symbol under varying IOPs, respectively. In addition, the pretrained CGAN, as a substitution for optical fiber transmission model, accurately characterizes the distortion of signals with different IOPs, with an average bit error ratio (BER) difference of only 1.83%, an average mean square error (MSE) of 0.0041 and an average K-L divergence of 0.0046. In summary, this paper delivers new insights into the application of E2EDL and demonstrates the feasibility of joint geometric probabilistic shaping-based E2EDL for fiber optic communication systems with varying IOPs.

With various service types including massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC), fifth generation (5G) networks require advanced resources management strategies. As a method to segment network resources logically, network slicing (NS) addresses the challenges of heterogeneity and scalability prevalent in these networks. Traditional software-defined networking (SDN) technologies, lack the flexibility needed for precise control over network resources and fine-grained packet management. This has led to significant developments in programmable switches, with programming protocol-independent packet processors (P4) emerging as a transformative programming language. P4 endows network devices with flexibility and programmability, overcoming traditional SDN limitations and enabling more dynamic, precise network slicing implementations. In our work, we leverage the capabilities of P4 to forge a groundbreaking closed-loop architecture that synergizes the programmable data plane with an intelligent control plane. We set up a token bucket-based bandwidth management and traffic isolation mechanism in the data plane, and use the generative diffusion model to generate the key configuration of the strategy in the control plane. Through comprehensive experimentation, we validate the effectiveness of our architecture, underscoring its potential as a significant advancement in 5G network traffic management.

In this work, a low-complexity data-driven characterized-long-short-term-memory (C-LSTM)-aided channel modeling technique is proposed for optical single-mode fiber (SMF) communications. To fully utilize the sequence correlation learning ability of traditional long short-term memory (LSTM) networks and solve the gradient explosion problem, the feature information is introduced into the traditional LSTM input layer to better characterize the intersymbol interference caused by dispersion in SMF modeling. The simulation results show that the proposed C-LSTM can effectively alleviate the gradient explosion problem with a stable and ultimately lower mean square error (MSE) than traditional LSTM. Compared with the split-step Fourier method (SSFM) and the conditional generative adversarial network (CGAN), the proposed C-LSTM has superior computational complexity. Moreover, due to the sequence correlation learning ability inherent to C-LSTM, coupled with the flexibility of feature information selection, the proposed C-LSTM-aided modeling technique has a higher modeling accuracy than traditional LSTM. Moreover, the C-LSTM-aided modeling technique can be effectively extended to other channel modeling applications with strong sequence correlations.

As optical fiber communication continues evolving towards higher transmission rates and greater transmission capacity, a stable single longitudinal mode (SLM) narrow linewidth fiber laser is essential for optical fiber communication. This paper proposes a method to optimize the compound ring cavity (CRC) filter based on a genetic algorithm (GA) is proposed. Through the intelligent algorithm, a fiber laser with better performance can be obtained under the requirement of single longitudinal mode filtering. By using the optimized CRC structure in the experimentally-built fiber laser, the output center wavelength of the laser is obtained to be 1549.15 nm, the optical signal-to-noise ratio (OSNR) is more than 70 dB, and the fluctuation of wavelength and output power is below 0.01 nm and 0.25 dB within 60 min, respectively. In addition, the frequency noise characteristics of the output SLM laser of the proposed fiber laser are measured using a homemade linewidth test system based on an unbalanced Michelson interferometer, and the laser linewidth at different measurement times is estimated from the frequency noise spectrum by the β-line method. The minimum linewidth of the designed laser at an integration time of 0.002 s is 2.399 kHz. By building a coherent polarization-multiplexed optical communication system of 40 km, the designed laser achieves the transmission at a rate of 40 GBaud 16-quadrature amplitude modulation (16QAM), demonstrating that the designed laser can realize the requirement of communication transmission.