Lei Guo

Free-space optical (FSO) coherent links are promising for high-capacity space-ground networks, yet their performance is fundamentally limited by turbulence-induced intensity scintillation and spatial-mode distortion. Existing coherent FSO studies mainly rely on continuous-wave sources and complicated adaptive optics (AO) compensation, which incur high implementation complexity. In contrast, recent ultrashort pulse (USP) coherent systems have shown intrinsic temporal- domain scintillation immunity, leaving spatial-domain aberration unaddressed. Furthermore, traditional spectral phase encoding used in coherent USP systems is prone to local-optimum trapping and faces a high risk of loss-of-lock under turbulence. In this work, we design a dual-stage perturb-rollback algorithm to enhance comb-phase (CP) locking robustness, and further propose a spatio-temporal mutualism (STM) mechanism that reutilizes the temporal features of CP as AO compensation inputs. The spatial correction, in turn, enhances temporal matching precision, forming a bidirectional cooperative closed-loop correction. Simulations reveal that USP transmission reduces scintillation by up to 9.36 dB, while STM yields an additional 15.14 dB bit error ratio (BER) improvement in strong turbulence regions for uplinks. This work provides a high-performance and scalable solution to constructing next-generation space-ground FSO feeder systems with superior sensitivity and reliability.

High-order modulation format optical signals are widely deployed in modern optical networks but remain insufficiently protected at the physical layer, leading to significant security vulnerabilities. This work proposes an all-optical secure transmission scheme for 16QAM signals based on phase coding and dispersive stretching. The scheme employs symmetric cascaded in-phase and quadrature Mach–Zehnder modulators (IQMZMs) to realize phase encoding and decoding, providing a simple structure with good suitability for photonic integration. The universality of constant-envelope key signals within the proposed architecture is investigated, providing references for key-format and device selection in practical applications and important guidance for future research on all-optical digital encryption. To the best of our knowledge, this is the first demonstration of all-optical digital encryption and decryption of 16QAM signals using high-order modulated key signals. Compared with existing schemes of the same category, the key space increases by a factor of $2^{n}$, where $n$ denotes the number of symbols. Furthermore, the key signal undergoes dynamic temporal broadening through dispersive processing, enabling steganographic transmission. Simulation results at a bit rate of 100 Gb/s demonstrate significant security enhancement with high stability and reliability. Overall, the proposed scheme provides an effective approach to enhancing physical-layer security in high-order modulation optical transmission systems.

This paper investigates energy-efficient integrated sensing and communication (ISAC) aided by multiple active reconfigurable intelligent surfaces (active-RISs). We develop a multi-active-RIS-enabled joint beamforming framework and formulate a system-level power optimization problem that explicitly accounts for the active-RIS amplification power in ISAC. To support real-time beamforming decisions, we propose a priority-aware deep deterministic policy gradient (DDPG) algorithm with a hierarchical reward design that provides clear exploration guidance for high-dimensional joint beamforming. Simulation results show that the proposed framework improves overall power efficiency compared with passive-RIS and conventional DDPG baselines under different ISAC requirements and network scales.

Sensor-cloud technology is a key enabler for transportation of smart cities, providing large-scale traffic data for analysis in increasingly complex traffic systems. However, spatio-temporal traffic data poses significant computational challenges, making it critical to fully exploit such data and explore cloud–edge collaborative intelligence in data-driven scenarios. To address this, we propose a spatio-temporal large-scale and lightweight model collaboration framework (ST-LLC), which distributes computational workloads across the cloud and edge to achieve accurate traffic prediction. In this framework, a lightweight graph network deployed at the edge captures short-term dynamics, while dual auto-encoders in the cloud extract long-term traffic priors. These heterogeneous features are then integrated through a cross-feed attention (CFA) mechanism, and a large language model (LLM) is designed to transfer domain knowledge and enhance adaptability. We evaluate the proposed framework on three real-world public datasets. Extensive experimental results demonstrate that ST-LLC effectively integrates cloud–edge traffic sequence features and achieves superior prediction accuracy compared with state-of-the-art methods. Finally, we discuss practical challenges such as communication latency and data security, and outline future directions.

Enabled by 6G wireless technologies, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) create a new dimension for optimizing performance in Uncrewed Aerial Vehicle (UAV) communications through full-space signal coverage. However, existing research on STAR-RIS-assisted UAV secure communications still faces critical challenges, including the reliance on ideal Channel State Information (CSI) assumptions, amplitude optimization complexity under mode switching protocols, and limited scalability to meet multi-user communication demands. To address these challenges, we propose a robust and secure STAR-RIS-assisted UAV communication approach for a multi-user and multi-eavesdropper scenario. By jointly optimizing user scheduling, transmitting and reflecting coefficients of STAR-RIS, transmit power and flight trajectory of UAV, we aim to maximize the average worst-case achievable secrecy rate. To tackle the non-convexity and coupled decision variables of the formulated problem, we propose an alternating optimization framework, with a Lagrange multiplier method for power allocation, a deterministic model reformulated via S-procedure for CSI uncertainty quantification and robust handling, and a penalty-based double-loop iterative algorithm forcing the phase-shift matrix toward a rank-one solution. Finally, theoretical analysis and simulation results validate the superior secrecy performance of the proposed algorithm over other representative algorithms.

This paper proposes a physics-informed neural network (PINN) scheme for localizing bending eavesdropping in coherent optical communication systems. First, we establish a signal transmission model under bending eavesdropping based on the Manakov equations to analyze its impact on physical information such as linear birefringence and nonlinearity. Subsequently, a PINN is developed by incorporating physical information such as linear birefringence and nonlinear effects into a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) architecture. To validate the effectiveness of the scheme, we construct an experimental platform for bending eavesdropping localization in an 80 km, 168 Gbps quadrature phase shift keying (QPSK) coherent optical communication system. Polarization data during eavesdropping are collected from nine positions with bending radii of 10.8 mm and 15 mm. The performance of three models, including CNN, CNN-BiLSTM and PINN is evaluated for bending eavesdropping localization under both bending radii. Experimental results demonstrate that the PINN achieves localization accuracies of 100% and 99.8% under bending radii of 10.8 mm and 15 mm, respectively. This work offers a novel theoretical framework and methodological approach for localizing bending eavesdropping in optical fiber communication systems.

To address the increasing computation-intensive tasks, we consider a multi-unmanned aerial vehicle (UAV)-enabled computing offloading system and aim to explore its maximum service capacity. The existing solutions aim to optimize user service experience in terms of delay or energy efficiency, but this fundamentally assumes that all tasks are completable. We highlight that all computational tasks in practical application environments have specific delay constraints. As such, it is unrealistic to consider that all tasks can always be completed on schedule when resources fall short of demand. To this end, we propose a service-maximized bilevel (SMax-bilevel) optimization scheme, and formulate a task completion rate maximization problem. Due to its non-convex complexity, we decompose the original problem into a tractable bilevel structure. At the upper level, an encoding mechanism-enhanced adaptive differential evolution algorithm (ADEEM) is developed to determine UAV deployment locations that maximize service coverage. At the lower level, a theorem is presented to guide user selection, task offloading, and resource allocation, aiming to maximize the number of completed tasks within their delay constraints. The two levels iteratively interact to produce an optimal solution. Simulation results demonstrate that the proposed SMax-bilevel scheme significantly enhances system service capacity while reducing algorithmic complexity, outperforming existing benchmark solutions.

Conventional emergency communication networks are plagued by critical limitations, including protracted recovery cycles and coverage voids, which are further exacerbated by the escalating frequency of natural disasters and public safety incidents. To address these challenges, we propose an unmanned aerial vehicle relay-assisted hybrid free-space optical (FSO)/radio-frequency (RF) system engineered for rapid deployment and robust performance in emergency scenarios. In this architecture, FSO links form the high-capacity aerial backbone, while RF channels deliver last-mile connectivity to end users. Furthermore, a unified end-to-end channel model is developed to analytically integrate the effects of fiber attenuation, atmospheric turbulence, and RF path loss, establishing closed-form relationships between received signal-to-noise ratio and bit error rate. Field experiments validate zero-bit-error transmission on the 100 m FSO relay link, a sustained downlink rate of 127.48 Mbps, and stable coverage, which collectively underscore the system's effectiveness for resilient, high-throughput emergency communications.