Both the security and the freshness of data are crucial in the Industrial Internet of Things (IIoT). Uncrewed aerial vehicles (UAVs) have been widely used for sensing data collection in IIoT due to their high flexibility, low cost, and easy accessibility. However, the issue of trajectory planning in multi-UAV-assisted IIoT has not been adequately studied with regard to the security threat mitigation and the freshness of data. Therefore, we investigate the trajectory planning problem for secure data collection in a multi-UAV-assisted IIoT network, using UAVs to collect data timely while detecting and mitigating the effects of ground malicious users. In the envisioned network, some UAVs act as airborne base stations to collect data, and the others act as jammers to emit artificial noises to interfere with the monitoring. In order to minimize the average information age (AoI) of collected data, we propose a novel confidence-weighted twin delayed deep deterministic policy gradient (CTD3) algorithm for multiple UAV trajectory optimization. In this algorithm, UAVs are modeled as an agent (i.e., a cognitive entity) that is capable of planning their flight trajectory based on the target of minimizing the AoI of collected data and the perceived information of the surrounding environment. In addition, we innovatively introduced random network distillation (RND) technology into reinforcement learning and used its weighted the loss function of policy network to guide the UAV to better utilize the empirical data from its interactions with the environment to continuously learn and optimize its behavioral policies, thereby improving the efficiency of data utilization. Our simulation results demonstrate the effectiveness and superiority of the CTD3 algorithm in terms of both the cumulative discount reward and the average AoI.

Digital Subcarrier Multiplexing (DSM) has emerged as a preferred solution over its Single-Carrier (SC) counterpart for high-speed long-haul Standard Single Mode Fiber (SSMF) transmission, owing to its flexible spectral allocation and adaptive transmission capabilities. However, fiber nonlinearity remains a fundamental limitation on the transmission bandwidth-distance product, while Digital Backpropagation (DBP) serves as a representative Nonlinearity Compensation (NLC) method. Traditional symmetric DBP assumes a uniform nonlinearity accumulation over the SSMF link, which deviates from the inherently asymmetric power profile in practice. Specifically, we propose an asymmetric DSM-DBP scheme based on a nonlinear accumulation asymmetry metric ΔE(ρ), which enables the efficient determination of the optimal dispersion splitting ratio ρ ∈ [0, 1] without exhaustive optimization. Consequently, we can efficiently determine the position of nonlinear compensation operators within each DBP step for high-speed long-haul DSM transmission systems. When the single-wavelength, four-subcarrier, 800 Gb/s Probabilistic Shaping Dual-Polarization 64-Quadrature-Amplitude-Modulation (PS-DP-64QAM) signals are transmitted, the asymmetric DSM-DBP increases the optimal launch power to 5dBm and achieves a Signal-To-Noise Ratio (SNR) improvement of 0.2 dB over symmetric DSM-DBP after the 1600 km SSMF transmission. Under a Normalized Generalized Mutual Information (NGMI) threshold of 0.89, the SSMF reach can be extended to 2400 km, representing a reach extension of approximately 160km compared to traditional symmetric DSM-DBP. Meanwhile, the robustness of the proposed asymmetric DSM-DBP is numerically verified for various modulation formats, variable baud rates, and different subcarrier configurations. These results highlight the scalability and practical relevance of the proposed asymmetric DSM-DBP for next-generation high-capacity optical networks.

The vulnerability of intensity modulation direct detection (IM-DD) systems to physical-layer eavesdropping poses a significant threat to data center interconnects (DCI). In this Letter, we propose a dual-dynamic strategy for physical-layer encryption to enhance the security of IM-DD systems. The scheme synergistically combines dynamic key update and dynamic rule-based pulse-amplitude modulation (DR-PAM) symbol scrambling to achieve efficient security protection. The master key is dynamically updated according to a pre-negotiated rule, enabling the generation of a distinct session key for each data frame via a hash function. This approach ensures real-time operation without frequent key negotiation. To our knowledge, the DR-PAM is the first universal symbol scrambling scheme supporting arbitrary PAM orders. Experimental results demonstrate that at 56 Gbit/s and 112 Gbit/s data rates, legitimate receivers achieve performance identical to unencrypted links, while illegal receivers are completely unable to decrypt the data. The proposed encryption scheme has better compatibility with existing transmission systems and introduces no performance penalty, thus delivering a secure, real-time, and practical solution for cost-sensitive, high-speed DCI.

This paper proposes a secure encryption scheme for intensity modulation direct detection (IM-DD) data center interconnects employing 4-level pulse amplitude modulation (PAM4). The scheme integrates asymmetric key management with chaos-based symbol-level encryption to enhance physical-layer security. For master key management, a non-interactive Goldwasser-Micali (GM) based distribution is introduced for short-key scenarios. An interactive Elliptic Curve Diffie-Hellman (ECDH) based agreement with forward secrecy is adopted to support longer keys in high-security deployments. The master key is dynamically updated per frame, from which session keys are derived via a hash function to drive chaotic systems for generating chaotic sequences. For symbol-level encryption, we propose a cascaded architecture combining a chaos-driven variable-step Josephus permutation for symbol scrambling with Latin square encryption for symbol value substitution. Experimental results at 100 and 140 Gbit/s over 2 km standard single-mode fiber show that the bit error rate (BER) performance of the encrypted PAM4 signal is basically comparable to that of the unencrypted link, achieving receiver sensitivities of approximately -6.7 dBm and -5.8 dBm, respectively, at the 7% forward error correction threshold. For an eavesdropper without the correct key, the recovered data has a BER of approximately 0.5, rendering the extraction of any meaningful information impossible. Therefore, the proposed encryption scheme enhances physical layer security without requiring dedicated optical hardware or incurring any additional performance penalty.

The integration of intelligent reflective surfaces (IRS) on uncrewed aerial vehicles (UAVs), termed UAV-IRS, to bolster wireless communications has emerged as a hotspot of academic research and industrial application. In this paper, we investigate the problem of secure communication in the harsh communication environment assisted by multiple UAV-IRSs, where the UAV-IRSs act as relays to assist the downlink secure communication between the base station and the users. To maximize the security sum rate between the base station and the users, the trajectory planning and phase shift design of multiple UAV-IRS needs to be jointly optimized. To solve this complex non-convex optimization problem, we introduce a distributed collaborative optimization scheme for multiple UAV-IRSs called credit-aware cooperative multi-agent reinforcement learning (MARL), which takes MARL as the base algorithm, and then solves the credit allocation problem among multiple UAV-IRSs by using cooperative game theory to facilitate exploration, and finally constrains non-cooperative behaviors among UAV-IRSs by using the primal-dual optimization algorithm to promote cooperation. Finally, the effectiveness and superiority of the proposed scheme is verified by comprehensive simulation experiments.

Reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) is a promising solution for efficient low-altitude uncrewed aerial vehicle (UAV) operations. However, the existing works usually lack RIS-enabled anti-eavesdropping mechanisms, making it challenging to ensure secure low-altitude operations. To address this gap, we envision a novel secure low-altitude application scenario, where a ground ISAC platform communicates with aerial UAVs while simultaneously sensing and interfering with malicious eavesdroppers using dedicated artificial noise. Meanwhile, an active RIS is introduced to not only enhance communication performance but also provide supplementary non-line-of-sight (NLoS) links for sensing and security when line-of-sight (LoS) links are blocked. A novel optimization is established to jointly optimize communication/sensing/security precoders and RIS phase-shift matrix, with the goal of maximizing sensing signal-to-clutter-noise ratio (SCNR), under the constraints of communication and security performance. A maximization-minimization (MM) algorithm and a semi-definite relaxation (SDR) are proposed to solve this non-convex complex problem. Experimental results demonstrate the effectiveness of the proposed algorithms and the superiority of the anti-eavesdropping mechanism, with the SINR of malicious eavesdroppers significantly reduced by 5 dB.

Vehicular Edge Computing (VEC) is an essential part of the Internet of Vehicles (IoV) due to its low latency by moving the computational resources close to the edge. Although the introduction of network slicing into VEC improves resource utilization through dynamic resource allocation based on real-time demands and priorities, it increases the deployment and operational costs. In view of this, this paper envisions a resource allocation strategy for VEC based on network slicing technique, in which the tasks involved are not only dynamic but also heterogeneous. To minimize the system cost (including resource consumption and computation, network slice maintenance and reconfiguration costs), this paper proposes CST-RL, a confidence-based self-adjusting two-timescale reinforcement learning algorithm. This solution performs resource allocation and activation scheduling for network slices on a large timescale, while allocating slices to heterogeneous tasks on a short timescale to meet dynamic demands. In addition, we innovatively utilize critic in reinforcement learning to predict and compare the expected benefits of network slices with versus without reconfiguration. We introduce the Random Network Distillation (RND) technique to assess the confidence level of these benefits, thus providing guidance for network slices to automatically decide whether and when to undergo reconfiguration. Finally, we demonstrate the effectiveness and superiority of CST-RL through simulations. Results show that CST-RL yields 27.77% lower system cost compared to the scheme without network slicing and 15.15% lower system cost compared to performing constant network slicing configuration, with guaranteed Quality-of-Service.

Uncrewed aerial vehicle aerial vehicle (UAV)-enabled spatio-temporal crowdsourcing has a promising potential for efficient, real-time data collection from Internet of Things (IoT) in smart city applications. However, the existing works for such a problem mainly adapt to simple ideal synthetic scenarios and neglect the characteristics of the urban environment. In this paper, we consider both the system characteristics and urban environment and formulate the problem using both mathematical formulation and the Markov decision process. To ensure the timeliness and fairness of data collection from widely distributed IoT devices, we use the age of information (AoI) to represent the freshness of data. Such a problem jointly minimizes the average AoI and energy consumption of UAVs and IoT devices under the constraints of time sensitivity, regional boundaries, no-fly zones, UAV dynamics, and transmission range. A deep reinforcement learning (DRL)-based UAV-enabled spatio-temporal framework is proposed where three state-of-the-art DRL algorithms are introduced, namely twin delayed deep deterministic policy gradients (TD3), proximal policy optimization (PPO), and soft actor-critic (SAC). They all approximate the policy and value functions by deep neural networks based on actor-critic architecture. Experiments are conducted in both the synthetic and the real-city scenarios with real urban building data, and the results demonstrate the proposed framework's effectiveness, scalability, and transferability. Moreover, the proposed TD3, PPO, and SAC outperform deep deterministic policy gradient algorithm, random strategy, and meta-heuristic algorithms.

Cloud-network convergence represents a pivotal architectural evolution in modern communication networks, enabling unified orchestration of computing, storage, and network resources. Within such environment, trust evaluation plays an essential role in ensuring secure resource scheduling and reliable service deployment. While graph representation learning (GRL) has emerged as a powerful paradigm for trust evaluation by capturing complex relational patterns among entities, existing approaches still face three fundamental challenges: resource heterogeneity, data sparsity, and insufficient interpretability. To address these challenges, we propose LAGTrust, a Language-Augmented Graph representation learning framework for trust evaluation in cloud-network convergence. LAGTrust integrates large language models (LLMs) with graph neural networks through three synergistic components: (i) a Heterogeneous Feature Representation module that leverages LLMs to project multi-dimensional resource attributes into a unified semantic space while employing spatio-temporal graph neural networks to capture structural and behavioral dynamics; (ii) an Adaptive Feature Enhancement module that performs hierarchical LLM-driven augmentation for semantic completion, relationship inference, and profile enrichment; and (iii) a Semantic Explanation Generation module that synthesizes multi-source evidence through causal reasoning to produce interpretable trust assessments. Extensive experiments on real-world and synthetic topologies demonstrate that LAGTrust consistently outperforms baseline methods across multiple evaluation metrics, particularly under cold-start scenarios, validating its superior accuracy, robustness, and interpretability for trust evaluation in cloud-network convergence environments.

To address the challenge of balancing sensing accuracy and resource overhead, a novel cost-accuracy balanced cell-free multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system architecture was proposed. In this architecture, a single receiving base station (BS) strategy was adopted, by which resource overhead was reduced compared to traditional multi-receiving schemes. Furthermore, joint delay-angle positioning and an optimal BS selection strategy were innovatively integrated to enhance localization accuracy compared to traditional single-receiving schemes. To achieve high-rate communication and high-precision sensing, an optimization problem for joint beamforming design and BS selection was established, and was subsequently reformulated based on the constructive interference utilization mechanism. Mathematical techniques, such as fractional programming and semidefinite relaxation, were employed to solve the established complex non-convex optimization problem. Simulation results demonstrate that compared to the conventional single-receiving scheme, the positioning Cramér-Rao bound (CRB) is reduced by approximately 63.27% under the same overhead. Compared to the conventional multi-receiving scheme, energy consumption and latency are reduced by approximately 66.71% and 72.22%, respectively. Consequently, the proposed scheme achieves a superior Pareto front between resource overhead and sensing accuracy.

Existing studies on uncrewed aerial vehicle (UAV)-enabled multi-tier mobile edge computing (MEC) largely overlook heterogeneous dual-source tasks. To address this, a dual-source multi-tier (DS-MT) system is developed where both Internet of Things (IoT) terminals and sensing UAVs generate multi-modal tasks for terminal-edge-cloud and edge-cloud offloading. A joint trajectory and resource optimization is formulated to maximize task success rate and fairness. A soft actor-critic (SAC) algorithm solves this non-convex problem. Simulations show that the proposed system outperforms baselines, improving task success rate by 5.83%-255.91% and fairness by 1.24%.

In the Internet of Vehicles, vehicular crowdsensing is crucial for alleviating traffic congestion and ensuring the safety of autonomous driving. However, practical vehicular crowdsensing processes face dual challenges of skewed spatial distributions of vehicles and inadequate data quality guidance. These issues cause sensing redundancy in high-participation areas (HPAs) and coverage deficits in low-participation areas (LPAs), while also leading to unstable data quality. Given that participants' decisions are profoundly influenced by bounded rationality and psychological preferences, this paper proposes a collaborative incentive mechanism integrating behavioral economics and psychology (BEP-IM) to drive sustained spatial coverage and proactive sensing shaping. First, to mitigate coverage deficits in LPA, a reference-dependent two-sided selection and bidding strategy (RD-TSB) is designed to guide participants toward LPA via a reference-driven utility evaluation. Concurrently, a loss-aversion-based sustained incentive strategy (LA-RPI) is introduced to enhance their sustained participation within LPAs by amplifying loss perception. Furthermore, to overcome weak data quality constraints, an operant conditioning-based proactive sensing shaping strategy (OC-SFQ) is constructed, utilizing a closed-loop mechanism of relative improvement, variable-ratio reinforcement, and association updating to drive participants to output high-quality data. Simulation results demonstrate that the proposed mechanism effectively increases participation frequency in LPAs and optimizes sensing data quality.

Uncrewed aerial vehicle (UAV)-enabled mobile edge computing (MEC) has received wide attention for its ability to significantly reduce latency with flexible mobile aerial nodes. However, the existing works merely consider a fixed periodic network slicing and usually neglect the characterization of computing tasks in practical applications. Motivated by this, we envision a novel scenario where the dynamics and heterogeneity of users and tasks are considered in UAV-enabled MEC networks. We propose a novel self-adjusting two-timescale network slicing scheme based on the internal mechanism in deep reinforcement learning (DRL) to automatically determine whether and when network slicing needs to be reconstructed. Such a novel scheme balances reconstruction cost and system performance compared with traditional fixed schemes. We build an optimization to minimize total system cost for joint UAV trajectory planning and resource allocation at both network and user slice levels. To tackle this problem, we design a novel self-adjusting two-timescale proximal policy optimization (PPO) algorithm that utilizes the property of the critic network, referred to as SaTPPO. Experiment results manifest the effectiveness of the proposed SaTPPO, and it has a lower system cost than the traditional fixed schemes. Moreover, the proposed SaTPPO outperforms the state-of-the-art baselines concerning effectiveness and efficiency.

Optical fiber link monitoring has become a cornerstone for achieving high reliability and autonomous operation in next-generation intelligent optical networks. With the continuous growth of network capacity, node density, and transmission data rates, physical-layer impairments require real-time, in-service, and distributed monitoring capabilities that surpass the limitations of traditional reactive maintenance strategies. This paper provides a comprehensive and systematic overview of digital longitudinal monitoring techniques derived from the nonlinear Schrödinger equation (NLSE), including digital backpropagation, correlation-method, and mean-squared error based approaches. We discuss their mathematical foundations and clarify how they can monitor the profiles of physical parameters using only the received signals. Application cases in core, metro, access, and submarine networks are analyzed, demonstrating their feasibility and potential in power profile estimation (PPE), transient event capture, loss localization, amplifier gain analysis, fiber-type identification, and multi-parameter monitoring such as polarization dependent loss and differential group delay. Beyond profiling current achievements, we analyze practical challenges in real network deployment, including model-accuracy mismatches, computational complexity, sensitivity to parameter drift, and so on. Finally, we outline future research directions toward complexity reduction, parameter self-adaptation, enhanced robustness, and seamless integration with software-defined and data-driven control planes. These perspectives are expected to support the evolution of NLSE-based link monitoring from laboratory demonstrations to scalable deployment in large-scale operational optical systems.

In high-capacity and short-reach applications, double-sideband self-coherent detection (DSB-SCD) has garnered significant attention due to its ability to recover optical fields of DSB signals without requiring a local oscillator. However, DSB-SCD is fundamentally constrained by the non-ideal receiver transfer function, necessitating a guard band between the carrier and signal. While the conventional twin-single-sideband (twin-SSB) modulation scheme addresses this requirement, it incurs substantial implementation complexity. In this paper, we propose a spectrum inversion-based double-sideband (SI-DSB) modulation scheme, where spectral inversion shifts the DSB signal to the high-frequency region, creating a guard band around the zero frequency. After photodetector detection, baseband signal recovery is achieved through subsequent spectral inversion. Compared with the twin-SSB modulation scheme, this approach significantly reduces DSP complexity. The simulation exploration two modulation formats of pulse–amplitude modulation and quadrature-amplitude modulation, demonstrating a comparable system performance between SI-DSB and twin-SSB modulation schemes. We also illustrate the parameter optimization process for the SI-DSB modulation scheme, including carrier-to-signal power ratio and guard band. Furthermore, validation with three FADD receivers further demonstrates the superior performance of the proposed SI-DSB modulation in DSB-SCD systems.

Uncrewed aerial vehicle (UAV)-assisted integrated sensing, computation, and communication (ISCC) network enables the entire data analysis process for practical applications. The existing works of UAV-assisted ISCC merely consider a single data source, and there still exist gaps in the collection of environmental data via multiple sources. Motivated by this, we envision a novel UAV-assisted air-ground collaborative ISCC network that fully explores the cooperation between aerial UAVs and ubiquitous ground Internet of Things (IoT) devices. To achieve effective, efficient, and fair joint task scheduling and resource allocation, an optimization is established to minimize two novel indicators, i.e., computation offloading and sensing penalty indices, subject to constraints of boundary, anti-collision, and UAV energy consumption. To tackle this problem, a deep reinforcement learning (DRL) framework is proposed where three advanced DRL algorithms are included under centralized and decentralized control schemes. In former scheme, the central controller makes globally optimal decisions. In latter scheme, multiple agents decide independently based on local information. We demonstrate a forest fire monitoring use case simulated in a national forest park. Results show the mutually interfering, competitive, and beneficial relationships among triple functionalities. Besides, our solution outperforms three state-of-the-art baselines in terms of effectiveness and efficiency.

Radio-over-Fiber (RoF) schemes have received widespread attention in fronthaul links of fifth-generation (5G) wireless networks due to the advantages of large bandwidth and low transmission loss. To trade off the robustness and spectral efficiency (SE) of ROF, the delta sigma modulation (DSM) has been recognized as a promising alternative to common public radio interface (CPRI) for implementing novel mobile fronthaul (MFH) architectures. In this article, an intensity modulation and direct detection (IM/DD) system with a two-stage DSM structure was proposed. We proposed a two-stage 1-bit DSM to convert high-order Quadrature Amplitude Modulation (QAM) format into 4-level Pulse Amplitude Modulation (PAM-4) format, achieving the transmission of high order QAM signals through IM/DD links. The two-stage DSM architecture achieves a signal-to-noise ratio (SNR) of 58 dB by suppressing in-band quantization noise (QN) introduced through first-stage DSM quantization via subsequent second-stage DSM processing, thereby enabling support for 65536-QAM signal transmission. We demonstrated the transmission of a 56-Gbaud DSM-PAM-4 signal in the C-band over a 2-km fiber link. The results show that the proposed system is capable of supporting up to 65536-QAM signals while achieving a bit error rate (BER) below the hard-decision forward error correction (HD-FEC) threshold of 3.8e-3.

A single-fiber forward transmission distributed vibration sensor (SF-FTDVS) is proposed in this article based on bi-directional structure, which breaks the dependence of traditional FTDVS on the interference loop structure or the multifiber optical path. The proposed SF-FTDVS inherits the advantages of fiber sensors with forward transmission. It can realize the relay-free distributed vibration sensing greater than 210 km since the continuous forward transmission of light waves provides good signal-to-noise ratio (SNR) performance. Besides, the bidirectional transmission structure enhances the consistency of vibration event perception, so that it can achieve a more precise location. Experiments demonstrated the feasibility of vibration event location with a wide-frequency range from 400 Hz to 10 kHz, allowing for an un-repeated sensing link up to 212 km. To the best of our knowledge, this represents the longest perception distance achieved by a relay-free sensor. The average location standard deviation (STD) and the location fluctuation range are estimated to be 15.7 m and +/- 37 m, respectively, demonstrating an improvement of 14% and 12% compared to the traditional distributed vibration sensors (DVS) scheme. The proposed SF-FTDVS not only offers the advantages of simple deployment, high-positioning accuracy, and wide vibration response bandwidth, but also exhibits good compatibility with other components, demonstrating great potential for applications in Internet of Things (IoT) systems.

Integrated sensing and communications (ISAC) is emerging as one of the six application scenarios for future wireless networks. Characterizing the Pareto boundary is an urgent issue in multiple-input multiple-output (MIMO) ISAC systems. The lack of unified sensing metrics and the neglect of the instantaneous worst-case sensing requirement in the existing works present challenges to this issue. In this paper, we propose a more universal and operable theoretical limit analysis framework where the high-signal-to-noise ratio (SNR) channel capacity is characterized under instantaneous covariance mismatch constraint. We use the covariance mismatch that implies the distance to optimal covariance as the sensing metric. The optimal covariance can be computed by optimizing any key sensing metric. An MIMO ISAC Pareto boundary can be obtained by computing channel capacity under fine-grained sensing thresholds, below which the mismatch must be constrained. In the experiments, three radar modes are considered, and the results show that different radar modes affect capacity performance and a trade-off exists between communication and sensing. In addition, pure communication capacity is the upper bound of the communication capacity in ISAC. Moreover, capacity under instantaneous constraint approaches that under average one in pure MIMO communications when signal length approaches infinity.

Reconfigurable Intelligent Surface (RIS)-assisted Autonomous Aerial Vehicle (AAV)-enabled Integrated Sensing and Communication (ISAC) systems have received wide attention due to flexible mobility of AAVs and auxiliary links provided by RIS. However, the existing works usually neglect the peak-to-average power ratio (PAPR), a key communication performance metric that reflects amplifier efficiency, interference levels, and equipment costs. Motivated by this, we consider PAPR constraints in RIS-assisted AAV-enabled ISAC system. An optimization for joint beamforming and RIS phase-shift matrices design is established, with the goal of minimizing the weighted sum of the multiuser interference (MUI) for communication and sensing beam pattern mismatch (BPM), subject to PAPR constraints, power constraints, ideal beam direction approximation constraints, and constant mode constraints on the phase-shift matrices. To tackle this non-convex problem, a multivariate cyclic iterative alternating-direction multiplier method (MCI-ADMM) is adopted to decouple the original problem into three sub-problems regarding three variables. Some mathematical techniques are adopted to solve these sub-problems, including augmented lagrangian, constraint reconstruction method based on orthogonal Procrustes problem, manifold optimization, quadratic representation, and variable substitution. The experimental results show inherent trade-offs between dual functionalities under PAPR constraints. In addition, the consideration of PAPR performs better system performance compared with baselines. Also, the stability and reliability under different parameters settings are verified.