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.
This paper investigates robust physical-layer security (PLS) for UAV-assisted double-layer heterogeneous networks (HetNets) under channel state information (CSI) uncertainty. A joint channel assignment and power control optimization problem is formulated to maximize system sum capacity while satisfying worst-case secrecy constraints. To address the resulting mixed-integer nonlinear programming (MINLP) problem and multi-agent non-stationarity, we propose a Robust Action-Masked Multi-Agent Deep Reinforcement Learning (RAM-MADRL) framework. In this framework, the action-masking mechanism enforces physical feasibility during decision making, while the cooperative mini-batch mechanism is adopted as a training strategy to improve learning stability under the CTDE architecture. Simulation results under a representative setup with four UAVs, seven U2A links, a secrecy-rate requirement of c0=2 bps/Hz, and a UAV altitude of 20 m show that the proposed RAM-MADRL achieves about a 10% improvement in system sum capacity over learning-based baselines while maintaining robust secrecy performance under CSI uncertainty.
The accelerating demands of 6G networks, characterized by large-scale connectivity and the need for real-time processing across massive device populations, necessitate communication architectures featuring enhanced intelligence and flexibility. Cybertwin-enabled systems have emerged as a promising framework for supporting secure and adaptive operations; however, their Physical Layer Security (PLS) performance under practical multi-antenna Base Station (BS) configurations remains insufficiently understood. This study advances existing Secrecy Outage Probability (SOP) analyses through the incorporation of Maximum Ratio Transmission (MRT) beamforming at the BS and realistic modeling of wireless and wired channels, including Nakagami-m fading and impulsive noise. Closed-form SOP expressions are derived for six scenarios arising from the combination of three distinct eavesdropping conditions and two beamforming strategies, which are directed either toward the Cybertwin Host (CTH) or toward the server. When the server functions as an internal eavesdropper, the beamforming impact is governed by the decoding order and interference structure inherent in Non-Orthogonal Multiple Access (NOMA). For scenarios involving an external eavesdropper, a tractable SOP expression for the server is obtained using a Laguerre-integration-based approximation. Analytical and simulation results show that MRT beamforming consistently enhances the SOP of the CTH, whereas its effect on the server depends on the resulting signal-to-interference-plus-noise ratio. These findings provide a comprehensive analytical foundation for evaluating PLS in cybertwin-enabled 6G networks.
To address the coverage enhancement challenge in blockage-prone Millimeter-Wave (mmWave) networks where instantaneous Channel State Information (CSI) is unavailable, we propose a multiple-RIS assisted low-complexity coverage enhancement approach. Firstly, a gridding-based optimization framework is proposed to discretize the continuous weak coverage area into a set of grids and transform the coverage problem into a deterministic Max-Min SNR problem. Then, a sub-array decomposition and global angle optimization strategy is incorporated, and an Enhanced Dung Beetle Optimizer (eDBO) algorithm is developed to reduce computational complexity and solve the formulated non-convex problem, respectively. Simulation results verify that the proposed framework with eDBO successfully eliminates coverage holes and achieves high-quality uniform coverage, significantly outperforming the benchmarks in both robustness and effectiveness.
Backscatter Communication (BC) enables ultra-low-power wireless connections by employing simple Backscatter Devices (BDs) to reflect and modulate incident radio frequency (RF) signals for data transmission. Due to its inherently open nature, security in BC has becomes a main concern in practical applications. Mutual authentication between a BD and an RF source serves as a primary security mechanism to verify the legitimacy of devices, authenticating the origin of received signals. However, achieving mutual authentication in BC systems is challenging due to the severe computational and energy constraints of BDs. Previous work cannot solve this issue in an effective way. To address this issue, this paper presents APAuth+, a lightweight and efficient authentication protocol designed specifically for BC systems. APAuth+ exploits the inherent ability of BDs to harvest energy from incoming RF signals and integrat this capability into the authentication process, which consists of three phases: initialization, preparation, and authentication. Concretely, APAuth+ relies on secure power-harvesting measurements and secret-key verification to perform mutual authentication between the BD and the AP. Security analysis shows that APAuth+ is resilient against a wide range of attacks, including eavesdropping, replay, relay, brute-force, and jamming attacks. Both theoretical analysis and numerical simulations confirm the accuracy, robustness, and efficiency of APAuth+ under diverse operating conditions. Comparative results further demonstrate that APAuth+ outperforms existing authentication schemes in terms of authentication accuracy, efficiency, and attack resilience.
To satisfy the uplink security requirements for legitimate users communicating under multi-antenna eavesdropping, while preventing eavesdroppers from accessing confidential information and enabling reliable interception of eavesdroppers, we first establish rigorous mathematical definitions of Trusted Integrated Sensing and Communication (TISAC) and elaborate the inherent spatial boundary constraints for the spatial coverage of Low-Earth-Orbit (LEO) ISAC satellite systems. Subsequently, we formulate an uplink power allocation optimization model for TISAC with multi-antenna eavesdroppers, which quantifies the inherent coupling between system parameters and overall TISAC performance. Furthermore, we propose a Multi-Variable Dynamic Power Allocation (MV-DPA) scheme that decomposes the joint uplink optimization problem into multiple subproblems for iterative optimization, which achieves a favorable trade-off between TISAC service performance and computational complexity. Simulation results demonstrate that during LEO satellite beam scanning, the proposed scheme effectively enlarges TISAC coverage for sensing-aided secure uplink transmission in multi-antenna eavesdropping scenarios. This work provides theoretical guidance and technical support for secure uplink transmission in 6G networks.
Any anomaly in the data flow of industrial control networks can directly translate into security vulnerabilities within the control system. Therefore, maintaining continuous situational awareness of industrial control networks and achieving precise identification and effective handling of abnormal data are critical to ensuring the reliability and functional safety of control systems. Anomaly detection tasks in industrial control networks aim to identify behavior deviating from normal patterns within network traffic or device data. Although existing unsupervised and self-supervised learning methods can train models without labeled data, their performance is limited by prior assumptions about data distributions, and it is difficult to ensure model stability when the training data is contaminated. To this end, this paper designs a Data Reconstruction and Anomaly Detection method, termed as DRAD. Specifically, the proposed method reconstructs the original data from the perspective of complete data-sample features, rather than relying solely on partial attribute learning. DRAD comprises four core modules. The state representation module maps industrial data stream samples to states of a Markov decision process. The action generation module outputs reconstructed data via a deterministic policy network. The state sampling module uniformly samples interactions from the simulated environment within the training set. The reward computation module designs instantaneous rewards based on reconstruction errors. Each module collaboratively optimizes within a deep deterministic policy gradient framework to maximize long-term cumulative rewards and learn normal data patterns. Moreover, DRAD gradually converges to a more robust detection strategy through long-term exploration, thereby significantly enhancing the stability of detection performance. Extensive experiments on eight datasets show that DRAD improves the AUC-ROC and AUC-PR by 6.8%-17.0% and 12.1%-47.3% and improves robustness under different anomaly contamination rates compared with seven representative competing methods.
Turbo Product Codes (TPCs) are increasingly considered efficient Forward Error Correction (FEC) schemes in modern high-speed communication systems. A key obstacle to their implementation lies in the design of a low-complexity decoder architecture. While recent soft-decision decoders commonly adopt Chase or Belief Propagation (BP) algorithms, this work introduces an alternative bit-wise iterative decoding approach along with its corresponding hardware structure, aiming to minimize decoding complexity and enhance hardware efficiency. Additionally, a set of innovative techniques for lowering complexity and accelerating convergence is presented. Both performance simulations and implementation outcomes confirm that the proposed architecture not only attains considerable reductions in complexity and gains in hardware efficiency, but also shows marginally improved error correction performance relative to prior solutions. These attributes position the design as a viable option for practical high-throughput communication hardware.
As 6G technology advances, it promises to deliver ultra-high transmission speeds and enhanced network capabilities, supported by developments in Massive MIMO and Extremely Large MIMO technologies. However, the expansion of transmission antenna arrays significantly increases signal processing complexity. In this paper, we analyze approximate dynamic precision computation in large-scale MIMO systems and explore dynamic precision-aware approximate computation as a means to counteract the rising complexity. We develop a theoretical framework that clarifies the relationships between quantization bit width, spectral efficiency, and Bit Error Rate (BER). First, we determine a critical point (a stationary point) beyond which further increases in Signal-to-Noise Ratio (SNR) yield negligible gains in spectral efficiency. We show that this SNR stationary point shifts by approximately 6 dB for each additional bit of quantization precision. Moreover, we establish a relationship between quantization bit width and BER, identifying a threshold bit width beyond which further increases no longer yield meaningful BER reductions for a given modulation order and SNR. Using these findings, we propose several dynamic bit-width adaptation strategies to optimize system performance. Specifically, we introduce techniques to adjust the quantization bit width based on real-time SNR, modulation scheme, and target BER. These strategies move away from traditional worst-case fixed-precision designs toward more resource-efficient and performance-tailored approaches. Our methods offer a new framework to manage quantization complexity, paving the way for innovative designs in future 6G communication systems.
The burgeoning advancement in communication technologies, coupled with the widespread adoption of mobile devices, has led to an exponential increase in communication data volumes. Navigating this deluge of data in the big data era poses significant challenges in terms of processing and analysis. Clustering, an unsupervised learning approach, stands out as a potent analytical tool in this context, capable of segmenting data into groups of similar entities to unearth underlying patterns and structures. Given its capacity to discern intrinsic data relationships, clustering has garnered considerable interest for its potential applications within the communication field. Despite the wealth of research, many existing surveys either focus solely on the theoretical aspects of clustering algorithms or restrict their scope to a single domain, often overlooking the intrinsic synergy between data transmission and content processing. Addressing this gap, this survey delivers an exhaustive review of clustering algorithm applications from a unique cross-disciplinary perspective, bridging the realms of wireless communications and media communications. We argue that as communication systems evolve towards semantic and content-aware networking, a unified view of these domains is essential. This comprehensive review not only aids readers in grasping the varied applications of clustering algorithms—ranging from physical layer security to semantic image analysis—but also empowers researchers to refine algorithm design by drawing on analogous use cases across these sectors. To enhance comprehension, we elucidate diverse clustering algorithm implementations within wireless and media communications, illustrating with detailed examples. Moreover, we highlight prevailing challenges and delineate future research trajectories for clustering algorithm applications in these fields.
Encrypted traffic classification is a critical task in network security and management. However, existing methods often neglect the structural and multi-dimensional correlations of traffic. Furthermore, their heavy reliance on large-scale labeled data limits their generalization ability in dynamic networks. To address the above limitations, this paper proposes a novel few-shot encrypted traffic classification framework based on self-supervised learning (S2F-GNN). Firstly, we represent network sessions as dynamic heterogeneous graphs consisting of flow, time, and attribute nodes. This representation effectively captures the structural features and temporal evolution of traffic in a multi-dimensional space. Secondly, we design a multi-task self-supervised framework that synergizes masked autoencoder, graph structure prediction, clustering, and contrastive learning. This framework efficiently learns universal and robust traffic representations from large-scale unlabeled data. In addition, we design a Jensen-Shannon Divergence (JSD)-based feature selection mechanism to identify features that remain stable across changing environments. This further enhances the model’s adaptability in few-shot and dynamic scenarios. Experimental results demonstrate that S2F-GNN significantly outperforms the state-of-the-art methods on multiple public datasets. Especially in the few-shot scenarios with scarce labeled data, the average accuracy and F1-score are improved by 4.04% and 9.68%, respectively.
The rapid advancement of Autonomous and Intelligent Transportation Systems (A-ITS) has driven the widespread adoption of vehicular communication technologies to enhance the safety, efficiency, and sustainability of modern transportation networks. Key communication technologies, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X), are fundamental for the operation of A-ITS, enabling seamless interaction among vehicles, infrastructure, and other road users. This survey provides a comprehensive analysis of these critical communication technologies and explores the integration of edge computing and Artificial Intelligence (AI) to optimize A-ITS performance. Furthermore, we identify and discuss the major challenges facing A-ITS, including network reliability, data privacy, security, and interoperability, which must be addressed for successful deployment. We also examine the role of emerging 5G/6G networks in smart cities and the Internet of Things (IoT), focusing on their potential to advance autonomous transportation systems. This work serves as a valuable resource for understanding the current state and future prospects of vehicular communications in A-ITS.
In complex traffic environment, the limited sensing range of Connected and Autonomous Vehicles (CAVs) leads to missing Regions of Interest (ROI) when occlusions occur. Collaborative perception can extend the sensing range and recover ROI through information sharing, but it also introduces additional computation and increases processing latency. In this paper, we propose a joint latency-resource optimization architecture to achieve accurate and real-time perception under occlusions. First, we model an edge-vehicle collaborative network, and formulate a dual optimization problem for resource allocation and minimizing perception data collection latency. Second, with resource constraints, this problem is transformed into an ROI fusion quality optimization problem, which is solved by the Improved Discrete Salp Swarm (IDSS) algorithm. Finally, based on the fusion quality optimization results, the Hybrid Action Space-based Deep Deterministic Policy Gradient (HDDPG) algorithm is introduced to dynamically allocate available computation resources, thereby achieving minimized perception fusion latency. Simulation results demonstrate the efficiency of the proposed joint optimization architecture, which could reduce the processing latency by 0.1 seconds compared to comparison algorithms, with higher accuracy.
Orthogonal Frequency-Division Multiplexing (OFDM) signals inherently exhibit a high Peak-to-Average Power Ratio (PAPR). This forces the Power Amplifier (PA) to operate with large input back-off, resulting in substantial energy waste and posing a serious challenge for green communication. To address this, we propose a novel transmission framework that integrates a nonlinear companding-based PAPR reduction method with the Digital Post-Distortion (DPoD) technique to balance power efficiency and Bit Error Rate (BER) performance. To recover the transmitted signals, a Successive Decision Feedback Nonlinearity Equalizer (SDFNE) is developed at the receiver side. The SDFNE employs a time-domain least squares equalizer with closed-loop iterative feedback to jointly mitigate companding distortion and PA nonlinearity. The proposed framework balances transmitter-side PAPR suppression and receiver-side nonlinear equalization. This enables the PA to operate closer to its saturation point while achieving excellent BER performance. Meanwhile, compliance with spectral emission mask requirements is maintained. Numerical results on diverse PA models demonstrate that the proposed transmission framework with the SDFNE method significantly outperforms conventional nonlinear signal processing methods, including nonlinear companding, digital predistortion, and DPoD. Superior BER performance is achieved in a power-efficient manner, making the proposed framework particularly suitable for resource-constrained communication systems.
The Internet of Things (IoT) has transformed the healthcare industry, with technologies such as big data, blockchain, and artificial intelligence driving significant benefits through technology convergence. However, Healthcare IoT (H-IoT) systems face persistent security and privacy challenges. This meta-synthesis analyzes the current literature to identify key aspects of H-IoT security, including requirements, threats, and proposed technical solutions, while emphasizing the role of technology convergence in addressing these issues. The reviewed studies are categorized into two groups: qualitative research (focused on security features, requirements, threats, and attacks) and quantitative research (organized into six technical solution domains: blockchain; cryptography; authentication schemes and protocols; artificial intelligence and machine learning; H-IoT systems with embedded security solutions; and security and privacy frameworks). Finally, future research directions are explored to emphasize the critical role of technology convergence in enhancing H-IoT security.
With the advent of the 6G era, Inclusive Intelligent Services (IIS)—which rely on the convergence of advanced communication technologies and Artificial Intelligence (AI) algorithms—are expected to become ubiquitous. These services necessitate efficient distributed intelligent learning and reasoning mechanisms to support diverse and dynamic application scenarios. However, the increasing complexity of emerging applications poses significant challenges for orchestrating differentiated, customized services through efficient Service Function Chaining (SFC) provisioning. SFC refers to the structured arrangement of various Virtual Network Functions (VNFs) to enable seamless service delivery. To address the stringent requirements of 6G IIS, particularly ultra-low latency and high reliability, this study proposes an advanced Deep Reinforcement Learning (DRL) framework augmented with a generative Conditional Variational AutoEncoder (CVAE) for automated and adaptive SFC provisioning. The proposed hybrid approach leverages VAE-driven feature extraction and dimensionality reduction to enhance generalization, while the DRL component optimizes exploration and decision-making through trial-and-error learning. The simulation results demonstrate that the proposed approach significantly improves the performance of 6G intelligent services, surpassing baseline algorithms in key metrics, including provision cost, SFC acceptance rate, and end-to-end SFC delay.
Coverage performance is a critical metric for evaluating the configuration design of satellite constellations, particularly for mega Non-Geostationary Earth Orbit (NGSO) constellations characterized by a large number of satellites, low orbital altitudes and rapid motion speeds. Traditional analytical methods fail to meet the timeliness requirements for mega-constellations due to its extremely high computational complexity. To address this challenge, this paper proposes a geometric relationship-aware and efficient coverage performance analysis based on geographic longitude and argument of latitude (GL-u) mapping. By mapping both satellite trajectories and ground-target access areas onto the GL-u plane composed of the geographic longitude of the subsatellite point and the argument of latitude, the proposed method enables rapid coverage analysis through the intersection points of the corresponding mapped polygons. Unlike traditional methods, the proposed scheme does not require grid discretization or time slicing, while supporting the calculation of nearly all coverage performance metrics. Moreover, compared to the existing 2D-Maps based methods, it offers a more straightforward mapping process for ground-target access areas and ensures a one-to-one correspondence between the GL-u plane positions and actual spatial positions. Simulation results verify the accuracy of the proposed method, with the computational error below 0.4 seconds, and demonstrate its superior computational efficiency over STK for constellations exceeding 1500 satellites. This work provides an efficient analytical framework that enables real-time coverage assessment and rapid design for next-generation mega-constellations.
Achieving robust 5G-Advanced indoor localization under sparse data is an active research topic. Pure Channel Charting (CC) preserves local neighborhood structure but yields unitless embeddings with potentially nonlinear global distortions, complicating metric alignment. Model-based localization degrades under Non-Line-of-Sight (NLoS) propagation and unknown noise statistics. We propose Model-assisted Channel Charting (MCC), which fuses: (i) masked-autoencoder pretraining on Channel State Information (CSI); (ii) triplet-based manifold learning driven by geodesic dissimilarity; and (iii) a link-wise heteroscedastic pseudorange likelihood that jointly predicts per-link residual means and uncertainties. A multi-head regressor outputs positions, residual biases, and noise variances, and uncertainty weighting balances the losses. Unlike prior model-assisted CC that relies on pairwise distance scaling, MCC anchors the chart directly to metric coordinates via the physics-consistent likelihood while learning data-driven link reliability for reweighting. Evaluations on a challenging 5G Indoor Factory (InF) dataset with severe NLoS show that MCC achieves state-of-the-art accuracy. With 18 base stations and a labeled density of 1/2 points/m2, the proposed Model-based Fingerprinting (MFP) and Model-based Triplet (MT) achieve the position error of 0.62 m and 0.69 m for 90% of cases, respectively. Under very sparse labels ( < 1/20 points/m2), MT outperforms competing baselines. Calibration results further indicate that the predicted uncertainties monotonically correlate with empirical residual errors, supporting reliability-aware localization in harsh NLoS conditions.
Superdirective antenna arrays, characterized by their ability to achieve extremely high directivity in compact antenna arrays, hold significant promise for advancing wireless communications. This review provides a comprehensive overview of the theoretical foundations, practical considerations, and recent advances in superdirective antenna array-aided wireless communications. We begin with a review of classical superdirectivity theory. We then discuss the challenges that hinder the practical use of superdirective antenna arrays. Specifically, mutual coupling, high sensitivity, narrow bandwidth, poor radiation efficiency and hardware limitations are discussed, and recent efforts to address them are systematically reviewed. Emerging applications, such as multi-user communications, superdirective antenna pairs, and superdirective holographic array design and Electromagnetic Hybrid Beamforming (EHB) are also discussed. In addition, superdirective antenna array prototypes are surveyed. Finally, the potential research directions are also discussed for future development, including impedance matching, antenna design, integrated performance optimization, hardware implementation, and cross-layer system integration. This work aims to bridge the gap between theoretical developments and practical applications, serving as a valuable reference for both researchers and engineers in the field.
