We propose a moving source localization algorithm using the Alternating Direction Method of Multipliers (ADMM) to estimate the position and velocity of a moving source from time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. The algorithm first employs the Taylor series expansion to eliminate the range variable. Subsequently, through the ADMM framework, it decouples the complex constrained optimization problem into two separate subproblems that have closed-form solutions. The final estimate is then obtained through alternating iterations. We further introduce a scale-matching adaptive penalty mechanism to alleviate the numerical instability caused by the inherent scale mismatch between position and velocity states. Theoretical analysis establishes the convergence properties of the proposed algorithm and shows that the KKT conditions of the reformulated problem are satisfied at convergence. Simulation results demonstrate that the proposed algorithm achieves a significant improvement in localization accuracy.

Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fixed 2D plane, neglecting key parameters such as direction of arrival (DoA), time of arrival (ToA), and vertical spatial variations. Such a limitation is primarily due to the reliance on static learning paradigms, which hinder generalization beyond the training data distribution. To address these challenges, we propose UrbanRadio3D, a large-scale, high-resolution 3D RM dataset constructed via ray tracing in realistic urban environments. UrbanRadio3D is over 37x larger than previous datasets across a 3D space with 3 metrics as pathloss, DoA, and ToA, forming a novel 3Dx 3D dataset with 7x more height layers than prior state-of-the-art (SOTA) dataset. To benchmark 3D RM construction, a UNet with 3D convolutional operators is proposed. Moreover, we further introduce RadioDiff-3D, a diffusion-model-based generative framework utilizing the 3D convolutional architecture. RadioDiff-3D supports both radiation-aware scenarios with known transmitter locations and radiation-unaware settings based on sparse spatial observations. Extensive evaluations on UrbanRadio3D validate that RadioDiff-3D achieves superior performance in constructing rich, high-dimensional radio maps under diverse environmental dynamics. This work provides a foundational dataset and benchmark for future research in 3D environment-aware communication.

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments show that this integrated GenAI framework consistently surpasses traditional interpolation and deep learning methods, especially under sparse sensing conditions. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments.

Orthogonal Time Frequency Space (OTFS) modulation effectively suppresses Doppler frequency shift in high-mobility scenarios for achieving reliable transmission. However, OTFS requires high-precision channel estimation, leading to significant pilot overhead and susceptibility to co-channel interference from heterogeneous mobile users, which compromises its spectrum efficiency. To address this challenge, this paper studies the resource allocation for an intelligent reflecting surface (IRS)-aided OTFS-non-orthogonal-multiple-access (NOMA) system with the co-existing of high-mobility and low-mobility users involving millimeter-wave (mmWave) communication capability. It focuses on maximizing the spectral efficiency of high-mobility users, by jointly optimizing base station transmit power and IRS passive reflection coefficients. An alternating optimization (AO) algorithm is designed to tackle the intractable non-convex problem. With the AO approach, the problem is decoupled into a power allocation subproblem and a reflection coefficient optimization subproblem (solved via successive convex approximation methods), yielding a suboptimal solution through iterative alternation. An IRS phase initialization scheme is developed to enhance the convergence speed and solution quality of the proposed algorithm. Moreover, the worst-case transmission rate and robust IRS phase optimization design are investigated for imperfect channel state information. Simulation results demonstrate that the proposed scheme effectively improves the transmission rate while maintaining high reliability, outperforming other benchmark schemes. Design recommendations for OTFS parameters in mmWave band are provided to strike the balance between signal transmission reliability and efficiency.

This paper investigates covert communication in the presence of an eavesdropper employing stochastic resonance (SR)–based detection. A unified analytical model is developed to characterize nonlinear noise-matching detectors, with SR serving as a representative example. Closed-form expressions for the achievable covert rate are derived under a KL-divergence-based covertness constraint, revealing that SR-enabled eavesdroppers can significantly enhance detection performance under perfectly matched noise conditions. More importantly, this work establishes a fundamental performance–robustness tradeoff: although stochastic resonance can amplify weak signals, its detection gain is highly sensitive to noise-level mismatch and interference uncertainty. We prove that under random or unknown interference, the worst-case detection performance of SR-based detectors is strictly inferior to that of conventional linear detectors, such as energy detection. This result demonstrates that the detection advantage offered by stochastic resonance is not free and inevitably comes at the cost of robustness. Numerical results validate the theoretical analysis and show that random, fast-varying interference constitutes a minimax-optimal strategy for covert transmission when the eavesdropper's detection model is unknown. These findings provide new insights into the fundamental limits of covert communication against advanced nonlinear detectors.

This letter investigates multi-user covert communications. Specifically, we propose an intelligent spectrum control (ISC) scheme that employs spectrum sensing-assisted dynamic control to generate time-frequency resource occupation matrices for multiple legitimate users. Guided by these matrices, the users can occupy different frequency slots irregularly for covert data transmission. By performing spectrum sensing in advance, the proposed scheme can also proactively avoid external interference and co-channel collisions among users. In addition, we consider an eavesdropper with wideband detection capability and derive the closed-form expressions for the minimum detection error probability (DEP) of the eavesdropper and the reliable transmission probability (RTP) of the legitimate users. We then optimize the transmit power to maximize the covert rate (CR), and further characterize the maximum number of legitimate users that can access the network covertly and concurrently. Simulation results demonstrate the superiority of the proposed ISC scheme in terms of covertness, reliability, and user capacity.

We propose an innovative physical layer authentication method, leveraging deep learning to robustly safeguard millimeter wave communications against pilot contamination and clone attacks. Unlike traditional upper-layer authentication mechanisms, our method capitalizes on the spatial-temporal characteristics of millimeter wave channels to extract unique fingerprints, thus establishing a lightweight channel-based authentication technique. Existing methods largely overlook pilot contamination attacks, which may severely degrade the performance of physical layer authentication. Furthermore, traditional threshold-based methods struggle to differentiate between multiple nodes, while supervised learning-based methods are practically constrained due to the unavailability of attackers’ instantaneous channel state information. Moreover, traditional real-valued deep neural networks are inefficient in utilizing the phase information of complex-valued channels, rendering them inadequate for designing practical physical layer authentication schemes. To address these challenges, we propose an autoencoder, empowered by an alternating direction method of multipliers, which can detect and mitigate pilot contamination attacks by exploiting the inherent sparsity of channels. Subsequently, we design a weighted loss function to optimize the proposed classifiable autoencoder to strike an effective balance between detecting clone attacks and authenticating multiple nodes. Finally, to further enhance feature extraction from complex-valued channels, we customize a complex-valued classifiable autoencoder incorporating an innovative complex-valued long short-term memory module. Our simulation results unveil that the proposed method significantly outperforms existing approaches in maintaining high authentication accuracy even under pilot contamination, achieving a desirable trade-off between false alarm and detection rates. Additionally, our proposed complex-valued neural networks further enhance the accuracy of clone attack detection and multiple legitimate nodes authentication.

Unmanned aerial vehicles (UAVs) have become key components of sixth-generation (6G) wireless networks and have significantly promoted the rapid development of the low-altitude economy (LAE). However, UAV communications are inherently vulnerable to eavesdropping attacks owing to the open and broadcast nature of wireless channels, which makes transmission secrecy a critical challenge. This paper investigates an air-to-ground secure transmission system in which a UAV-mounted reconfigurable intelligent surface (RIS) assists a base station (Alice) while simultaneously serving multiple ground users (Bobs) under the surveillance of an eavesdropper (Eve). We mount the RIS on the UAV to jointly exploit UAV mobility and RIS reconfigurability for physical-layer security (PLS). First, we consider a single-user case in which we formulate a secrecy throughput maximization problem by jointly optimizing the UAV trajectory and RIS phase shifts. The framework is then extended to a multi-user non-orthogonal multiple access (NOMA) system, where the additional transmit power allocation between users is optimized. To address the resulting non-convex problems, we develop an efficient alternating optimization (AO) framework by leveraging successive convex approximation (SCA) and semidefinite relaxation (SDR). The numerical results show that the proposed method achieves rapid convergence and delivers significant improvements in secrecy performance over benchmark schemes with random RIS phase shifts and without UAV trajectory planning. Our results highlight the importance of UAV-RIS cooperation in achieving secure and scalable multi-user communications for future wireless networks.

In delay-intolerant covert communications, supporting high-capacity services (e.g., image or video transmission) is extremely challenging under stringent covertness constraints and limited bandwidth, due to the fundamental conflict between transmission quality and latency. To address this issue, this paper proposes a novel transmission framework enabled by semantic communication and frequency hopping (FH) for covert wireless communication systems under the finite-blocklength regime. At the transmitter, semantic communication first extracts task-relevant features from the source data to compress the transmitted information, thereby reducing transmission delay while maintaining communication quality. From the receiver’s perspective, the proposed scheme exploits the advantages of FH reception to enhance the signal-to-noise ratio (SNR) at the legitimate receiver, thereby decreasing the decoding error probability and improving transmission efficiency, which in turn shortens the transmission delay. Specifically, we formulate a bi-objective optimization problem that simultaneously maximizes semantic reconstruction quality and minimizes total transmission delay, subject to covertness, average power, and channel equivocation constraints. This non-convex problem is addressed via a hybrid optimization strategy (HOS) that combines outer-layer enumeration with inner-layer alternating optimization (AO) enhanced by the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed scheme outperforms conventional schemes by achieving lower transmission delay, strong covertness, and effective anti-jamming and anti-interception capabilities.

Integrated air-ground communication (IAGC) has emerged as a promising solution to deliver seamless wireless coverage and high-data-rate services. However, potential malicious eavesdroppers pose a serious threat to the confidential transmission in IAGC due to their non-cooperative behaviors and the inherent openness of communication channels. To tackle this problem, a dynamic spectrum control (DSC)-based transmission scheme is proposed to enhance covert performance and communication reliability in IAGC. With the proposed scheme, we apply the principles of block cryptography, perform adaptive iterative and orthogonal transformations to generate sequence sets that drive transmission decisions. Guided by these sequences, multiple legitimate users can dynamically occupy different frequency slots and transmit data simultaneously. In addition, we analyze the probability of frequency slot multiplexing when several data groups occupy the same frequency slot in a time slot, resulting in the closed-form expression for the detection error probability. We then derive the maximum reliable transmission probability and ergodic rate subject to the covert communication constraints. Simulation results demonstrate that the proposed scheme can achieve superior covert performance compared with benchmark schemes. Furthermore, we evaluate and discuss the effects of key parameters in the proposed DSC-based transmission scheme on communication security and reliability.

The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have emerged as a promising approach for precoder design in rate-splitting multiple access (RSMA) systems. Although SCA-based algorithms deliver satisfactory performance, their lengthy optimization process and high computational complexity—due to repeatedly solving approximate problems—hinder real-time implementation. To address this challenge, we propose an alternating direction method of multipliers (ADMM)-induced deep learning network (AIDLN)-based precoder design for multiuser downlink communications, leveraging the strengths of both model-based optimization and data-driven deep learning. Specifically, we first develop an iterative precoding algorithm based on ADMM, where each subproblem admits a closed-form solution. Next, we unfold the iterations of this ADMM-based algorithm into the layers of a neural network, constructing a deep-unfolding architecture. To accelerate convergence, we introduce trainable parameters alongside the ADMM hyperparameters. Numerical results demonstrate that our AIDLN-based precoder achieves superior performance compared to traditional model-based optimization while maintaining low computational complexity, making it suitable for real-time applications.

The proliferation of unauthorized Uncrewed Aerial Vehicles (UAVs) poses significant security risks, necessitating robust detection systems. However, in practical long-range surveillance scenarios, UAV signals often deteriorate due to severe attenuation and complex environmental interference, rendering traditional detection methods ineffective. To address this, this letter proposes a novel signal enhancement framework comprising an improved adaptive Wiener filter. We introduce a dynamic noise spectrum estimation strategy coupled with a decision-directed phase compensation mechanism. This approach effectively suppresses non-stationary background noise while reconstructing high-fidelity time-frequency features by rectifying phase distortions. Experimental results on a proprietary real-world dataset demonstrate that the proposed method significantly improves signal quality, enabling high-accuracy detection using YOLO models even in ultra-low Signal-to-Noise Ratio (SNR) regimes compared to raw data-based baselines.

The terahertz (THz) band offers abundant spectrum resources for high-throughput communication and ultra high-precision localization. This paper investigates secure communication in cooperative THz orthogonal frequency-division multiplexing (OFDM) bistatic integrated sensing and communications (ISAC) systems, where multiple base stations (BSs) equipped with extremely large-scale antenna arrays (ELAAs) collaboratively serve downlink users while concurrently locating multiple targets. Malicious targets are assumed to act as potential eavesdroppers attempting to intercept confidential information intended for legitimate users. To mitigate these threats, we formulate a joint optimization problem for analog beamforming, digital precoding, true-time delayers (TTDs), and sensing signal covariance matrix design. The objective is to maximize the minimum secrecy rate subject to Cramer-Rao bound (CRB) constraints that ensure localization accuracy. This problem is highly challenging due to the non-convex CRB constraint, strongly coupled variables, high computational complexity from ELAA, and near-field channel modeling. To address these challenges, we propose a novel data-driven framework that integrates graph neural networks (GNNs) with the Mamba architecture. Our proposed framework first encodes the interactions among users, targets, and BSs into a heterogeneous graph and then employs message passing to optimize vertex features. The Mamba blocks further enhance this process through their selection mechanism and state space modeling capabilities, enabling dynamic and context-aware optimization of beamforming, TTD configurations, and sensing parameters. Numerical simulations validate that the proposed method outperforms both conventional and learning-based baselines, while offering high computational efficiency and strong generalization across different network conditions.

Interference recognition serves as the preprocessing technology for interference suppression. Researchers typically employ the method based on the time-frequency graph - deep convolutional neural network (CNN) in pursuit of recognition accuracy. Nevertheless, certain time-frequency analysis processing approaches or network models with excessive parameters make it challenging to apply this technology to devices with limited resources. In this paper, a weight pruning and parameter quantization assisted multi-Scale lightweight network named WPMNet is proposed, which can directly take the raw time series data as input. The network model comprises three multi-scale convolutional modules, an attention mechanism, and fully connected layers. The multi-scale convolutional modules are composed of convolutional kernels of different sizes in parallel, aiming to extract deep-level features and features of various granularities simultaneously. To compress the model, we introduce an adaptive search algorithm for determining the optimal pruning threshold for weight pruning, ensuring that the accuracy decline is within an acceptable range. And the model is further compressed through parameter quantization. The experimental outcomes demonstrate that prior to the lightweighting of the model, the average recognition accuracy was 97.6%. After the lightweight processing, the compression ratio of the network attained 84.5%, while the average recognition accuracy merely declined slightly to 95.08%. This substantiates that the proposed approach is applicable to scenarios that simultaneously demand both high precision and model deployability, such as embedded systems or mobile devices.

Network slicing in software-defined integrated satellite-terrestrial networks (SD-ISTNs) enables the creation of customized network slices on shared infrastructure, improving flexibility and resource utilization. However, node failures due to software/hardware faults, energy depletion, or attacks may disrupt slices, causing service interruptions. Failure recovery in SD-ISTNs is particularly challenging, as satellite mobility results in a time-varying yet predictable topology and the availability of multi-dimensional resources dynamically fluctuates. Therefore, in this paper, we investigate rapid failure recovery for network slicing in time-varying SD-ISTNs by jointly leveraging communication, storage, and computation resources to achieve efficient recovery and minimize the service completion time. Specifically, we employ the multi-functional time-expanded graph (MF-TEG) to model time-varying SD-ISTNs with multi-dimensional resources, and formulate the failure recovery problem as a mixed integer nonlinear programming (MINLP) problem. To effectively solve this problem, we propose two novel algorithms: (i) an effective reformulation-based mixed integer linear programming (ER-MILP) algorithm that reformulates the original MINLP into a tractable MILP and serves as a high-quality benchmark, albeit with relatively high computational complexity; and (ii) a binary-search-based penalty successive upper bound minimization-rounding (BS-PSUM-R) algorithm that achieves near-optimal performance with significantly lower complexity. Simulation results demonstrate that both algorithms effectively reduce service completion time compared with baseline algorithms.

Autonomous aerial vehicle (AAV)-assisted data collection in wireless sensor networks (WSNs) is challenging under uncertain topology and limited prior information. This paper investigates such scenarios and formulates AAV trajectory planning as a multi-objective partially observable Markov decision process (POMDP) that jointly optimizes age of information (AoI), energy consumption, and coverage ratio. We introduce a graph encoder that enables structural reasoning over heterogeneous random topologies and supports generalization to unseen deployments. Built upon temporal difference learning for model predictive control v2 (TDMPC2), a world-model-based deep reinforcement learning (DRL) algorithm, we develop TDMPC2-Graph (TDMPC2-G), which executes multi-step predictive rollouts to improve trajectory planning efficiency and quality. Simulation results show that model-free baselines SAC and TD3 fail to learn effective policies in this challenging setting, whereas TDMPC2-G demonstrates robust performance across all prior information levels. Compared to exhaustive coverage strategies, TDMPC2-G reduces terminal AoI by 10–14%, while maintaining comparable energy consumption and coverage ratios, thereby validating its robustness across structurally heterogeneous random topologies.

The rise of heterogeneous aerial and space platforms within Space-Air-Ground Integrated Networks (SAGINs) introduces significant challenges, as the limited spectrum resources force these platforms to operate within shared frequency bands, resulting in co-existing systems. Effective interference management in such networks requires both the design of communication channels and the dynamic mitigation of interference between them. Prior research has largely focused on interference mitigation with fixed communication links, often overlooking adaptive channel selection, which can result in performance degradation. In this study, we address this limitation by introducing MetaRS, an innovative, self-intelligent rate-splitting solution designed for more flexible interference management in co-existing SAGINs. MetaRS enables adaptive channel and communication scheme selection, by leveraging a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA)-based framework enhanced with a one-pass diffusion model. Specifically, the FD-RSMA-based framework allows MetaRS to dynamically shift its interference management strategy according to the current network status. The integration of the diffusion model further enhances MetaRS by allowing it to recognize and adapt to real-time channel conditions and user deployment, thereby enabling self-intelligent interference mitigation. Simulation results demonstrate that MetaRS significantly outperforms conventional SDMA, RSMA, and FD-RSMA approaches. This improvement stems from MetaRS’s joint optimization of channel selection and its adaptive, intelligent interference management capabilities, which effectively balance channel utilization and mitigate interference in complex, multi-platform environments.

Uncrewed aerial vehicles (UAVs) play a crucial role in modern communication systems owing to their high mobility and broad coverage. However, due to the inherent open nature of the wireless channels, UAV-to-ground links are facing significant security threats from eavesdroppers and malicious jammers. To address these challenges, we propose a dynamic spectrum control (DSC) scheme integrating joint UAV trajectory and transmit power optimization to enhance UAV communication security in this paper. This scheme divides transmission channels from time and frequency dimensions and intelligently generates secure decision sequences using cryptographic principles based on real-time channel states, enabling transmissions for legitimate users without intra-cell interference. Based on a rapid-flooding time synchronization protocol, we analyze inter-cell collision probability (CP) and formulate an optimization problem for the secrecy rate. To further enhance security, we conduct a joint UAV trajectory and transmit power optimization. Through the successive convex approximation (SCA) method, we transform the non-convex optimization problem into a tractable convex form, obtaining a suboptimal solution. Simulations demonstrate that our proposed scheme significantly enhances security compared to conventional UAV communication methods.

Software-Defined Integrated Space-Terrestrial Networks (SD-ISTNs) offer a flexible and scalable architecture for 6G, enabling seamless global connectivity and support for diverse services. Network slicing is a critical technology in SD-ISTNs, enabling the creation of customized network slices on shared physical infrastructure to support diverse service requirements. However, network slicing in SD-ISTNs faces significant challenges due to time-varying topologies, limited and dynamic multi-dimensional heterogeneous resources, as well as diverse Quality of Service (QoS) requirements. To address these challenges, this article investigates network slicing across multiple consecutive timeslots in time-varying SD-ISTNs. We discuss key enabling technologies, including time-varying graph modeling, Virtual Network Functions (VNFs) deployment, flow routing, and their joint optimization. Specifically, we adopt time-varying graph models to characterize the dynamic topology and establish a unified representation of multi-dimensional resources in SD-ISTNs. Based on these models, we propose network slicing strategies across multiple timeslots to improve resource utilization and network performance. A case study demonstrates the effectiveness of our proposed strategy. Finally, we discuss open research issues for network slicing in time-varying SD-ISTNs.

The rapid development of 6G, Internet of Things, and low-altitude wireless networks has greatly increased the demand for accurate wireless position sensing. This paper focuses on passive position sensing which estimates signal source’s position without active transmission by solely analyzing received signals. We identify and summarize the key factors influencing position sensing accuracy across three dimensions including network configuration, parameter acquisition, and algorithmic processing, and highlight unresolved technical challenges. To systematically address these issues, we propose a novel framework named PCDL integrating four functional modules, Perception, Cognition, Decision, and Learning. We demonstrate effectiveness of the PCDL framework by a case study, validating its ability on improving the localization accuracy for the sources in the blind zone. The PCDL framework structures an integration of artificial intelligence into the position sensing workflow, guiding the design for the future intelligent wireless position sensing system.