Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over LAWNs, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.

This paper introduces a sensing-centric joint communication and millimeter-wave radar paradigm to facilitate collaboration among intelligent vehicles. We first propose a chirp waveform-based delay-Doppler quadrature amplitude modulation (DD-QAM) that modulates data across delay, Doppler, and amplitude dimensions. Building upon this modulation scheme, we derive its achievable rate to quantify the communication performance. We then introduce an extended Kalman filter-based scheme for four-dimensional (4D) parameter estimation in dynamic environments, enabling the active vehicles to accurately estimate orientation and tangential-velocity beyond traditional 4D radar systems. Furthermore, in terms of communication, we propose a dual-compensation-based demodulation and tracking scheme that allows the passive vehicles to effectively demodulate data without compromising their sensing functions. Simulation results underscore the feasibility and superior performance of our proposed methods, marking a significant advancement in the field of autonomous vehicles.

Due to the growing diversity of vertical applications, current integrated sensing and communications (ISAC) technologies in wireless networks remain insufficient to support complex services beyond communications. To this end, future networks are evolving toward an integrated heterogeneous service provisioning (IHSP) platform, which aims to integrate a broad range of heterogeneous services beyond the dual-function scope of ISAC. Nevertheless, this trend intensifies the conflicts among concurrent heterogeneous services under constrained resource sharing. In this paper, we overcome this resource constraint by the joint use of two novel elastic design strategies: compromised service value assessment and flexible multi-dimensional resource sharing. Consequently, we propose a value-prioritized elastic multi-dimensional multiple access (MDMA) mechanism for IHSP. First, we define the compromised Value-of-Service (VoS) metric by incorporating elastic parameters to characterize user-specific tolerance and compromise in response to various performance degradations under constrained resources. This VoS metric serves as the foundation for prioritizing resource sharing among IHSP services with fairness among concurrent competing demands. Next, we adapt the MDMA to elastically multiplex services using appropriate multiple access schemes across different resource domains. This protocol leverages user-specific interference tolerances and cancellation capabilities across different domains to reduce resource-demanding conflicts and co-channel interference within the same domain. Then, we maximize the system’s VoS by jointly optimizing MDMA design and power allocation. Since this problem is non-convex, we propose a monotonic optimization-aided dynamic programming (MODP) algorithm to obtain its optimal solution. Additionally, we develop the VoS-prioritized successive convex approximation (SCA) algorithm to efficiently find its suboptimal solution. Finally, simulations are presented to validate the effectiveness of the proposed designs.

Wireless sensing is recognized as a promising technology for next-generation wireless networks, utilizing signals from devices such as Wi-Fi to detect and interpret humanrelated information, including movement status and sleep quality. However, the broadcast nature of wireless signals poses significant privacy risks, as unauthorized users may intercept these signals, leading to potential privacy breaches. Given the sensitive personal information embedded in CSI data and the limitations of encryption-based protection at the transmitter, conventional privacy protection measures are inadequate. Thus, developing physical layer-based privacy protection technologies for wireless sensing is urgently needed. In this paper, we consider a wireless sensing system model with privacy leakage and characterize wireless sensing performance as a classification problem. We propose a novel multi-antenna signal processing-based privacy protection strategy. To illustrate the fundamental tradeoff between sensing and privacy protection, we model the wireless sensing process as a communication task based on non-cooperative joint sourcechannel coding and introduce the concept of a sensing rate region. Our main contribution is the characterization of sensing and privacy protection performance at two key points within the sensing rate region: P-OS, indicating the maximum sensing rate of a legitimate receiver constrained by the minimum achievable sensing rate of an illegitimate receiver, and P-OPP, indicating the minimum sensing rate of an illegitimate receiver constrained by the maximum sensing rate of a legitimate receiver. Based on our analysis, we provide strategies to establish achievable boundaries between P-OS and P-OPP. Moreover, we define the secure sensing rate RPP, indicating the privacy protection performance of the system. Within this framework, we examine several illustrative examples, validated through numerical simulations

Deploying foundation models across distributed airborne networks offers a promising solution for delivering flexible, high-coverage, and on-demand generative AI services. However, the deployment and tuning of foundation models present critical challenges on airborne platforms such as Unmanned Aerial Vehicles (UAVs), due to the intensive computational requirements, substantial memory footprint, and high communication overhead, particularly given these platforms’ limited power and memory capacity as well as the limited communication connections. In view of these, a collaborative fine-tuning and inference framework for deploying foundation models over UAV networks is proposed, which employs a split model deployment strategy to distribute computational loads across multiple UAVs. The framework also incorporates a multi-stage fine-tuning approach utilizing a large vision model-based knowledge distillation and personalized local tuning to further enhance performance while maintaining system stability despite UAV mobility. The proposed framework could achieve foundation model fine-tuning in a memory- and computation-efficient manner. To further improve the communication and computation efficiency, two variants of the framework are proposed via leveraging over-the-air computations and parameter-efficient fine-tuning techniques in communication and local computation. Extensive experimental evaluation demonstrates the superior and stable performance of the proposed framework compared to baselines in terms of generalization, communication efficiency, memory efficiency, and scalability.

Wireless Human-Machine Collaboration (WHMC) represents a critical advancement for Industry 5.0, enabling seamless interaction between humans and machines across geographically distributed systems. As the WHMC systems become increasingly important for achieving complex collaborative control tasks, ensuring their stability is essential for practical deployment and long-term operation. Stability analysis certifies how the closed-loop system will behave under model randomness, which is essential for systems operating with wireless communications. However, the fundamental stability analysis of the WHMC systems remains an unexplored challenge due to the intricate interplay between the stochastic nature of wireless communications, dynamic human operations, and the inherent complexities of control system dynamics. This paper establishes a fundamental WHMC model incorporating dual wireless loops for machine and human control. Our framework accounts for practical factors such as short-packet transmissions, fading channels, and advanced HARQ schemes. We model human control lag as a Markov process, which is crucial for capturing the stochastic nature of human interactions. Building on this model, we propose a stochastic cycle-cost-based approach to derive a stability condition for the WHMC system, expressed in terms of wireless channel statistics, human dynamics, and control parameters. Our findings are validated through extensive numerical simulations and a proof-of-concept experiment, where we developed and tested a novel wireless collaborative cart-pole control system. The results confirm the effectiveness of our approach and provide a robust framework for future research on WHMC systems in more complex environments.

Autonomous aerial vehicle (AAV) target tracking technology is an essential component for enabling diverse low-altitude activities. Due to the constraints on energy and computing resources of AAVs, current approaches face challenges in balancing prolonged flight duration with precise tracking while avoiding high computational complexity. Therefore, this paper proposes an energy-aware formation control algorithm for multiple AAVs to cooperatively track a target while retaining a desired formation pattern. First, to achieve a balanced outcome in terms of tracking performance and control effort, an actor-critic based learning predictive rule is explored to develop a near-optimal control protocol that stabilizes error dynamics and minimizes value functions for discrete-time AAV systems. By decomposing the infinite-horizon target tracking problem into a sequence of finite-horizon sub-problems, the reinforcement learning (RL)-based predictive control algorithm can achieve fast convergence in approximating the solution of Hamilton--Jacobi--Bellman (HJB) equation. Furthermore, by employing a delicately designed asynchronous policy iteration mechanism with adjustable learning intervals in RL, the cumbersome learning process can be effectively mitigated, thereby attaining both high learning efficiency and a reduced computational burden simultaneously. The involved errors are proven to be convergent and simulation results validate the optimality of our method.

Low Altitude Economy (LAE), empowered by aerial platforms such as unmanned aerial vehicles (UAVs), is rapidly evolving as a key enabler for next-generation smart services like urban mobility, environmental monitoring, and emergency response. To support these applications, aerial platforms should perform real-time, efficient navigation and sensing services in dynamic, dense, and uncertain low-altitude environments via large artificial intelligence (AI) models. However, enabling such intelligence requires overcoming significant challenges of limited onboard resources and dynamic connectivity for the aerial platforms. This paper provides a comprehensive study on addressing these challenges to enable large AI model-based services in LAE networks. We propose a cloud-edge-end architecture that facilitates collaborative model training and inference among aerial and ground nodes. We also explore three key enabling technologies, i.e., model collaboration, dynamic networking, and resource management. We validate the proposed framework through a case study on radio map generation for LAE networks. Moreover, we discuss future research directions that pave the way for scalable, intelligent, and resilient large AI model-driven LAE networks.

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.

In the digital twin edge network (DTEN), the effective data processing at edge is essential to meet real-time requirements of DT models. However, due to the selfishness and limited computation resource, edge servers (ESs) are not willing to participate in data processing without reasonable reward. In this paper, a contract-learning integration method is proposed to address the computing incentive and data offloading problems in DTEN. A two-dimensional computation-reward contract incentive mechanism is designed to motivate ESs to provide computation resource for data processing. Then, the optimal contract items are obtained while satisfying individually rational and incentive compatible constraints. Next, based on the designed incentive mechanism, we model the data offloading process as an optimization problem to obtain the minimum system delay. A multi-agent deep reinforcement learning method is introduced to optimize the data offloading strategy without requiring prior knowledge of the DTEN. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed method.

Precise channel state information (CSI) is crucial to ensure the reliability of deep-space communications. However, severe Doppler shifts and solar scintillation during superior solar conjunction lead to rapidly time-varying channels, making the obtained CSI outdated. To solve this problem, we propose an attention-enhanced gated recurrent unit (Att-GRU)-based channel prediction scheme for deep-space communications. We first establish a time-varying deep-space channel model affected by Doppler shift and solar scintillation. Subsequently, we integrate the attention mechanism into the GRU and propose an Att-GRU-based channel prediction scheme to capture the temporal dependencies in the deep-space channel. By dynamically assigning weights to different time steps, the proposed scheme further improves the model's ability to predict complex channel variations. Finally, simulation results show that the proposed Att-GRU achieves up to 16.5 % normalized mean squared error (NMSE) reduction compared with the GRU model.

As a cutting-edge security technology, covert communication aims to hide the transmission behavior rather than the contents. However, its development is facing challenges due to the fact that the conventional fixed-position antennas cannot fully utilize the spatial degrees of freedom. Therefore, an effective way to overcome this challenge is to develop the more efficient flexible position antennas (FlexPAs), which can realize flexible beamforming and spatial multiplexing by dynamically regulating the position, shape or size, providing new solutions to covert communications. In this article, we first investigate three general architectures of FlexPAs and analyze their advantages and disadvantages, especially for covert communications. Then, we briefly review the evolution of covert communications, and illustrate the benefits that FlexPAs can bring. In addition, we present important specific methods to improve the covertness by exploiting FlexPAs with the emphasis on two perspectives, including the transmitter and receiver. Finally, we propose an FlexPAs-aided covert transmission scheme to verify its effectiveness, and future research directions and challenges are discussed.

Low Earth Orbit (LEO) satellite constellations are rapidly evolving, providing pervasive coverage that enables new opportunities for AI-driven services. Delivering Deep Neural Network (DNN)-based applications on these satellites faces unique challenges due to limited onboard computational resources, energy constraints, and stringent latency requirements. In this article, we review the state-of-the-art techniques for onboard DNN inference and task offloading, highlighting the key bottlenecks and resource management challenges. We further discuss emerging solutions, including reinforcement learning-driven offloading strategies and lightweight AI models, that can enhance resource efficiency while maintaining service quality. Finally, we outline future research directions and potential application scenarios for realizing high-quality AI services in LEO satellite networks.

This paper investigates intelligent predictive beamforming design for simultaneous wireless information and power transfer-integrated sensing and communication (SWIPT-ISAC) systems for low-altitude economy wireless networks. Considering the downlink scenario where the base station aims to localize the moving targets/communication users and also transfer power to them, we formulate a weighted sum optimization problem to balance the trade-off between achievable communication rate and harvested energy, subject to sensing accuracy constraints defined by the Cram & eacute;r-Rao lower bound. To address the non-convexity of the problem, we propose the Time-Spatial Fusion Network (TSFusionNet), an unsupervised deep learning (DL) framework that leverages multi-step historical channel state information for predictive beamforming design. TSFusionNet integrates convolutional and recurrent layers with a differential attention mechanism to capture spatial-temporal dependencies and mitigate non-stationary channel dynamics. We introduce a dynamic penalty-based loss function to enforce sensing constraints during training. Simulation results show that by adjusting the weight factor, the proposed method achieves a trade-off between rate and energy while meeting sensing accuracy requirements. Moreover, it significantly reduces computational complexity by up to approximately 96.8% in parameters and 81.5% in FLOPs, compared to existing DL frameworks.

With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systematically explore the application of GDMs in wireless networks. Firstly, we identify the core challenges of wireless networks and argue why GDMs are uniquely suited to address them. We then introduce the mathematical principles of GDMs and representative models. Furthermore, we organize our comprehensive review through a structured taxonomy that categorizes GDM-based schemes into the sensing, transmission, and Applications, complemented by a security plane. For each representative scheme, we analyze its innovative points, the role of GDMs, strengths, and weaknesses. Ultimately, we extract key challenges and provide potential solutions, with the aim of providing directional guidance for future research in this field.

Cell-Free massive MIMO (CF mMIMO), as a cornerstone for future wireless communication networks, can significantly enhance spectral efficiency, reduce energy consumption, and achieve seamless coverage. This paper investigates dynamic resource allocation in CF mMIMO systems provisioning integrated sensing and communication (ISAC) and involving multiple users and heterogeneous services. To address the rapidly varying channels, high complexity, task-dependent relevance and insufficient information sharing among access points (APs) arising from ISAC requirements, we propose a novel Hierarchical Attention-Driven Multi-Agent Reinforcement Learning (HADMARL) framework. A higher-layer “Commander Controller” outputs the AP-user pairing matrix, which is passed to a lowerlayer “Worker Controller” that leverages the central attention mechanism to perform fine-grained resource allocation. Simulations demonstrate that HAD-MARL achieves about a 10% improvement in system utility score compared to benchmarks, with the performance gains becoming more pronounced as the number of APs and users increases. We empirically demonstrate and visualize the effect of the central attention mechanism on the formation of efficient collaboration among APs. The source code is available at: https://github.com/Andyyy2000/HAD.git.

Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learning (RL) is a powerful framework, it often encounters difficulties with high-dimensional state spaces and complex environments, leading to substantial computational demands, distributed intelligence, and potentially inconsistent outcomes. Large language models (LLMs), with their extensive pretrained knowledge and advanced reasoning capabilities, offer promising tools to enhance RL in optimizing 6G wireless networks. We explore RL models augmented by LLMs, emphasizing their roles and the potential benefits of their synergy in wireless network optimization. We then examine LLM-enabled RL across various protocol layers: physical, data link, network, transport, and application layers. Additionally, we propose an LLM-assisted state representation and semantic extraction to enhance the multi-agent reinforcement learning (MARL) framework. This approach is applied to service migration and request routing, as well as topology graph generation in unmanned aerial vehicle (UAV)-satellite networks. Through case studies, we demonstrate that our framework effectively performs optimization of wireless network. Finally, we outline prospective research directions for LLM-enabled RL in wireless network optimization.