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.

Flying ad hoc networks (FANETs) offer flexible, real-time wireless communication solutions for multi-drone systems by utilizing drones as network routers. However, FANETs’ unique characteristics, including high mobility, unstable network topology, and intermittent connectivity, pose significant challenges in designing efficient and reliable routing protocols. Traditional routing protocols for mobile ad hoc networks often fall short in highly dynamic airborne environments due to excessive control overhead, increased latency, and inefficient route maintenance. To address these issues, this paper proposes an enhanced on-demand predictive (EDP) routing protocol that integrates a neighbor coverage-based predictive flooding mechanism and an adaptive link quality-based route maintenance strategy. The flooding mechanism mitigates directional deafness by using a Kalman filter-based probabilistic forwarding model, while the route maintenance method optimizes path selection based on distance, traffic load, and link lifetime. Simulation results show that EDP significantly improves packet delivery rate, reduces network delay, and lowers overhead compared to benchmarks, making it well-suited for applications in FANETs.

With the exponential growth of smart devices connected to wireless networks, data production is increasing rapidly, requiring machine learning (ML) techniques to unlock its value. However, the centralized ML paradigm raises concerns over communication overhead and privacy. Federated learning (FL) offers an alternative at the network edge, but practical deployment in wireless networks remains challenging. This paper proposes a lightweight FL (LTFL) framework integrating wireless transmission power control, model pruning, and gradient quantization. We derive a closed-form expression of the FL convergence gap, considering transmission error, model pruning error, and gradient quantization error. Based on these insights, we formulate an optimization problem to minimize the convergence gap while meeting delay and energy constraints. To solve the non-convex problem efficiently, we derive closed-form solutions for the optimal model pruning ratio and gradient quantization level, and employ Bayesian optimization for transmission power control. Extensive experiments on real-world datasets show that LTFL outperforms state-of-the-art schemes.

The integration of blockchain technology with the sixth generation (6G) networks offers a promising approach to enhance the reliability and trustworthiness of Industrial Internet of Things (IIoT) systems. Since IIoT devices typically lack the capability to directly participate in blockchain consensus, drone networks offer a viable alternative by providing dynamic coverage and reducing dependence on fixed infrastructure such as centralized servers. Sharding is an effective method to improve the scalability of blockchain systems, yet existing sharding schemes overlook the complexity and dynamic nature of drone network topologies. These networks frequently experience changes due to drone mobility, task variations, and energy constraints, all of which can disrupt consensus communications. To address these challenges, we propose a time-varying graph (TVG)-based blockchain sharding scheme (BSTVG) tailored for large-scale drone blockchain networks. The TVG-based model captures the temporal dynamics of drone communications. We adopt an improved K-Means++ clustering algorithm that incorporates communication conditions to adapt network sharding. Additionally, we develop mechanisms for intrashard consensus and cross-shard transaction processing. To accommodate node joins, exits, and significant topological changes, we introduce a slot-epoch coupling mechanism that dynamically adjusts the epoch length. We analyze the security of the proposed scheme and validate its performance through simulations. Experimental results demonstrate that our scheme not only enhances the throughput but also reduces energy consumption of the drone blockchain network.

The industrial Internet of things (IIoT) is increasingly employing blockchain-assisted drone swarms to execute critical missions. However, due to the high energy consumption, traditional blockchain mechanisms with substantial computation overhead cannot be deployed on drones with limited power resources. To address this issue, we propose a novel renewable energy-aware consensus mechanism, named proof of green (PoG). In particular, based on the hybrid energy supply framework, we design a dynamic election strategy. To jointly optimize renewable energy consumption and historical reputation of drones, we formulate a multi-objective optimization problem to determine the optimal node selection scheme for drones. Moreover, to enhance the fairness of the election process, we introduce a verifiable random function (VRF)-based perturbation factor and integrate PoG into the Byzantine fault tolerance (BFT) voting process. Simulation results demonstrate that compared to traditional mechanisms, our scheme reduces drone battery consumption and provides robust resistance against common security threats in drone swarms.

With the explosive advancement of unmanned aerial vehicles (UAVs), the security of efficient UAV networks has become increasingly critical. Owing to the open nature of its communication environment, illegitimate malicious UAVs (MUs) can infer the position of the source UAV (SU) by analyzing received signals, thus compromising the SU location privacy. To protect the SU location privacy while ensuring efficient communication with legitimate receiving UAVs (RUs), we propose an Active Reconfigurable Intelligent Surface (ARIS)-assisted covert communication scheme based on virtual partitioning and artificial noise (AN). Specifically, we design a novel ARIS architecture integrated with an AN module. This architecture dynamically partitions its reflecting elements into multiple sub-regions: one subset is optimized to enhance communication between the SU and RUs, while the other subset generates AN to interfere with the localization of the SU by MUs. We first derive the Cram & eacute;r-Rao Lower Bound (CRLB) for localization with received signal strength (RSS), based on which, we establish a joint optimization framework for communication enhancement and localization interference. Subsequently, we derive and validate the optimal ARIS partitioning and power allocation under average channel conditions. Finally, tailored optimization methods are proposed for the reflection precoding and AN design of the two partitions. Simulation results validate that, compared to baseline schemes, the proposed scheme significantly increases the localization error of MUs by approximately 37.65% with only a 3.69% reduction in the communication rate between the SU and RUs, thereby effectively protecting the SU location privacy.

The rapid proliferation of edge computing resources, together with transformative breakthroughs in artificial intelligence (AI), has catalyzed the emergence of edge AI. As a representative paradigm of AI-communication integration in 6G, edge AI extends AI capabilities to edge nodes and mobile devices, enabling ubiquitous, real-time, and secure intelligent services. Edge AI inference refers to the routine invocation phase of trained models, where forward propagation is executed to produce task-specific outputs under stringent latency and reliability requirements. However, it encounters critical challenges stemming from the escalating computational demands of AI models and the intrinsic resource limitations of edge environments. To address these challenges, this paper presents a comprehensive survey on edge AI inference in 6G mobile networks, identifying key principles and innovative solutions for optimizing inference performance under edge resource constraints. We begin by outlining communication-efficient techniques and adaptive model deployment strategies designed to mitigate transmission, computation, and storage overhead in edge AI inference. We then review collaborative inference mechanisms that facilitate effective workload distribution across cloud-edge-device computing nodes. This is followed by an in-depth exploration of task-oriented optimization, which coordinates network resources throughout the end-to-end inference workflow to boost the performance of both discriminative and generative edge AI inference. Finally, we examine practical platforms and promising applications, discuss future research directions and open issues, with the hope of inspiring continued advancements in this evolving field.

Satellite communication networks operate under stringent computational constraints and are susceptible to sophisticated cyberattacks. This paper introduces a novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution. In the long-term loop, the ground segment processes historical data to estimate key statistical parameters of the task environment. Additionally, we incorporate the time-varying characteristics of satellite wireless links to account for the dynamic communication context. In the short-term loop, the satellite employs a receding horizon optimization that models dynamic task arrivals and maximizes a utility function considering detection rates and resource costs. To counter intelligent adversaries interception, we introduce a deception mechanism using Bayesian persuasion theory. By strategically manipulating the short-term action sequences in the telemetry downlink, we mislead an external attacker’s beliefs. We mathematically model the attacker’s optimal response under channel uncertainty and demonstrate that our framework significantly reduces attacker utility. The approach’s effectiveness is formally proven using Lyapunov theory.

Unmanned aerial vehicles (UAVs) can be utilized effectively as airborne base stations, offering wireless communication and federated learning (FL) services for terrestrial edge devices (EDs). FL enables EDs to collaboratively train a global model for specific tasks without sharing their local raw data. However, due to the unreliable wireless links and dynamic topologies, potential malicious nodes may attempt to masquerade as legitimate nodes to pose various potential threats (e.g., inference, poisoning, and backdoor attacks) to undermine the trustworthiness of the intermediate model parameters. Although existing cryptographic authentication protocols focus on the data-level security, security level can be further enhanced at the physical layer level. In this paper, we design FedPLA, a UAV-aided federated learning framework enhanced by physical layer authentication, which utilizes SNR difference for lightweight authentication to ensure FL intrinsic security. Subsequently, we formulate a multi-step decision problem for joint UAV trajectory and resource allocation, aiming to minimize the latency and energy costs while maximizing secure edge device number with intrusion-proof and unjustly accused-proof guaranty. To efficiently deduce strategies while avoiding potential dangerous policies, we develop an LSTM-enhanced safe deep reinforcement learning algorithm (LSTM-SDRL) for real-time strategy making. Furthermore, extensive simulations demonstrate the effectiveness of the proposed LSTM-SDRL.

The growing demand for maritime broadband services exposes the limitations of conventional satellite–ground systems, which often fail to jointly optimize spatial–temporal resources, computation costs, and the trade-offs between energy and information freshness. To address these challenges, we develop a physics-aware system model for near-shore Space-Air-Ground-Sea Integrated Networks (SAGSIN), capturing realistic maritime channels, Poisson task arrivals, Age of Information (AoI), and energy consumption. Based on this model, we propose a AI-enabled dual-timescale framework based on heterogeneous Mixture-of-Experts (MoE). Specifically, the framework operations are formulated as a two-agent Markov decision process: the long-slot agent determines satellite beam-hopping coverage, while the short-slot agent jointly optimizes per-slot task offloading, UAV deployment, and resource allocation. Both agents employ MoE policies with Top-K gating. Simulation results validate that the proposed framework achieves superior AoI–energy trade-offs compared with baseline methods, highlighting the potential of AI-driven autonomy in next-generation network orchestration.

Achieving the ambitious performance goals of Sixth-Generation (6G) networks necessitates harnessing new spatial degrees of freedom. Unmanned aerial vehicle (UAV) and movable antenna (MA) technologies are key enablers in this regard, providing such freedoms at the macro and micro scales, respectively. This paper investigates the uplink of a multi-user multiple-input multiple-output (MU-MIMO) system comprising multiple UAVs, each equipped with an MA array. To unlock the full potential of such system by jointly optimizing UAV positions, MA positions and transmit-receive beamforming, we formulate the optimization problem into a sum-rate maximization target function, which is highly challenging due to its non-convex nature and tightly coupled optimization variables. To address it, we propose a novel algorithm framework, termed Group Greedy Orthogonal Weighted Minimum Mean Square Error (GGO-WMMSE). The original problem is first converted into a weighted minimum mean square error (WMMSE) form, which is subsequently recast as a regularized least squares problem with sparse structure. Based on this sparsity observation, a novel sparse regression algorithm termed as Regularized Least Squares based Gradient Grouped Simultaneous Orthogonal Matching Pursuit (RLS-G-GSOMP), efficiently boosts the cohesive optimization on the precoders, UAV locations, and MA positions. Numerical results validate the substantial performance gain of the proposed algorithms, manifesting fast convergence speed. Our algorithms demonstrate superior performance under various system configurations compared to state-of-the-art baselines. These results collectively underscore the potential of our proposed framework as a powerful new method for optimizing next-generation wireless networks.

With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements. The Space Computing Power Network (Space-CPN) offers a promising solution by providing onboard computing and extensive coverage capabilities for real-time inference. This paper presents a remote sensing artificial intelligence applications deployment framework designed for Low Earth Orbit satellite constellations to achieve real-time inference performance. The framework employs the microservice architecture, decomposing monolithic inference tasks into reusable, independent modules to address high latency and resource heterogeneity. This distributed approach enables optimized microservice deployment, minimizing resource utilization while meeting quality of service and functional requirements. We introduce Robust Optimization to the deployment problem to address data uncertainty. Additionally, we model the Robust Optimization problem as a Partially Observable Markov Decision Process and propose a robust reinforcement learning algorithm to handle the semi-infinite Quality of Service constraints. Our approach yields sub-optimal solutions that minimize accuracy loss while maintaining acceptable computational costs. Simulation results demonstrate the effectiveness of our framework.

Advancements in artificial intelligence and low-earth orbit satellites have promoted the application of large remote sensing foundation models (FMs) for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large FMs, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links. To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time to 33% of on-board training time.

Low earth orbit (LEO) satellite communication network is an important means to address coverage in remote areas and emergency communications. Constrained by the practical limitation of being unable to deploy ground gateway stations globally, China’s LEO satellite networks urgently need to reduce their dependence on terrestrial nodes. Computing and network convergence (CNC) technology, which leverages inter-satellite links and heterogeneous computing power to enable service pre-positioning, is a key approach to breaking through this bottleneck. Firstly, the development status of LEO satellite CNC was reviewed. Then, the main challenges faced by this technology were analyzed. Subsequently, driven by typical services, an “edge-end” bi-level CNC architecture for LEO satellites was designed. Finally, the development trends of this technology were discussed and potential future research directions were outlined.

Beam hopping (BH) is a pivotal technology for the dynamic reconfiguration of time-space-frequency resources in multi-beam satellite (MBS) systems, significantly enhancing the on-orbit efficiency and flexibility of payloads. However, the increasing demand for satellite communications leads to high concurrency of multi-tasking and potential single-satellite overload under limited onboard payload resources. Moreover, co-frequency interference (CFI) resulting from overlapping beam illumination and bandwidth reuse adversely affects communication reliability and stability. To address these overlooked issues, this paper proposes a collaborative multi-satellite BH (CMSBH) method for joint load balancing and interference avoidance. Specifically, we formulate the problem as a multi-objective optimization of throughput, delay fairness, and load balancing. Additionally, a novel multi-level interference penalty mechanism is introduced to handle intra-satellite, inter-satellite, and neighboring cell CFI. We then employ a centralized training and decentralized execution (CTDE) framework using the QMIX model to learn a globally coordinated policy suitable for lightweight onboard deployment. The effectiveness of the proposed method is verified through simulation experiments.

Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and noisy, and when DMRS configurations vary across deployment scenarios. This paper proposes DANCE (Diffusion-based Noise-Adaptive Null-space Channel Estimation), a diffusion-based channel estimator for OFDM systems. We formulate DMRS-aided channel estimation as a sparse linear inverse problem whose measurement operator is induced by the pilot pattern. The resulting range-null space decomposition separates the measurement-constrained range-space component from the unobserved null-space component, which is reconstructed through a learned diffusion prior. To avoid directly imposing noisy pilot samples as exact constraints, DANCE introduces a noise-adaptive posterior correction into the reverse diffusion process. The correction coefficient and the residual sampling variance are jointly calibrated according to the observation noise level, thereby reducing pilot-noise injection while retaining useful measurement information. We further design a conditional U-Net denoiser for complex-valued OFDM channel grids, where the real and imaginary components are represented as separate feature channels and downsampling is performed only along the subcarrier dimension. Simulations based on 5G NR tapped delay line (TDL) and clustered delay line (CDL) channel models show that DANCE achieves consistently lower normalized mean squared error (NMSE) than conventional estimators and diffusion-based posterior sampling methods under different signal-to-noise ratios, DMRS configurations, Doppler frequency shifts, and train-test distribution mismatches.

The evolution of unmanned aerial vehicles (UAVs) into embodied intelligent agents in the low-altitude economy is reshaping edge computing networks. However, the high mobility of UAVs induces severe topology dynamics, limiting the efficacy of traditional fully connected multiagent reinforcement learning because of dimensionality and credit assignment challenges. Furthermore, existing graph attention approaches neglect explicit communication boundary constraints, leading to mismatches between value evaluation and physical topology. To address these challenges, this paper proposes the spatial-aware graph attention multiagent twin delayed deep deterministic policy gradient (SAGA-MATD3) algorithm. By embedding a dynamic spatial masking mechanism based on the communication radius into the critic network, the proposed method enforces physical reachability constraints and attenuates extraneous noise. Simulation results demonstrate that SAGA-MATD3 significantly reduces service latency and improves fairness under an acceptable energy-consumption tradeoff, achieving a 37.1% improvement in convergence reward and enabling the self-organization of robust load-balanced mesh topologies.

This paper investigates a multi-satellite cooperative covert satellite communication (SatCom) scheme, where a positive covert rate is achieved in ultra-dense low Earth orbit (LEO) satellite constellations by exploiting both interference and directional uncertainty. Specifically, interference arises from aggregate sidelobe leakage from other satellite transmissions, while directional uncertainty stems from the random selection of the transmitting satellite among multiple accessible ones. To this end, we first propose a LEO satellite network model and formulate the corresponding hypothesis testing problem for the cooperative covert SatCom scheme. The power distribution of the aggregate interference is quantified and approximated using stochastic geometry. Next, by analyzing the detection error probability and outage probability under the impact of aggregate interference, we derive an approximate covert capacity expression for the case of a single accessible satellite, which maintains a positive covert rate even as the slot length approaches infinity. Furthermore, by leveraging directional uncertainty through hiding the signal’s angle of arrival, we analyze the multi-satellite cooperative covert SatCom scheme, leading to a concise approximate expression that reveals significant covert capacity improvement. Numerical simulations are performed to verify the superiority of the proposed scheme, suggesting that a positive covert capacity can be achieved with interference uncertainty and significantly enhanced by directional uncertainty as the number of satellites increases.

As an emerging paradigm in next-generation network architectures, satellites equipped with edge servers have attracted increasing attention. With the growing demand for low-latency and wide-area seamless coverage, they have emerged as a promising technology. However, their mobility and limited resources necessitate an efficient resource allocation and service migration strategy. Most existing studies have focused on Low Earth Orbit (LEO) satellite constellations, which typically handle low traffic volumes and overlook service diversity. Moreover, resource allocation and service migration are inherently interdependent problems that require joint optimization. To address this challenge, we propose a joint optimization model for resource allocation and service migration in a Medium Earth Orbit (MEO) rosette constellation. Our approach integrates a queuing framework for practical issues such as handover failures and packet loss. We optimize resource allocation and service deployment by formulating the problem as a Markov Decision Process (MDP) and employing an enhanced Proximal Policy Optimization (PPO)based algorithm. Simulation results validate that the proposed method outperforms existing approaches regarding system reward and service success rate.

Autonomous agents, including unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned surface vessels (USVs), and unmanned underwater vehicles (UUVs), are widely applied across diverse fields, such as environmental monitoring, logistics, exploration, and military applications, due to their ability to operate autonomously in complex environments. As tasks grow in complexity, there is a shift from individual autonomous agents to collaborative autonomous agent swarms (AASs). These swarms, connected through wireless links, leverage collective intelligence to perform more sophisticated tasks than individual autonomous agents can achieve alone. Deploying AASs introduces challenges in coordination, resource utilization, and adaptability, which traditional model-based methods struggle to address. Recent advancements in machine learning (ML), particularly distributed machine learning (DML) techniques such as federated learning (FL) and multi-agent reinforcement learning (MARL), offer promising solutions. These techniques enable autonomous agents to learn collaboratively without centralizing data, thereby preserving efficiency while adapting to dynamic environments. However, applying DML in AASs presents unique challenges due to autonomous agents' dynamic movement, unreliable communication links, harsh operating conditions, heterogeneity, and resource constraints. Therefore, this survey provides a comprehensive review of integrating DML into AASs. Specifically, we analyze four representative types of autonomous agents in a unified perspective examining their fundamental characteristics, communication models, and dynamic behaviors. Then, we discuss the limitations of basic ML models in AASs, highlighting the need for DML and the key requirements and metrics for its successful implementation. Furthermore, we explore recent advancements in FL and MARL as applied to AASs, highlighting use cases and key techniques. By identifying current technological progress and gaps in the literature, this survey offers valuable insights for researchers and practitioners and outlines potential directions for future research to enhance the capabilities of AASs through DML.