Unmanned aerial vehicle (UAV) swarms are essential for mission-critical aerial Internet of Things (IoT) applications. However, reliable multi-hop routing in these swarms is severely challenged by highly dynamic topologies and partial, asynchronous observations. Furthermore, conventional training data poorly represent rare but decisive disruptions, such as terrain occlusion, no-fly-zone detours, and congestion bursts. To address these issues, this paper proposes a deployment-aligned robust learning framework organized into three functional layers. At the policy layer, we propose the Multi-Agent Opportunistic (MAO) routing method, learned via a Belief Graph-based MAPPO (BG-MAPPO) algorithm. To overcome partial and stale observations, BG-MAPPO constructs a local belief graph encoding the dynamic neighborhood state, which is then used to jointly determine the optimal forwarder-set size and a diversity-aware routing distribution. At the training layer, the policy is optimized inside a terrain-aware, physics-calibrated Digital Twin Network (DTN). To prevent overfitting to limited training distributions, a diffusion-based generator (FD3M) enriches the training data with trajectory-and-flow samples covering critical corner cases. At the validation layer, large-scale simulations show that MAO reduces end-to-end delay by 27–34% compared to representative baselines. The policy also incurs modest operational overhead, maintaining millisecond-level inference and bounded signaling. Finally, hardware-in-the-loop (HIL) experiments confirm that the DTN accurately predicts physical execution, with relative gaps of only 4% in packet delivery ratio and 7% in delay, effectively bridging the sim-to-real gap for robust UAV-swarm networking.

Path planning is a fundamental application of an unmanned aerial vehicle (UAV) swarm. Performing such a task in a complex mountain environment with a wind field would encounter challenges such as the sim-to-real (simulation-to-reality) gap. In this paper, we present a digital twin (a virtual replica of the physical system)-assisted path-planning framework for a UAV swarm with two phases: global path planning and real-time trajectory planning. For global path planning, we develop an optimization model that combines the constraints of a single UAV and the swarming rules, while also accounting for wind effects. To solve the optimization model, we improve the polar lights optimization (PLO) algorithm via multiple strategies (named PLOM), enhancing the initialization, exploitation, exploration, and equilibration processes. The major improvement strategies consist of opposition-based learning with refraction and elite, the logarithmic spiral motion, the Cauchy-Gaussian operator, and sine cosine perturbation. We construct a realistic geographical simulation environment based on a digital elevation model (DEM) and dominant wind, and design controlled experiments under different conditions of UAVs, threats, waypoints, and wind speeds. The simulation results demonstrate that the PLOM algorithm always achieves the best solution in swarm path planning scenarios with different complexities. Meanwhile, the PLOM algorithm has the greatest robustness with nearly the shortest runtime.

With the rapid development of low-altitude economies, unmanned aerial vehicle (UAV) swarm has attracted growing interest for cooperative target search. However, most existing studies focus on static targets and assume UAVs operate at a single horizontal altitude, limiting their practical applicability. This paper proposes a novel multi-UAV cooperative search framework for moving targets based on multi-agent deep reinforcement learning (MADRL). By coordinating UAVs across high, medium, and low-altitude layers, the system achieves improved search efficiency through altitude-adaptive operations. We further introduce a revisit-time compensation mechanism to enhance detection performance for moving targets in a multi-layer UAV swarm. To address the challenges of slow convergence and sparse feedback in MADRL, we propose hybrid-reward-based value decomposition networks (HRVDN) algorithm that integrates dense local rewards with sparse global rewards, accelerating learning while encouraging agents to collect high-value information. Simulation results demonstrate that the proposed approach outperforms existing methods in terms of target search rate and area coverage.

Digital twin network (DTN) technology provides significant support for intelligent applications of unmanned aerial vehicle (UAV) swarms. However, related research focuses on DTN applications and lacks attention to the construction and maintenance of high-fidelity digital twin (DT) models. In this paper, we first develop a DT simulation platform for UAV swarms. Then, a dynamic data-driven DT model calibration scheme is proposed on the example of the most fundamental flight state of a UAV. The scheme utilizes parameter identification to estimate offline the key parameters of the measurement model and system deviations. Furthermore, the flight state is corrected online utilizing data assimilation actuated on the actual and virtual data. Simulation experiments on the DT simulation platform are conducted, and the effects of data sampling rate on calibration accuracy and computational load are analyzed. The results demonstrate that parameter identification and data assimilation significantly improve the fidelity of the DT model in terms of the optimal sub-pattern assignment (OSPA) metric to different degrees.

As an advanced framework, the digital twin network (DTN) provides effective management and decision support for autonomous aerial vehicle (AAV) swarms and has become a recent research hotspot. The effectiveness of many DTN applications relies on the assumption that high-fidelity digital twin (DT) models exist and are readily available. However, constructing such high-fidelity DT models of AAV swarms is a challenging task, especially in complex and dynamic environments. Despite its importance, there is a notable lack of research focused on the construction of high-fidelity DT models specifically for AAV swarms. This study proposes a dynamic data-driven approach for constructing and calibrating DT models of AAV swarms to achieve long-term consistency with real-world AAV behaviors. The method leverages parameter identification to estimate key parameters of DT models and data assimilation to refine and calibrate the model. It can provide high-fidelity DT AAV models for artificial intelligence model training and facilitate AAV swarm DTN from concept to real application. Additionally, this article developed a DT simulation platform for AAV swarms, validating the proposed method through software-in-the-loop simulations and physical testing. Results indicate that the optimal subpattern assignment metric decreases by an average of 79.2% after calibration, significantly improving the DT model’s fidelity.

Digital Twin (DT) technology represents a cutting-edge methodology that digitally maps physical entities with high fidelity, leading to the Digital Twin Network (DTN) through its integration with network technologies. DTN establishes bidirectional communication between virtual and physical spaces, enabling real-time monitoring, dynamic optimization, and precise control of physical networks. This addresses challenges posed by network expansion and service diversification, revolutionizing the management and optimization of complex network systems. Despite its potential, DTN implementation remains challenging, with research still nascent and lacking detailed guidelines. This paper aims to bridge this gap by presenting a comprehensive survey of the reference architecture for real-world DTN implementation and its key enabling technologies. It begins by defining the conceptual foundation of DTN and reviewing related architectural studies. This is followed by the proposal of a universal and scalable modular DTN architecture, encompassing the physical layer, data layer, DT model layer, and service layer. We then explore the critical enabling technologies required for implementing this architecture and analyze applications enhanced by DTN. Notably, We propose a five-level digital twin model evolution taxonomy framework that systematically reveals the evolution path from basic mapping to ultra-high-fidelity autonomous inference. This framework provides a structured evaluation benchmark for optimizing and advancing digital twin models. Finally, we discuss the primary open issues in DTN, offering theoretical and practical guidance for future research in this field.

Cooperative target search (CTS) technology is highly desirable in various multi-autonomous aerial vehicle (AAV) applications. However, searching for unknown targets in a dynamic threatening environment is a challenging problem, especially for AAVs with limited sensing range and communication capabilities. Besides, traditional searching methods lack scalability and efficient collaboration among the AAV swarm in dynamic environments. In this work, a digital twin (DT)-enabled distributed CTS approach was presented for AAV swarms and achieving sim-to-real transfer. Specifically, a new scalable multi-agent reinforcement learning (MARL) based algorithm called SAMARL is adopted to improve effectiveness and adaptability, combining a multi-head attention mechanism. In SAMARL, a scalable observation space with graph representation and an environmental cognition map is designed to thoroughly consider the target search rate, area coverage, and safety assurance. Then, a DT-driven training framework is proposed to facilitate the continuous evolution of MARL models and address the tradeoff between training speed and environment fidelity. Furthermore, we innovatively develop a distributed AAV swarm digital twin cooperative target search validation system, including real flight control, communication simulation tools, and a 3D physics engine. Extensive simulations validate its superiority compared to state-of-the-art strategies. More importantly, we also conduct real-world flight experiments on different scale mission areas and AAV swarms, further demonstrating the generalization and scalability of trained models.

Over the past decade, unmanned aerial vehicles (UAVs) have been widely applied in many areas, such as goods delivery, disaster monitoring, search and rescue etc. In most of these applications, autonomous navigation is one of the key techniques that enable UAV to perform various tasks. However, UAV autonomous navigation in complex environments presents significant challenges due to the difficulty in simultaneously observing, orientation, decision and action. In this work, an efficient state-decomposition deep deterministic policy gradient algorithm is proposed for UAV autonomous navigation (SDDPG-NAV) in 3-D complex environments. In SDDPG-NAV, a novel state-decomposition method that uses two subnetworks for the perception-related and target-related states separately is developed to establish more appropriate actor networks. We also designed some objective-oriented reward functions to solve the sparse reward problem, including approaching the target, and avoiding obstacles and step award functions. Moreover, some training strategies are introduced to maintain the balance between exploration and exploitation, and the network is well trained with numerous experiments. The proposed SDDPG-NAV algorithm is capable of adapting to surrounding environments with generalized training experiences and effectively improves UAV’s navigation performance in 3-D complex environments. Comparing with the benchmark DDPG and TD3 algorithms, SDDPG-NAV exhibits better performance in terms of convergence rate, navigation performance, and generalization capability.

To face the challenges in emergency scenarios, a multitask and multiobjective optimization algorithm for computation offloading and relay communication is investigated for the air-ground integrated networks, composed of unmanned aerial vehicles (UAVs), emergency vehicle users (EVUs) and ground sensor nodes (GSNs). We propose an HFL-DDQN algorithm, which combines horizontal federated learning (HFL) with double deep$Q$-network (DDQN). First, UAVs are separated into two clusters according to the services they provide, i.e., edge computing or relay communication. Next, the optimization problems are formulated for two types of services, respectively. For the computation offloading tasks of EVUs, the optimization objective is to minimize the weighted sum of delay and energy consumption. For the sensor data transmission of GSNs, the optimization objective is to maximize the minimum rate of relay links. We define the total cost of the system as the sum of two types of services. Then, federated aggregation is used to joint training the global neural networks model without sharing raw data. Furthermore, the DDQN is improved by adopting prioritized experience replay to achieve better convergence. The simulation results show that the proposed HFL-DDQN algorithm not only outperforms the state-of-the-art baselines in terms of the system cost but also promotes the generalization in execution process, which is especially applicable to the rescue scene under accidents.

Over the past decades, unmanned aerial vehicles (UAVs) have been widely used in both military and civilian fields. In these applications, flocking motion is a fundamental but crucial operation of multi-UAV systems. Traditional flocking motion methods usually designed for a specific environment. However, the real environment is mostly unknown and stochastic, which greatly reduces the practicality of these methods. In this article, deep reinforcement learning (DRL) is used to realize the flocking motion of multi-UAV systems. Considering that the sim-to-real problem restricts the application of DRL to the flocking motion scenario, a digital twin (DT)-enabled DRL training framework is proposed to solve this problem. The DRL model can learn from DT and be quickly deployed on the real-world UAV with the help of DT. Under this training framework, this article proposes an actor–critic DRL algorithm, named behavior-coupling deep deterministic policy gradient (BCDDPG), for the flocking motion problem, which is inspired by the flocking behavior of animals. Extensive simulations are conducted to evaluate the performance of BCDDPG. Simulation results show that BCDDPG achieves a higher average reward and performs better in terms of arrival rate and collision rate compared with the existing methods.

In this paper, the resource allocation for vehicle-to-everything (V2X) underlaying 5G cellular mobile communication networks is considered. The optimization problem is modeled as a mixed binary integer nonlinear programming (MBINP), which minimizes the interference to 5G cellular users (CUs) subject to the quality of service (QoS), the total available power, the interference threshold, and the minimal transmission rate. To achieve that, the original MBINP is decomposed into three steps: transmission power initialization, subchannel assignment, and power allocation. Firstly, the minimum transmission power required by the V2X users (VUs) is set as the initial power value. Secondly, the Hungarian algorithm is used to obtain the appropriate subchannel. Finally, an optimization mechanism is proposed to the power allocation. Simulation results show that the proposed algorithm can not only ensure the minimal transmission rate of VUs but also further improve the CUs’ channel capacity under the premise of guaranteeing the QoS of the CUs.

In beyond fifth-generation (B5G) cellular internet of things (IoT) networks, energy supply and data aggregation of a massive number of devices are two vitally challenging issues. To address these challenges, we propose a wireless powered MIMO over-the-air computation (AirComp) design framework. Firstly, wireless power transfer (WPT) is utilized to charge massive IoT devices simultaneously by exploiting the open nature of wireless broadcast channel. Then, AirComp is adopted to reduce latency of massive data aggregation via exploring the superposition property of wireless multiple-access channel. To realize efficient convergence of energy supply and data aggregation in practical IoT networks, a robust design algorithm is provided by jointly optimizing beamforming of both WPT and AirComp. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

Radio frequency identification (RFID) has been widely used in various areas, such as logistics, healthcare, manufacture and so on. However, tag collision problem greatly affects the performance of RFID systems by reducing bandwidth utilization and increasing identification delay etc. In this paper, we propose a dynamic query tree anti-collision (DQTA) protocol, which dynamically adjusts the number of subgroups of each collision slot. Based on the number of consecutive colliding bits in tag response k, DQTA splits colliding tags into 2^k subgroups. Dividing colliding tags into more appropriate subgroups, the number of collision slots and transmitted message bits are effectively reduced. Comparing with the most related state-of-art works, the proposed DQTA protocol can identify tags with less time and fewer transmitting message bits.

Directional antennas have numerous advantages over omnidirectional antennas in CSMA/CA ad hoc networks. However, estimating the available bandwidth of a flow in the medium access process in such a network is very challenging. In this paper, we present a passive available bandwidth estimation algorithm for directional ad hoc networks, termed PABE-D. The estimation process consists of two phases, i.e., the preliminary and refined estimation phases. In the preliminary phase, the available transmission/reception duration in each beam of the node and the available bandwidth of the directional link are obtained by analyzing the available duration of both the sender and receiver. In the refined phase, we analyze the effect of the directional hidden terminal and deafness problems to further improve the estimate accuracy. The performance of our proposed PABE-D algorithm is evaluated in two typical topologies, i.e., the parallel and random grid topologies. The simulation results demonstrate that our algorithm can effectively estimate the available bandwidth in directional ad hoc networks.

In this paper, we investigate the issue of sustainable communications for beyond fifth-generation (B5G) cellular internet of things (IoT) networks under adverse but practical conditions. A massive number of simple IoT devices without batteries harvest requisite energy from a part of the received signal. A design framework including channel state information (CSI) acquisition, signal construction, information decoding and energy harvesting, is first provided for sustainable communications of massive IoT. Then, based on the proposed design framework, we reveal the impacts of practically adverse factors, e.g., channel uncertainty, successive interference cancellation (SIC) and non-linear energy harvesting, on the performance of B5G cellular IoT. Furthermore, in order to effectively alleviate the impacts of these adverse factors, an outage-constrained robust algorithm is designed to maximize the overall performance of sustainable B5G cellular IoT. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

In this study, the problem of energy‐efficient power allocation (EEPA) for secrecy wireless information and power transfer in a massive multiple‐input multiple‐output relay aided secure communication system is well addressed. The relay forwards the signal sent from a source to a legitimate destination with the harvested energy based on a decode‐and‐forward relaying protocol, while a passive eavesdropper intends to intercept the message. The authors first derive a closed‐form expression for the secrecy energy efficiency of the considered system under practical conditions, i.e. no instantaneous eavesdropper channel state information (CSI) and only imperfect legitimate CSI. Then, they propose an EEPA scheme for maximising the secrecy energy efficiency. Finally, simulation results validate the effectiveness of the proposed scheme.

In this paper, we address the problem of optimal power allocation at the relay in two-hop secure communications. In order to solve the challenging issue of short-distance interception in secure communications, the benefit of large-scale MIMO (LS-MIMO) relaying techniques is exploited to improve the secrecy performance significantly, even in the case without eavesdropper channel state information (CSI). The focus of this paper is on the analysis and design of optimal power allocation for the relay, so as to maximize the secrecy outage capacity. We reveal the condition that the secrecy outage capacity is positive, prove that there is one and only one optimal power, and present an optimal power allocation scheme. Moreover, the asymptotic characteristics of the secrecy outage capacity is carried out to provide some clear insights for secrecy performance optimization. Finally, simulation results validate the effectiveness of the proposed scheme.

In this paper, we consider a large scale multiple input multiple output (LS-MIMO) relaying system, where an information source sends the message to its intended destination aided by an LS-MIMO relay, while a passive eavesdropper tries to intercept the information forwarded by the relay. The advantage of a large scale antenna array is exploited to improve spectral efficiency and enhance wireless security. In particular, the challenging issue incurred by short-distance interception is well addressed. Under very practical assumptions, i.e., no eavesdropper channel state information (CSI) and imperfect legitimate CSI at the relay, this paper gives a thorough secrecy performance analysis and comparison of two classic relaying techniques, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, asymptotical analysis is carried out to provide clear insights on the secrecy performance for such an LS-MIMO relaying system. We show that under large transmit powers, AF is a better choice than DF from the perspectives of both secrecy performance and implementation complexity, and prove that there exits an optimal transmit power at medium regime that maximizes the secrecy outage capacity.

Abstract This paper introduces the limited feedback precoding into the distributed antenna system and proposes to adapt the predetermined orthogonal space time block codes to the available channel state information at the transmitter. The optimal representation of precoding information, namely the precoder, with least bits therefore becomes the key problem. Inspired by the characteristics of the distributed antenna system, we focus our work on the precoder construction, adaptable in response to the large and small scale fading, such that the symbol error probability is significantly reduced over that of a fixed, non‐adaptive, independent and identically distributed precoder codebook design. Furthermore, a suboptimal power‐loading strategy is presented by minimizing the derived tight upper bound on the average pairwise error probability of the precoded orthogonal space time block codes, which approaches the optimal performance asymptotically without additional channel knowledge other than the available feedback information. We prove that the proposed precoded orthogonal space time transmission scheme can achieve full diversity order. In particular, the robustness of our proposed transmission scheme to channel estimation error and feedback delay is respectively investigated in some detail, and numerical results show that it obviously improves the link reliability and obtains substantial gains even with few bits of feedback in comparison with conventional antenna selection scheme. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we study the problem of physical layer security in a large-scale multiple-input multiple-output (LS-MIMO) relaying system. The advantage of LS-MIMO relaying systems is exploited to enhance both wireless security and spectral efficiency. In particular, the challenging issue incurred by short interception distance is well addressed. Under very practical assumptions, i.e., no eavesdropper's channel state information (CSI) and imperfect legitimate channel CSI, this paper gives a thorough investigation of the impact of imperfect CSI in two classic relaying systems, i.e., amplify-and-forward (AF) and decode-and-forward (DF) systems, and obtain explicit expressions of secrecy outage capacities for both cases. Finally, our theoretical claims are validated by the numerical results.

Transmit power and feedback bandwidth are two limited and interrelated resources which are crucial to system performance in wireless channel with feedback, so it is necessary to maximize their utilization efficiencies in the joint sense. In this paper, we investigate the inherent relationship between transmit power and codebook size in multiuser limited feedback MISO system by making use of Grassmann line packing theory, as an effort to provide an insight on how to jointly distribute the two resources to fulfill the diverse requirements. Then, the impact of feedback delay on the tradeoff relation is characterized in detail, and we find that even with relatively small delay, there is considerable performance loss with respect to the ideal case. Thereby, much more transmit power or feedback bandwidth should be consumed to achieve the same performance target. Finally, the theoretical claims are validated by numerical results.