This paper investigates a multiple uncrewed aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system equipped with movable antenna (MA) arrays. To align with practical scenarios, we simulate the dynamic roaming of ground users and the three-dimensional deployment of UAVs in the airspace. We aim to maximize the total data rate by jointly optimizing key operational variables, including UAV trajectories, user association, antenna positions, and beamforming. This formulated problem is subject to constraints on transmission power and the sensing signal-to-noise ratio. To address the challenge of dynamically unknown state transitions due to user mobility, the original problem is decomposed into two steps and solved using different algorithms. First, we utilize the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to address the ground-to-air association problem, periodically updating clusters and re-associating during training. The clustering hotspots are used to suggest flight directions for the UAVs. Second, we develop the soft actor-critic algorithm to solve the joint optimization problem of UAV trajectories, antenna positions, and beamforming. Experimental results demonstrate that UAVs equipped with MA arrays outperform those with traditional fixed antenna arrays in ISAC systems, and the proposed optimization strategy effectively enhances communication rates while ensuring sensing performance.

Deep neural networks (DNNs) have been widely applied in mobile intelligent applications. However, their high computational complexity poses significant challenges for resource-constrained mobile devices. To address this issue, this paper proposes a multi-uncrewed aerial vehicle (UAV)-assisted mobile edge computing architecture tailored for DNN inference tasks. By hierarchically partitioning the DNN model and distributing different sub-tasks between local devices and aerial servers for collaborative processing, the system effectively reduces the computational burden on user terminals. Taking into account the factors such as unbalanced network load and limited UAV energy, a task migration mechanism is introduced to support resource coordination and load balancing among multiple UAVs. The aim is to minimize total weighted energy consumption through joint optimization of user-UAV association, DNN partitioning, UAV trajectory, task migration, and computing resource allocation. Due to the dynamic and complex nature of the resulting optimization problem, we model it as a Markov decision process, and a soft actor-critic with prioritized experience replay (SAC-PER) is proposed to solve it. Furthermore, we integrate convex optimization techniques into SAC-PER as a subroutine to allocate computing resources to enhance the learning efficiency. Simulation results show that the proposed method achieves faster convergence and reduces the total weighted energy consumption by up to 14.6% compared with baseline methods.

Reconfigurable intelligent surface (RIS) has emerged as a pivotal technology for enhancing wireless networks. Compared to terrestrial RIS deployed on building facades, aerial RIS (ARIS) mounted on quadrotor unmanned aerial vehicle (UAV) offers superior flexibility and extended coverage. However, the inevitable tilt and altitude variations of a quadrotor UAV during flight may lead to severe beam misalignment, significantly degrading ARIS’s performance. To address this challenge, we propose an Euler angles-based ARIS control scheme that jointly optimizes the altitude and trajectory of the ARIS by leveraging the UAV’s dynamic model. Considering the constraints on ARIS flight energy consumption, flight safety, and the transmission power of a base station (BS), we jointly design the ARIS’s altitude, trajectory, phase shifts, and BS beamforming to maximize the system sum-rate. Due to the continuous control nature of ARIS flight and the strong coupling among variables, we formulate the problem as a Markov decision process and adopt a soft actor-critic algorithm with prioritized experience replay to learn efficient ARIS control policies. Based on the optimized ARIS configuration, we further employ the water-filling and bisection method to efficiently determine the optimal BS beamforming. Numerical results demonstrate that the proposed algorithm significantly outperforms benchmarks in both convergence and communication performance, achieving approximately 14.4% improvement in sum-rate. Moreover, in comparison to the fixed-horizontal ARIS scheme, the proposed scheme yields more adaptive trajectories and significantly mitigates performance degradation caused by ARIS tilting, demonstrating strong potential for practical ARIS deployment.

Traditional video transmission systems assisted by multiple uncrewed aerial vehicles (UAVs) are often limited by computing resources, making it challenging to meet the demands for efficient video processing. To solve this challenge, this article presents a multi-UAV-assisted device-to-device (D2D) mobile edge computing system for the maximization of task offloading profits in video stream transmission. In particular, the system enables UAVs to collaborate with idle user devices to process video computing tasks by introducing D2D communications. To maximize the system efficiency, the article jointly optimizes power allocation, video transcoding strategies, computing resource allocation, and UAV trajectory. The resulting nonconvex optimization problem is formulated as a Markov decision process (MDP) and solved relying on the twin delayed deep deterministic policy gradient (TD3) algorithm. Numerical results indicate that the proposed TD3 algorithm performs a significant advantage over other traditional algorithms in enhancing the overall system efficiency.

In this article, we consider deploying multiple unmanned aerial vehicles (UAVs) to enhance the computation service of mobile edge computing (MEC) through collaborative computation among UAVs. In particular, the tasks of different types and service requirements in MEC network are offloaded from one UAV to another. To pursue the goal of low-carbon edge computing, we study the problem of minimizing system energy consumption by jointly optimizing computation resource allocation, task scheduling, service placement, and UAV trajectories. Considering the inherent unpredictability associated with task generation and the dynamic nature of wireless fading channels, addressing this problem presents a significant challenge. To overcome this issue, we reformulate the complicated nonconvex problem as a Markov decision process and propose a soft actor-critic-based trajectory optimization and resource allocation algorithm to implement a flexible learning strategy. Numerical results illustrate that within a multi-UAV-enabled MEC network, the proposed algorithm effectively reduces the system energy consumption in heterogeneous tasks and services scenarios compared to other baseline solutions.

Data compression technology is able to reduce data size, which can be applied to lower the cost of task offloading in mobile edge computing (MEC). This article addresses the practical challenges for robust trajectory and scheduling optimization based on data compression in the uncrewed aerial vehicle (UAV)-assisted MEC, aiming to minimize the sum energy cost of terminal users while maintaining robust performance during UAV flight. Considering the nonconvexity of the problem and the dynamic nature of the scenario, the optimization problem is reformulated as a Markov decision process (MDP). Then, a randomized ensembled double Q-learning (REDQ) algorithm is adopted to solve the issue. The algorithm allows for higher feasible update-to-data ratio, enabling more effective learning from observed data. The simulation results show that the proposed scheme effectively reduces the energy consumption while ensuring flight robustness. Compared to the PPO and A2C algorithms, energy consumption is reduced by approximately 21.9% and 35.4%, respectively. This method demonstrates significant advantages in complex environments and holds great potential for practical applications.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can expand the coverage of mobile edge computing (MEC) services by reflecting and transmitting signals simultaneously, enabling full-space coverage. The orientation of the STAR-RIS plays a crucial role in optimizing the gain of received and transmitted signals, and a rotatable STAR-RIS offers potential enhancement for MEC systems. This article investigates a rotatable STAR-RIS-assisted MEC system, operated under three protocols, namely energy splitting (ES), mode switching, and time switching. The goal is to minimize energy consumption for multiple moving user devices (UDs) through the joint optimization of STAR-RIS configurations, orientation, computation resource allocation, transmission power, and task offloading strategies. Considering the mobility of UDs, we model the original optimization problem as a sequential decision-making process across multiple time slots. The high-dimensional, highly coupled, and nonlinear nature makes it a challenging nonconvex decision-making problem for traditional optimization algorithms. Therefore, a deep reinforcement learning (DRL) approach is employed, specifically utilizing soft actor-critic algorithm to train the DRL model. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in both convergence speed and energy efficiency, while reducing energy consumption by up to 52.7% compared to the fixed STAR-RIS scheme. Among three operating protocols, the ES yields the best performance.

In this paper, we propose a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and computation network. Specifically, the treble-functional UAVs are capable of offering communication and edge computing services to mobile users (MUs) in proximity, alongside their target sensing capabilities by using multi-input multi-output arrays. For the purpose of enhance the computation efficiency, we consider task compression, where each MU can partially compress their offloaded data prior to transmission to trim its size. The objective is to minimize the weighted energy consumption by jointly optimizing the transmit beamforming, the UAVs’ trajectories, the compression and offloading partition, the computation resource allocation, while fulfilling the causal-effect correlation between communication and computation as well as adhering to the constraints on sensing quality. To tackle it, we first reformulate the original problem as a multi-agent Markov decision process (MDP), which involves heterogeneous agents to decompose the large state spaces and action spaces of MDP. Then, we propose a multi-agent proximal policy optimization algorithm with attention mechanism to handle the decision-making problem. Simulation results validate the significant effectiveness of the proposed method in reducing energy consumption. Moreover, it demonstrates superior performance compared to the baselines in relation to resource utilization and convergence speed.

For multiple unmanned-aerial-vehicles (UAVs)-assisted mobile-edge computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multitype tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multiagent Markov decision process and propose a multiagent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic algorithms.

The deployment of mobile edge computing services in LEO satellite networks achieves seamless coverage of computing services. However, the time-varying wireless channel conditions between satellite–terrestrial channels and the random arrival characteristics of ground users’ (GUs) tasks bring new challenges for managing the LEO satellite’s communication and computing resources. Facing these challenges, a stochastic computation offloading problem of joint optimizing communication and computing resources allocation and computation offloading decisions is formulated for minimizing the long-term average total power cost of the GUs and the LEO satellite, with the constraint of long-term task queue stability. However, the computing resource allocation and the computation offloading decisions are coupled within different slots, thus making it challenging to address this problem. To this end, we first employ the Lyapunov optimization to decouple the long-term stochastic computation offloading problem into the deterministic subproblem in each slot. Then, an online algorithm combining deep reinforcement learning and conventional optimization algorithms is proposed to solve these subproblems. Simulation results show that the proposed algorithm can achieve the superior performance while ensuring the stability of all task queues in LEO satellite networks.

Wireless power communication networks(WPCN) can be used in disaster scenarios to provide reliable communication and power supply, helping to improve disaster response efforts. Using reconfigurable intelligent surface(RIS) to assist WPCN can improve signal quality and extend the transmission distance where traditional infrastructure is damaged or unavailable. However, it is important to transmit an emergency response in disaster scenarios as soon as possible, which is little consideration before. In this paper, we propose a time allocation scheme whose key idea is to find the minimum time for disaster scenario information dissemination while optimizing the time threshold for downlink energy harvest to confirm uplink information transmit requirements. We propose a new optimization problem of minimizing the transmit time duration by jointly optimizing the RIS shifts, downlink time for energy havest, uplink time and power for information transmission, subject to the constraints on harvesting power at user, the phase shift module 1, and the minimum Quality of Service(QoS) constraints. We find that our proposed optimization problem is multi variables and non-convex. To solve this, we apply alternating optimization to divide it into two sub-problems. Then Successive Convex Approximation and penalty-based algorithm are introduced to solve the sub-problems, respectively. Simulation results show that we can find the least time for information transmission in disaster scenarios.

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

Wireless secret key generation has emerged as a promising technique for Internet-of-Things (IoT) systems to establish shared encryption keys between the server and legitimate mobile user. This article focuses on the use of multidomain joint information to achieve a high key generation rate (KGR) and the implementation of a reliable, low-complexity secret key generation mechanism for distributed antenna systems (DAS) with orthogonal-frequency division multiplexing (OFDM). We present a space-time-frequency channel state information (CSI)-based key generation scheme based on a two-step approach of adaptive link selection and stepwise decorrelation algorithms. The performance is evaluated in terms of KGR, key disagreement rate (KDR), randomness, and computational complexity by using both a standardized channel model and real-world measurements. Numerical results show that our proposed low-complexity algorithms effectively utilize the space-time-frequency CSI to multiply the KGR in both indoor and outdoor environments. Through adaptive link selection in DAS, the KDR is maintained within a correctable range, thereby ensuring the validity of generated keys in dynamic environments. Further applying stepwise decorrelation reduces the computational complexity by more than half while satisfying all eight key generation randomness tests in the NIST test suite.

With the increasing data traffic in the fifth-generation (5G) communication system, the 5G new radio extended to unlicensed bands (5G NR-U) has become a promising approach to relieve the heavy pressure on the cellular system. To achieve the efficient coexistence with the WiFi system and improve the efficiency of temporal, spectral and spatial resource utilization, we first divide the transmission space into two subspaces by leveraging spatial reuse, where the data transmitted by cellular user equipments (UEs) falls into one subspace and the data transmitted by Internet of Things (IoT) devices coexisting with WiFi users through power control is in the other subspace. Then, the coexistence among cellular UEs, IoT devices, and WiFi users is formulated as an optimization model with the aim of maximizing the cellular system throughput via the joint power and subchannel allocation under the interference constraint. Although the resulting optimization problem is a mixed-integer nonlinear programmming, we decompose it into two subproblems and develop an alternating iterative approach to effectively solve them. Also, the closed-form allocations of the power and subchannels are obtained. Simulation results confirm that the proposed scheme can improve the cellular system performance and guarantee the coexistence in the unlicensed band.

Caching popular contents at edge networks (such as small-cell base stations) has been proposed to deal with the ever-growing mobile traffic. At the meantime, recommendation system is able to shape user demands for further prompting caching gain. In this paper, we study a multi-antenna multi-cell edge network employing transmit beamforming with caching-aware recommendation and user association. We first establish a framework for the joint problem of beamforming, user association, content caching and recommendation to minimize the content transmission delay of mobile users, by specifying a set of necessary conditions for all four component functions of the network. The resulting optimization problem corresponds to a non-convex, multi-timescale, and mixed-integer programming problem, which is hard to handle. To deal with the difficulty in solving the joint optimization problem by the direct formulation, we equivalently decompose it into three sub-problems. Then, we develop a computationally-efficient iterative algorithm to obtain the sub-optimal solution, where the three subproblems are tackled iteratively. Simulation results are conducted to demonstrate that the proposed algorithm can obtain lower transmission delay than baseline schemes.

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

Research and processing development on satellite communications has strongly re-emerged in recent years. Following the prosperity of various wireless services provided by satellite communications, the security issue has raised growing concerns since the space information network is susceptible to be eavesdropped by illegal adversaries in such a large-scale wireless network. Recently, the physical-layer security (PLS) has emerged as an alternative security paradigm that explores the randomness of the wireless channel to achieve confidentiality and authentication. The success story of the PLS technique now spans a decade and thrives to provide a layer of defense in satellite communications. With this position, a comprehensive survey of satellite communications is conducted in this article with an emphasis on PLS. We first briefly introduce essential background and the view of the satellite Internet of Things (IoT), as well as discuss related research challenges faced by the emerging integrated network architecture. Then, we revisit the most popular satellite channel model influenced by many factors and list the commonly used secrecy performance metrics. Also, we provide an exhaustive review of state-of-the-art research activity on PLS in satellite communications, which we categorize by different architectures including land mobile satellite communication networks, hybrid satellite-terrestrial relay networks, and satellite-terrestrial integrated networks. In addition, a number of open research problems are identified as possible future research directions.

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication toward 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains, including physical layer, network layer, and joint communication, computing, and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

This paper develops a tractable analysis framework to evaluate the reliability and security performance of cooperative non-orthogonal multiple access (co-NOMA) in cognitive networks, where both a primary base station (PBS) and a NOMA-strong primary user (PU) send confidential messages to multiple uniformly distributed PUs in the presence of randomly located external eavesdroppers. For constricting the interference to the PUs imposed by cognitive femto base stations (CFBSs), a mobile association scheme is introduced. Moreover, an eavesdropper-exclusion zone is introduced around the PBS for improving the secrecy performance of the primary networks. To characterize the security-reliability tradeoff of the considered network, we first derive the activation probability of CFBSs and the conditional probability density function associated with the distance between the relay user and other PUs. Then, the connection outage probability (COP) and the secrecy outage probability (SOP) of each PU with NOMA (co-NOMA) or non-cooperative NOMA (nco-NOMA) are separately derived to obtain the overall COP and SOP in the primary networks. Finally, the tradeoff between COP and SOP with co-NOMA (identified as transmission SOP) is investigated for simultaneously reflecting the security and reliability. Numerical results demonstrate the performance improvements of the proposed co-NOMA scheme in comparison to that of the nco-NOMA scheme in terms of different parameters. Furthermore, the security-reliability tradeoff performance of co-NOMA is shown.

Unmanned Aerial Vehicles (UAVs) communications have received increasing attention in both military and civilian applications due to low cost and ease of deployment. Security is an unavoidable yet challenging issue during the data transmission process of communication networks. In this paper, we concentrate on the resource allocation in secure relay network assisted by a UAV in the presence of multiple eavesdroppers. Our target is to maximize the secrecy rate by jointly designing the transmit beamformer and artificial noise subject to the transmit power constraint of UAV. The resulting optimization problem is highly intractable and the key observation is that the original optimization problem can be equivalently transformed into a two- level problem. In particular, the inner-level problem can be solved by exploiting Semi-Definite Relaxation (SDR) and Charnes-Cooper transformation techniques, and the outer-level problem is handled by performing one-dimensional algorithm. Also, the tightness of the rank-relaxation is analyzed. Finally, simulation results are provided to validate the performance of our proposed scheme.