The unmanned aerial vehicle (UAV) has been widely applied for data collection in Internet of things (IoT) networks due to its advantages of rapid deployment, flexible configuration, and high mobility. Therefore, we propose a multi-UAV cooperative path planning architecture based on machine learning algorithms. This architecture enhances the overall energy efficiency and task completion effectiveness of the data collection system by incorporating communication range constraints and co-optimizing the flight and hovering processes. Specifically, an optimization model is established with the objective of minimizing the weighted task completion time and total energy consumption, which is difficult to directly solve because of the high dimensional and strongly coupled characteristics. To deal with this problem, a multi-UAV cooperative path planning algorithm based on improved clustering and hybrid genetic algorithm (GA) and ant colony optimization (ACO) is proposed. First, the IoTDs are preliminarily clustered using the improved K-means algorithm, and the results are adaptively adjusted by incorporating the maximum UAV communication distance constraint. Second, considering the differences in data volume and priority among nodes within a cluster, a cluster head (CH) selection mechanism based on weighted normalized scoring is designed. Furthermore, the multi-UAV path planning problem is transformed into a traveling salesman problem for solution via a “flight-hover-flight” strategy. Simulation results demonstrate that, compared to traditional baseline schemes, the proposed algorithm fully leverages the positive feedback regulation of ant colony pheromones and the global search capability of the GA, achieving significant advantages in convergence speed and solution quality. Besides, the system’s comprehensive cost can be reduced by up to approximately 15%.

Accompanied by the booming development of artificial intelligence technology, deep learning has been widely used in many fields of cancer, specially in cancer prediction. The dependence on training data for deep learning naturally raises privacy leakage concerns. Federated learning is a promising solution to these issues, but it is limited by the computational capacity of clients. Therefore, this paper has proposed a Split Federated Learning (SFL)-based breast cancer prediction scheme with dynamic client selection aggregation, by aggregating data from multiple healthcare organizations under the premise of privacy protection. First, the split learning is integrated into federated learning to predict breast canner, which contributes to protecting data privacy and reducing the computational burden on client devices. Then, the dynamic client selection aggregation is devised to lower the aggregation communication costs and improve communication efficiency by utilizing Long Short-Term Memory (LSTM) networks to evaluate the availability of each client devices in participating training. Finally, we have conducted extensive experiments on the CAMELYON16 dataset to evaluate the performance of our proposed scheme, and the experimental results have shown that our proposed scheme can converge faster and achieve the lower communication costs.

The integration of large language models (LLMs) with mobile edge computing (MEC) systems presents a novel approach to enhancing vehicle-to-everything (V2X) connected autonomous driving. This study aims to address the prevalent challenges in multi-model sensor data fusion, such as latency, privacy preservation, and the need for dynamic adaptation to evolving environmental conditions, by leveraging real-time data from LiDAR sensors. We propose an LLM-based framework to improve V2X driving assistance systems’ operational efficiency, safety, and reliability, where pictures and image recognition work as integrated data from multiple sensors to train various vehicle and lane detection models. Based on the benefits of federated learning, i.e., distributed at each MEC server and optimising models accordingly, these training models can avoid the data privacy issue in V2X driving assistance implementation. The application of generated test data significantly improves the success rate of the lane detection feature and pedestrian detection, by 95% and 85%, respectively. The experiment results demonstrate that our proposed framework is effective and feasible.

Reconfigurable Intelligent Surface (RIS) has emerged as a pivotal technology in the development of sixth-generation (6G) mobile communication systems. In this paper, a wireless communication system assisted by RIS in the air is studied, in which RIS is installed on the UAV as a mobile relay (RIS-UAV) to re-establish the line-of-sight (LoS) link between the base station and users. Considering the active beamforming vector of base station, UAV trajectory and phase shift of RIS, a joint optimization scheme is proposed to maximize the energy efficiency of the system. Aiming at this optimization problem, a dual DDQN structure algorithm (PER-TDDQN) based on priority experience replay mechanism is proposed to solve this multi-objective joint optimization problem. The simulation results show that the proposed algorithm can effectively improve the energy efficiency of the system.

This paper addresses hierarchical service placement, UAV trajectory design, access control, and resource scheduling issues in multi-access edge computing (MEC)-enabled time division multiple access (TDMA)-based air-ground integrated networks (AGINs) comprising multiple unmanned aerial vehicles (UAVs) and a ground base station. UAVs perform environmental sensing and data collection, but their limited onboard computing and energy resources pose challenges for handling heterogeneous, delay-sensitive tasks. To tackle this, we propose a two-tier actor-based deep reinforcement learning (DRL) algorithm for multi-timescale decision making. A high-level deep Q-Network (DQN) agent determines frame-level service placement, while a low-level improved deep deterministic policy gradient (IDDPG) agent manages time-slot-level UAV trajectory, access control, bandwidth allocation, and transmission power. A constraint-aware reward mechanism ensures learning stability and feasibility. Simulations show that the proposed method outperforms baseline schemes in task success rate, energy efficiency, and scheduling fairness.

In vehicular communication and mobile edge computing (MEC) networks, limited storage resources and challenges related to vehicles data security pose significant concerns. To address these issues while reducing communication latency and enhancing network security, blockchain technology is introduced. Additionally, deep reinforcement learning (DRL) is leveraged to optimize content caching strategies. By formulating a Markov decision process (MDP) model and applying the proximal policy optimization (PPO) algorithm, efficient cache management and optimal resource allocation are achieved. Simulation results demonstrate that the proposed approach effectively improves cache hit rates and significantly reduces latency in vehicular communication environments, yielding superior performance.

In this study, we propose a mobile edge computing (MEC)-enabled vehicular Metaverse system designed for augmented reality (AR) services, where vehicles on the road can access the Metaverse service through nearby Metaverse service providers (MSPs) equipped with MEC servers. In this system, vehicles are charged for their use of computational and communication resources. Due to varying positions and viewing angles, vehicles may have different content preferences. To minimize cost while ensuring optimal quality of experience (QoE), we formulate an optimization problem that adjusts content resolution and resource allocation to match individual vehicle needs. To address this optimization problem, we introduce a deep reinforcement learning (DRL) algorithm integrated with active inference theory to solve the decision-making problem with the performance of low latency and high efficiency. Simulation results demonstrate that our proposed scheme outperforms comparative algorithms in comprehensive performance, providing an effective solution for optimizing AR-enabled vehicular Metaverse systems.

This paper investigates a covert communication system enhanced by a reconfigurable intelligent surface (RIS), where an unmanned aerial vehicle (UAV) serves as the transmitter while a Warden moves freely within a certain range. To enhance both communication covertness and energy efficiency, we jointly optimize the UAV trajectory and RIS phase shifts to maximize system energy efficiency. The optimization problem is formulated as a Markov decision process (MDP), and a reinforcement learning-based approach, LSTM-DDQN, is proposed to exploit deep reinforcement learning (DRL) in dynamic environments. By integrating long short-term memory (LSTM) to capture temporal dependencies and double deep Q-network (DDQN) to mitigate Q-value overestimation, the proposed algorithm enhances stability and decision-making effectiveness. Simulation results demonstrate that LSTM-DDQN significantly improves energy efficiency while effectively reducing the risk of detection compared to conventional DDQN, validating its superiority in RIS-assisted UAV covert communication.

With the rapid advancements in technologies such as autonomous driving, intelligent robotics, unmanned aerial vehicles, and low-altitude economy applications, the demand for high-precision and highly robust positioning systems has become increasingly critical. Multi-sensor fusion positioning has gained widespread adoption due to its superior accuracy and robustness, with factor graph optimization receiving particular attention for its efficient modeling of multi-source constraints. However, traditional approaches often assume fixed noise levels, neglecting the dynamic variations of uncertainty in complex environments. To address this limitation, this paper proposes an uncertainty-aware GNSS/IMU/Vision multi-sensor fusion positioning algorithm. Recognizing the distinct error characteristics of GNSS and IMU, the error state Kalman filter (ESKF) is first employed to develop dynamic uncertainty models for each sensor. Building upon this, an adaptive weighting factor based on the covariance trace is introduced to apply uncertainty weighting to the GNSS/IMU data, thereby mitigating the impact of error interference during high-noise periods. Finally, the weighted multi-source data, along with visual feature observations, are incorporated into the factor graph optimization framework, enabling global state estimation within a sliding window. The proposed method is validated using the GVINS public dataset, and experimental results demonstrate its superior performance in challenging low-altitude economy scenarios, such as weak GNSS signals and significant IMU drift. Compared to traditional factor graph optimization algorithms, the proposed method improves positioning accuracy by 33% and reduces velocity error by 40%.

In the dynamic and demanding 6 G IoT environments, UAV-enabled Mobile Edge Computing (MEC) systems face significant challenges such as varying communication conditions, energy constraints, and high task demands. To address these challenges effectively, we propose a novel optimization framework that integrates Reconfigurable Intelligent Surfaces (RIS) and employs the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The primary goals of this framework are to minimize latency, maximize throughput, and ensure energy efficiency by dynamically coordinating UAV trajectories, task offloading decisions, and RIS phase shifts. Through extensive simulations, we demonstrate that the proposed framework significantly reduces delay and improves throughput compared to traditional methods, highlighting the substantial benefits of combining RIS with UAV-MEC systems in dynamic IoT scenarios. These findings suggest that the integration of RIS can effectively enhance system performance and adaptability in next-generation wireless networks, providing a promising approach to tackle the complex challenges of 6G IoT environments.

This paper proposes a novel framework for downlink multi-user cluster communications in non-orthogonal multiple access (NOMA) systems assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). We focus on the energy splitting (ES) protocol and account for practical issues such as hardware impairments (HIs) and error propagation from imperfect successive interference cancellation (I-SIC). The objective is to maximize the total user throughput by jointly optimizing user clustering, dynamic decoding order, base station active beamforming, and the STAR-RIS’s transmission and reflection beamforming. Furthermore, to address user mobility„ we design an efficient two-step algorithm: first, K-means is used for periodic user clustering, followed by proximal policy optimization (PPO), a deep reinforcement learning(DRL) algorithm, is applied to dynamically solve the aforementioned joint optimization problem. Simulation results show that the proposed algorithm significantly outperforms baseline DRL approaches in total throughput and that the STAR-RIS-NOMA system significantly exceeds conventional RIS-NOMA and OMA systems in performance.

For the Internet of things (IoT) network, the integrated localization and communication (ILAC) is expected to provide high localization and communication performance simultaneously. However, the existing research to evaluate the performance of ILAC systems fails to reveal the fundamental performance of ILAC systems in practical IoT network topology analytically. In this paper, we develop a unified analytical ILAC framework using stochastic geometry. We then validate the theoretical results obtained from the proposed analytical framework with the simulation results via extensive Monte Carlo simulations. We further analyse the communication coverage and localization coverage probability with respect to the network density, time-frequency-power domain resource allocation, and communication throughout and localization threshold. Finally, based on the ILAC simulation results, we reveal design guidance for ILAC systems. Specifically, we observe the fundamental trade-off between localization and communication performance attributed to the time-frequency-power domain resource allocation. Network density positively affects the ILAC performance, while power control is much less effective due to the dense network topology. The major observations are that time-domain (TD) resource allocation is preferred in dense networks with low localization CRB thresholds, while frequency-domain (FD) resource allocation dominates in sparse networks with large localization CRB thresholds.

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm’s high energy efficiency and effectiveness.

In this paper, we address the problem of the operator’s economic profit maximization in a multi-access edge computing (MEC)-enabled time division multiple access (TDMA)-based air-ground integrated networking (AGIN) network. We consider to optimize task placement and replacement, unmanned aerial vehicle (UAV) placement, UAV flight time, access control, and task offloading ratios in user devices (UDs) and the UAV. The optimization is constrained by storage capacity, task processing quality of service (QoS) requirements, and TDMA requirements, etc. Our optimization is conducted in two time scales. Task placement and replacement are performed in a coarse-grained time scale (frame), while other optimizations are conducted in a fine-grained time scale (time slot). Due to the high dynamics of the environment, finding a solution is challenging. To address this problem, we present a hierarchical deep reinforcement learning (DRL) algorithm. The high-level component is a deep Q network (DQN) agent responsible for obtaining task placement and replacement solutions within a frame. The low-level component is an improved deep deterministic policy gradient (IDDPG) agent, which is used to address task processing-related issues within a time slot. Our simulations illustrate that the proposed algorithm has good performance in economic profit maximization compared with other algorithms.

The evolution of intelligent transportation systems necessitate the integration of advanced technologies to address challenges in data processing, real-time decision-making, and network coverage. In this paper, we present a novel intelligent transportation system architecture that synergizes Unmanned Aerial Vehicles (UAVs), Digital Twins (DTs), and an Integrated Sensing, Communication, and Computation (ISCC) framework. In this system, UAVs equipped with edge computing capabilities collaborate with the ground-based cloud center to process data collected by perception devices (PDs). Each UAV and PD is mirrored by a Digital Twin (DT) at the base station, enabling real-time monitoring and predictive analytics. We intend to maximize the network lifetime and minimize the economic overhead, which is achieved through the joint optimization of the association policies of UEs, input data caching decisions, UAVs’ flight trajectory and speed, task processing mode selection and data unload proportion allocation under joint processing. To tackle the inherent challenges of nonlinearity, dynamic network conditions, and heterogeneous data sources, the problem is modeled as a Markov Decision Process (MDP) and solved using an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, adapted to handle both continuous and discrete action spaces. The experimental results show that compared to the baseline algorithm, this approach not only exhibits faster convergence but also achieves outstanding performance in maximizing system utility for intelligent transportation systems.

With the proliferation of vehicular user equipment (V-UE) in the Internet-of-Vehicles (IoV) systems, cloud computing alone cannot process all V-UE tasks, especially those latency-sensitive ones. Although static roadside fog nodes have been employed to offload computation from V-UEs, mobile fog nodes carried by vehicles that have the potential to further improve the performance of computation offloading for vehicular tasks have not been sufficiently studied for IoV systems. In this paper, we consider a mixed cloud/vehicular-fog computing (VFC) system that employs vehicle-carried fog nodes (V-FNs) in addition to cloud servers to offload tasks from V-UEs. To minimise the maximum service delay (which includes the transmission delay, queueing delay, and processing delay) among all V-UEs, we jointly optimise the offloading decisions of all V-UEs, the computation resource allocation at all V-FNs, the allocation of resource block (RB) and transmission power for all V-UEs while considering the mobility of V-UEs and V-FNs. The joint optimisation is solved by devising a fireworks algorithm-based offloading decision optimisation scheme, in conjunction with a bisection method-based V-FN computation resource allocation scheme and a clustering-based communication resource allocation scheme. Simulation results show that our proposed schemes outperform the benchmarks in terms of service the maximum delay among all V-UEs.

With the growing popularity and demand for text-to-video generation applications on mobile devices, resource-constrained mobile terminals struggle to efficiently perform video diffusion inference tasks. Cloud-edge computing networks, with the enhanced computational capabilities, flexibility and spectrum utilization, offers a promising solution for improved performance. Traditional Deep Reinforcement Learning (DRL) based methods have been employed for video diffusion inference tasks. However, existing DRL solutions suffer from low data efficiency, insensitivity to latency, and inability to adapt to task load variations, which degrade the performance. In this paper, we propose a novel deep active inference approach for video diffusion inference task offloading and resource allocation in cloud-edge computing networks. Simulation results demonstrate that our method outperforms mainstream DRL in terms of data utilization efficiency and adaptability to cloud-edge networks, better coping with dynamic task load scenarios.

The phenomenon of information cascades in communication networks has been widely studied, with earlier research primarily using statistical models for analysis. Recently, machine learning methods combined with big data analysis have proven to be powerful tools for understanding network behavior. However, previous research efforts have largely overlooked the relationships among various influential factors. In this paper, we extend machine learning based data analysis methods by introducing the concept of causality, termed the Causality-based Big Data Analysis (CBDA) method, which incorporates factors such as fake agents, welfare effects, and trusted versus untrusted prior knowledge to examine their impact on the outcomes of erroneous information cascades. Numerical results demonstrate that each factor significantly influences the outcome, generally increasing the likelihood of its preferred result. This is achieved by clarifying how the weight assigned to each influential factor affects the behavior of subsequent agents. The results of our proposed CBDA method show that trusted prior knowledge significantly reduces the probability of erroneous information cascades, offering superior explainability and effectiveness compared to existing methods.

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation.

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover’s algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms.

The orthogonal time frequency space (OTFS) modulation has garnered significant attention due to its potential to combat the frequency Doppler effect in high-mobility scenarios, especially in the low earth orbit (LEO) satellite communication. However, facilitating the OTFS in multiple-input and multipleoutput (MIMO) satellite communication requires accurate channel state information, which is a challenging task. To this end, we propose a novel deep-learning-based framework to enhance the channel estimation accuracy in a MIMO satellite communication network by exploiting the channel correlation of the OTFS-MIMO channels. Through extensive simulations, our proposed framework shows an impressive capability to predict MIMO-OTFS channels with remarkable accuracy enhancement. The effect of dataset and data distribution on the channel estimation accuracy and generalization are also revealed.

Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving field that utilizes advanced AI algorithms to generate content. Through integration with mobile edge networks, mobile AIGC networks have gained significant attention, which can provide real-time customized and personalized AIGC services and products. Since blockchains can facilitate decentralized and transparent data management, AIGC products can be securely managed by blockchain to avoid tampering and plagiarization. However, the evolution of blockchain-empowered mobile AIGC is still in its nascent phase, grappling with challenges such as improving information propagation efficiency to enable blockchain-empowered mobile AIGC. In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC. We first innovatively apply age of information as a data-freshness metric to measure information propagation efficiency in public blockchains. Considering that GATs possess the excellent ability to process graph-structured data, we utilize the GAT to obtain the optimal information propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding information propagation efficiency compared with traditional routing mechanisms.

Unmanned aerial vehicles (UAVs), known for their high flexibility and maneuverability, are regarded as the aerial platforms of future integrated sensing and communication (ISAC) networks. The communication and sensing functions of ISAC share the same spectrum and signal waveform, which often results in communication information being embedded within the sensing waveforms, thereby increasing the risk of information leakage. To enhance the security of UAV-assisted ISAC, we propose a beamforming strategy based on the mutual cooperation between communication and sensing. Specifically, by utilizing the sensing function to process echo signals, we estimate the positions of potential eavesdroppers and obstacles, which supports subsequent obstacle avoidance trajectory planning and physical layer security design. To ensure the transmission secrecy, we introduce artificial noise into the system. By designing the UAV transmit beamforming and the covariance matrix of the artificial noise, we formulate an optimization problem that aims to minimize the signal-to-noise ratio (SNR) received by the eavesdropper. To address this non-convex optimization problem, we propose an optimization algorithm that combines Dinkelbach's transform and semidefinite relaxation (SDR). Simulation results demonstrate that the SNR of eavesdropper remains at a low level throughout the flight of UAV, validating the effectiveness of the proposed scheme.

In this paper, we investigate the safe task offloading and primary node selection in blockchain, digital twin (DT) and Multi-access Edge Computing (MEC) enabled Internet of Vehicles (IoV). Edge servers centers provide computing power for task processing for Mobile Vehicles (MVs), while blockchain can provide security guarantees for MVs during task offloading. Based on the above system, we propose a joint optimization scheme for vehicle task offloading decision and the Practical Byzantine Fault Tolerance (PBFT) consensus process. Due to the large number of optimization variables and constraints, the problem becomes more complex. Traditional convex optimization and dynamic programming methods are difficult to effectively solve this problem. To address this issue, we propose a deep reinforcement learning based algorithm that utilizes Proximal Policy Optimization (PPO). The experimental results show that the algorithm proposed in this paper outperforms the benchmark algorithm in terms of convergence and other aspects.

The massive data transmission in vehicular networks has given rise to the demand for high-capacity communication and information security. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology to escalate the communication capacity of multiple vehicle users (VUs), and design artificial noise (AN)-based secure transmission schemes for this new NOMA-mmWave communication architecture. The AN beamforming matrix is derived from the mmWave discrete angular channel model to fully exploit the characteristics of mmWave propagation and facilitate the analysis. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the analytical expressions for the performance metrics. Numerical results demonstrate that the proposed scheme can effectively improve the secrecy performance of the NOMA-mm Wave vehicular communications.

Multiaccess edge computing (MEC) empowered air–ground integrated networks (AGINs) hold great promise in delivering accessible computing services for users and Internet of Things (IoT) applications, such as forest fire monitoring, emergency rescue operations, etc. In this article, we present a comprehensive air–ground integrated MEC framework, where edge servers carried by unmanned aerial vehicles (UAVs) will provide efficient computation services to IoT devices and user equipment (UE) (which are collectively referred to as UEs). We aim to minimize the long-term average weighted sum of task completion delay and economic expenditure for all the UEs. This objective is achieved through various strategies, including preinstalling new service instances into UAVs, removing idle service instances from UAVs, task offloading decision making, access control, selecting appropriate service instances for each offloaded service request, and resource allocation optimization. Considering the complexity of the problem and the dynamics of the system, we reformulate the problem as a Markov decision process (MDP) and present a multiagent deep deterministic policy gradient (MADDPG)-based algorithm to enable low-complexity and real-time adaptive decision-making. Since our problem contains integer, binary and continuous variables, it is not straightforward to apply the MADDPG algorithm. Specifically, we first normalize the continuous variables, and then convert the continuous output generated by MADDPG into discrete variables, while ensuring the coupling constraints between different variables are preserved. The simulation results demonstrate the fast convergence of our proposed algorithm and its superior performance in minimizing costs compared with the baseline algorithms.

In the 6G era, space–air–ground integrated networks (SAGINs) can provide ubiquitous coverage for Internet of Things (IoT) devices. Multiaccess edge computing (MEC) and blockchain are two enabling technologies, which can further enhance the services capabilities of SAGINs, where MEC demonstrates a notable capability in efficiently minimizing both the task execution delays and system energy consumption, and blockchain can provide trust guarantee for task offloading and wireless data transmission among the entities operated by different operators in SAGIN. In this article, we present an MEC and blockchain enabled SAGIN architecture, which consists of two subsystems. In the MEC subsystem, a satellite and multiple unmanned aerial vehicles (UAVs) act as the edge nodes to provide IoT devices with computing power. Moreover, the satellite serves as the block generator and the client, and the UAVs serve as the consensus nodes of the blockchain subsystem. We intend to minimize the energy consumption within the network, which is achieved through the IoT devices’ task segmentation, the UAVs, and satellite’s bandwidth allocation among their served IoT devices. And moreover, the computing power of UAVs and the satellite also allocated in task processing and blockchain consensus. Considering the high dynamics of the network, it is impossible to obtain real-time and accurate channel information, so we remodel this problem as a Markov decision process, and propose a low-complexity adaptive optimization algorithm based on the deep deterministic policy gradient (DDPG). Our simulation results indicate that the proposed algorithm exhibits commendable performance in minimizing the network energy consumption and DDPG agent’s accumulated reward maximization.

Vehicular communications in future sixth-generation (6G) networks are expected to leverage integrated sensing and communications (ISACs) and mobile edge computing (MEC) techniques. However, the rapid proliferation of vehicle user equipment (V-UE) and the diversity of ISAC-aided and MEC-empowered vehicular communication and computation services demand a more intelligent and efficient resource allocation framework for the next-generation vehicular networks. To address this issue, we propose a comprehensive ISAC-aided vehicle-to-everything (V2X) MEC framework, where the V-UEs can offload their tasks to the edge server collocated at the roadside unit (RSU). We aim to minimize the long-term average total service delay of all the V-UEs by jointly optimizing the offloading decisions of all the V-UEs, the computation resource allocation at the ISAC-aided RSU, the transmission power, and the allocation of resource blocks for all the V-UEs, where the total service delay of a V-UE includes the task processing delay and the transmission delay if the V-UE offloads its task to the RSU. To solve the formulated mixed integer nonlinear programming problem, we design a multiagent deep deterministic policy gradient (MADDPG)-based offloading optimization and resource allocation algorithm (MADDPG-O2RA2). Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of convergence and the long-term average delay among all the V-UEs.

Vehicular mobile edge computing (vMEC) and non-orthogonal multiple access (NOMA) have emerged as promising technologies for enabling low-latency and high-throughput applications in vehicular networks. In this paper, we propose a novel multi-agent deep deterministic policy gradient (MADDPG) approach for resource allocation in NOMA-based vMEC systems. Our approach leverages deep reinforcement learning (DRL) to enable vehicles to offload computation-intensive tasks to nearby edge servers, optimizing resource allocation decisions while ensuring low-latency communication. We introduce an attention mechanism within the MADDPG model to dynamically focus on relevant information from the input state and joint actions, enhancing the model’s predictive accuracy. Additionally, we propose an attention-based experience replay method to expedite network convergence. The simulation results highlight the effectiveness of multi-agent reinforcement learning (MARL) algorithms, such as MADDPG with attention, in achieving better convergence and performance in various scenarios. The influence of different model parameters, such as input data volumes, task load levels, and resource configurations, on optimization results is also evident. The decision making processes of agents are dynamic and depend on factors specific to the task and environment.

Unmanned aerial vehicle (UAV) can work as a flying computing platform to supply computation services to users when the terrestrial infrastructure is insufficient or damaged, due to its high mobility, flexibility and controllability. However, there remain many challenges in practical UAV-assisted mobile edge computing (MEC) system. Among them, a unique challenge is how to coordinate communication and computing resources to adapt the diverse heterogeneous demands of users in dynamic network environments. Accordingly, this article investigates a more practical UAV-enabled MEC network, which considers the task backlog queues and the heterogeneous demands of users. With joint optimization transmit power, bandwidth ratio and UAV trajectory, we minimize the long-term energy consumption while ensuring the controllable task backlog queues. As the proposed problem is a long-term stochastic optimization problem, we utilize the Lyapunov method to transform it into two deterministic online optimization subproblems and iteratively solve them. Moreover, we design personalized Lyapunov control factors to meet the tradeoff between energy consumption and queue stability for different users with heterogeneous requirements. In terms of solving subproblems, for the first subproblem, we prove its convexity by using the convexity-preserving property of composite perspective function, and then obtain the closed-form optimal solution. For the second subproblem, we skillfully design a low-complexity trajectory scheduling algorithm by using successive convex approximation (SCA), penalty function, and convex function properties. The simulation results show that the proposed algorithm with a lower complexity effectively reduces the long-term energy consumption of the system while meeting the heterogeneous requirements of users.

In order to support delay-sensitive applications of vehicle equipment (V-UE) in the Internet-of-Vehicles (IoV) systems, it is necessary to allow V-UEs to offload their computationally intensive applications to a cloud or fog computing server. Existing works mainly focused on minimising the transmission and processing delays while ignoring the mobility of V-UEs and/or the queueing delays at the cloud or fog servers. In this paper, we consider a vehicular network supported by a mixed fog and cloud computing system, where the queues at the fog node (FN) and the cloud centre are modelled following the M/M/1 and M/M/C queueing models, respectively. To minimise the maximum service delay (which includes the transmission delay, queueing delay and processing delay) among the V-UEs, we propose to jointly optimise the offloading decisions of all V-UEs and the computation resource allocation at the FN while considering the V-UEs’ mobility and queueing delays at the FN and cloud centre. This is achieved by devising a fireworks algorithm-based offloading decision optimisation algorithm in conjunction with a bisection method-based FN computation resource allocation scheme. Simulation results demonstrate that our proposed algorithm achieves a much lower maximum service delay than the benchmarks.

Compared with 5G networks, 6G networks are guaranteed to provide various tailored end-to-end network services and emerging cloud-edge applications. Network slicing (NS) is regarded as the key enabler of 6G networks. Softwarization and virtualization technologies, such as software-defined networking and network function virtualization, are accelerating the way toward NS of 6G networks. The resource allocation issue in 6G NS is very crucial, worthy more research attention. In this article, we propose one efficient resource allocation algorithm, labeled asTailoredSlice-6G, so as to realize the tailored slices in 6G. When receiving one slice request, ourTailoredSlice-6Gwill identify the slice resource type in the first place. Then, ourTailoredSlice-6Gwill select its most suitable subalgorithm to do the resource allocation and slicing deployment. Each type of slice corresponds to its specific resource allocation subalgorithm, inserted in theTailoredSlice-6Galgorithm. In addition, each subalgorithm inTailoredSlice-6Gis guaranteed to run within polynomial time. Thus,TailoredSlice-6Ghaving the potential to be promoted to real networking application. To highlight the merits ofTailoredSlice-6G, we do the comprehensive simulation. Simulation results vividly reveal that ourTailoredSlice-6Goutperforms the selected heuristics that are representative in the literature.

Mobile-edge computing (MEC) is considered as a promising technology to enable low latency applications while consuming less energy, and nonorthogonal multiple access (NOMA) is regarded as a hopeful method of increasing spectrum efficiency and the wireless network capacity. In this article, we consider a NOMA-MEC-based Internet-of-Things (IoT) network, and propose a joint optimization framework to maximize the effective system capacity, i.e., the number of IoT devices whose tasks are processed successfully, and meanwhile to maximize the total energy saving. First, we concentrate on improving the effective system capacity from the wireless side by introducing NOMA, and from the IoT device side by task offloading decision optimization, where distributed optimization is conducted and closed-form solution is obtained. Then, we maximize the total energy saving also from two aspects, i.e., the device-side computation resource allocation, and the wireless side joint admission control, user clustering, orthogonal subcarrier assignment, and transmit power control, where we resort to graph theory and propose a low-complexity heuristic algorithm to solve it. Abundant simulation results demonstrate our proposed joint optimization algorithm performs well in both effective system capacity optimization and energy saving maximization.

Recently, the term “Internet of Things” (IoT) has elicited escalating attention. The flexibility, agility, and ubiquitous accessibility have encouraged the integration between machine learning (ML) with IoT. However, there are many challenges that present the key inhibitors in moving ML to the public solution, such as centralized training, poor training efficiency, and heavy computing capabilities requirements. Therefore, bringing learning intelligence to edge IoT nodes has been spotlighted for some researches. Meanwhile, how to govern the use of learning results efficiently, reliably, scalably, and safely is hampered by the heterogeneity and nonconfidence among IoT nodes. In this article, we propose a blockchain-based collective Q-learning (CQL) approach to address the above issues, where lightweight IoT nodes are used to train parts of learning layers, then employing blockchain to share learning results in a verifiable and permanent manner. We further improve the traditional Proof of Work (PoW). Instead of solving a meaningless puzzle, we regard the learning process in the IoT node as a piece of work. Accordingly, the winner is the IoT node with the minimum reduced percentage of the learning loss function, referred to as the Proof-of-Learning (PoL) consensus protocol. Specifically, in order to show how the CQL approach works, we use it to address a networking integrated cloud-edge-end resource allocation in IoT. The experimental results reveal the superior performance of the proposed scheme.

Blockchain technology has been widely used in many fields. However, the proof of work (PoW) problem in the mining process of mobile devices requires a large amount of computing resources and energy consumption, which brings huge challenges to mobile devices. Mobile edge computing (MEC) can effectively solve the above problems, allowing mobile devices to offload tasks to edge servers to relieve the pressure of limited computing resources on mobile devices. Nonorthogonal multiple access (NOMA) is good at improving spectrum efficiency, so that the system can accommodate more users. In this paper, we propose a new NOMA-based MEC-enabled blockchain framework. Under the conditions of a given task execution deadline, the decision of offloading, local computing resource allocation, user clustering and admission control, and transmit power control is jointly optimized to minimize the total cost of the system. Since the problem is hard to solve, we decouple it into subproblems for low-complexity solutions. First, we propose two heuristic algorithms to obtain the binary offloading decision and user association, and then closed-form solutions of local resource allocation and transmit power control are obtained under the required delay constraints. Simulation results show that our proposed algorithms perform good in cost reduction compared with other baseline algorithms.

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) has been intensely investigated in recent years, due to its potential to achieve new system concepts such as high-resolution wide-swath imaging and multimodal operation. Among the proposed orthogonal waveforms, inter-pulse phase modulation outperforms by the immunity to the impact of Doppler shift arising from the movement of the platform, thus no compensation of the Doppler effect is needed before SAR imaging. However, the demodulation procedure requires oversampling in Doppler domain, which poses strict restriction on the pulse repetition frequency (PRF) and seriously limits its applications. In this paper, a novel azimuth discrete periodic phase coding scheme and the corresponding demodulation method is proposed. By using this method, the undersampled echoes could be separated from each other after multichannel reconstruction, thus feasible for high-resolution wide-swath imaging. Theoretical derivations and simulation results validate the effectiveness of the proposed coding scheme.

To prolong the time duration of smart mobile devices (SMDs) or enable low‐latency tasks, mobile edge computing (MEC) has emerged as a promising paradigm by offloading tasks to nearby MEC servers (MECSs). In this study the authors propose an optimisation problem to minimise the weighted sum of the total delay and energy consumption of all SMDs in a multi‐MECS‐multi‐SMD network via multi‐dimensional optimisation on offloading strategy making, load balancing, computation resource allocation and transmit power control. Since the problem is NP‐hard, the authors decompose it into three subproblems to solve. First, they propose a low complexity heuristic algorithm to obtain the offloading strategies while guaranteeing load balancing between the multiple MECSs. Then they solve computation resource allocation subproblem using Lagrange dual decomposition. Finally, employing fractional programming, the authors transform the transmit power control subproblem into a convex programming problem where the closed‐form solution is obtained. The proposed simulation results verify the convergence of the proposed iterative algorithms, and demonstrate that the proposed joint optimisation could achieve good performance in both delay and energy reduction.

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user equipments, such as head-mounted displays (HMDs), ultralow-latency viewport rendering, and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multiaccess edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich terahertz (THz) communication is expected to enable ultrahigh-speed wireless data transmission. In this article, we propose to minimize the long-term energy consumption of a THz wireless access-based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning-based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor-critic (A3C)-based joint optimization algorithm is proposed. The simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.

Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.

Mobile edge computing enabled vehicular networking has appeared as a promising solution to the emerging resource hungry vehicular applications. In this paper, we study the computation offloading in a cognitive vehicular network that reuses the TV white space (TVWS) bands. We propose to maximize the average economical profit of the service provider by jointly considering communication and computation resource allocation, while guaranteeing network stability and the QoS of TVWS primary users. Based on Lyapunov optimization, we design an per-frame algorithm to tackle the joint optimization problem, where we first derive the closed-form solution for computation resource allocation, and then develop a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for radio resource allocation. Simulation results demonstrate that the proposed algorithm can flexibly balance the profit-delay tradeoff, and can improve the economical profit of the service provider significantly as compared with the existing schemes.

Simultaneous wireless information and power transfer (SWIPT) technique offers a potential solution to ease the contradiction between high data rate and long standby time in the fifth generation (5G) mobile communication systems. In this paper, we focus on the SWIPT network design and optimization with 5G new frequencies. To design an efficient SWIPT network, we first investigate the propagation properties of 5G low-frequency (LF) and high-frequency (HF) channels. Specifically, a measurement campaign focusing on 3.5 GHz and 28 GHz is conducted in both outdoor and outdoor-to-indoor scenarios. Motivated by the measurement results, we design a dual-band SWIPT network, where the HF band is used for short-distance information delivery, while the LF band is used for short-distance energy transfer and long-distance information delivery. The designed network has a win-win architecture which can enhance the throughput of cell-edge users and improve the energy-harvesting efficiency of cell-center users. To further boost the network performance, we devise a joint power-and-channel allocation algorithm, which has the advantages of low complexity and fast convergence. Finally, simulation results demonstrate that the designed dual-band network outperforms the conventional single-band network in terms of energy-harvesting efficiency and user fairness, and the proposed algorithm can further upgrade the network performance significantly.

Mobile edge computing (MEC) has emerged as a potential paradigm to enhance the processing capabilities of mobile user equipments (MUEs), while edge caching has become a promising means of alleviating traffic in the backhual. In this paper, we formulate a stochastic optimization problem to maximize the average economical profit of MEC server by jointly optimizing offloading decision and caching decision making, and the allocation of radio, computing, and caching resources in a cellular network, with network stability taken into account. To tackle this problem, we develop an online algorithm referred to as dynamic joint computation offloading, resource allocation, and content caching algorithm (DJORC) based on Lyapunov optimization theory. Specifically, the proposed DJORC only needs the current states of the system, and without requiring any prior-knowledge. By further using 0-1 integer programming and linear programming, the closed-form solution of the formulated problem is obtained. Simulation results are presented to verify the performance of DJORC under different parameter settings, as well as the performance gains obtained by DJORC over other existing schemes.

Internet of things (IoT) are envisioned to be an essential in our daily lives, but most IoT devices (IDs) are battery powered and have limited resource. Recently, fog computing (FC) has been proposed to support IoT systems, where part or all of the data are offloaded from IDs to fog nodes for processing or computation. In this paper, we propose to optimise the partial computation offloading in an OFDMA based FC IoT system, while ensuring fairness among IoT links with respect to their energy consumption. In particular, we minimize the energy consumption of the worst-case link by jointly optimizing the size of offloaded data and the assignment of subcarriers, while guaranteeing the rate requirement. The formulated min-max energy efficiency optimization problem (MEP) is solved using Lagrangian dual decomposition and subgradient projection, bases on which we propose an iterative algorithm. Our simulation results show that the proposed resource allocation algorithm is more energy efficient than the existing algorithms for FC supported IoT, while achieving fairness among IoT links.

In this paper, we develop computation offloading scheme based on device-to-device (D2D) communications. The scheme is proposed for effective computation execution where some mobile devices (MDs) could offload their computation intensive tasks to appropriate nearby MDs where necessary, with the assistance of the base station. Accordingly, we formulate a stochastic optimization problem to minimize the average expenses (e.g., wireless communication expense, computation service expense) of MDs in task offloading, while considering the computation resource budget constraint to restraint the behavior of the overuse of computation resource and guarantee mobile users' motivation for collaboration not impaired. To solve this problem, we propose an algorithm that does not need any prior- knowledge of available resources of MDs, referred to as the SEEP. To address a couple and mixed combinational subproblem in the SEEP, we decouple optimization variables for suboptimal. By doing so, both task scheduling and subcarrier assignment are obtained in closed forms, while power allocation is solved by developing efficient iterative algorithm that exploits D.C. (difference of convex functions) structure. Simulation results show the convergence of the SEEP, and illustrate SEEP can flexibly coordinate the tradeoff between expenses and delay, and can substantially reduce expenses of MDs against other existing schemes.

Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power, and radio bandwidth while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization, and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC, and the number of beneficial UEs over existing algorithms.

The wavelength of synthetic aperture lidar (SAL) is very short; vibration of millimeter magnitude will significantly change the phase of echo signal and lead to the defocus of imaging in azimuth direction, so vibration estimation is a key step of SAL imaging. The existing Space Correlation Algorithm (SCA) used to estimate vibration of SAL is only suitable for the condition of narrow azimuth beam and high signal-noise ratio (SNR). Based on division of inner view field by two detectors along track, a new data obtaining model of SAL is established in this paper. Furthermore, a new vibration estimation method is proposed. Simulations validate that the vibration of SAL can be accurately estimated in the condition of wide azimuth beam and low SNR with the proposed method, meanwhile, good imaging result is obtained.

In this paper, we propose a novel concept of virtual resource chain for Software Defined Campus WLANs (SD-WLANs). A typical SD-WLAN is composed of three layers, i.e., infrastructure, access control and application layers. Firstly, the network control plane is decoupled from the forwarding plane, and soft-defined access points (SD-APs) merely execute the forwarding rules according to the instructions from access controllers (ACs). Secondly, with a global view of the network, AC could abstract, encapsulate, and virtualize the physical resources (e.g., computing, storage, and radio resources). Finally, by binding all the resources between the infrastructure and access control layers, isolated virtual resource chains are built up and simultaneously mapped to northbound interface (NBI) to accommodate various services at the application layer. Benefiting from the virtual resource chain, a user-oriented load balance scheme is presented in this paper. In SD-WLANs, the load of APs could be balanced according to the user's demands, and our proposed user-oriented load balance scheme could be easily invoked only by coding at the application layer. Experiment results demonstrate that our scheme is able to distribute mobile stations among all the APs and increase the average system throughput, and thus to increase the flexibility and availability of networks.