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%.

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing collaborative task offloading schemes with DT empowerment are faced with challenges, such as the complex collaborative relationship between tasks and multiple Edge Servers (ESs), and spatio-temporal heterogeneity of ES resources. This paper investigates the issues of parallel collaborative offloading and resource allocation under the assistance of DT and Overlapping Coalition Formation (OCF) game. One novel scheme, abbreviated as OCF-based PCORA, is proposed. With comprehensive information within the digital space, the OCF-based PCORA scheme dynamically models collaborative relationships between tasks and ESs. Subsequently, each task is offloaded to its optimal ES coalition for efficient collaborative processing. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem is decomposed into three subproblems: ED association, bandwidth allocation, and collaborative task processing. The first subproblem is transformed through slack variable relaxation and solved using the interior-point method. The second subproblem, being naturally convex, is addressed via convex optimization method. For the third subproblem is modeled as an OCF game with transferable utility. On top of this, a bilevel iterative optimization algorithm is proposed to form overlapping task coalitions in a distributed manner. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 16.01%–44.97% and decreases the task offloading failure rate by 28.99%–75.41%, compared to state-of-the-art baselines.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems and simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are considered as promising technologies for enhancing the performance of wireless communication systems. In this paper, we investigate the performance of a STAR-RIS-aided CF-mMIMO system under channel aging, which has been ignored in previous studies. Firstly, we propose a linear minimum mean squared error (LMMSE) aggregated channel estimator and formulate statistical channel state information (CSI) properties for the subsequent system performance analyses. Then, closed-form expressions for the uplink and downlink spectral efficiencies (SEs) of the STAR-RIS-aided CF-mMIMO system under channel aging are explored, where for the uplink, the two-layer large-scale fading decoding (LSFD) and the simple centralized decoding (SCD) are utilized, respectively, and for the downlink, the maximal ratio (MR) precoding and fractional power control (FPC) are adopted. Moreover, the optimal LSFD coefficients that maximize the uplink SE is presented. Afterwards, for further enhancement of SEs, a novel optimization scheme is presented, which optimizes the passive beamforming (PB) of the STAR-RIS to minimize the normalized mean square error (NMSE) of the aggregated channel estimation. The simulation results reveal that the STAR-RIS-aided CF-mMIMO system achieves superior uplink and downlink performance compared to both the RIS-aided CF-mMIMO system and the conventional CF-mMIMO system without RIS over aging channel. Furthermore, the results show that the PB optimization can significantly reduce the NMSE of channel estimation, thereby improving the estimation accuracy and SEs under channel aging.

The widespread integration of artificial intelligence (AI) in next-generation communication networks poses a serious threat to data privacy while achieving advanced signal processing. Eavesdroppers can use AI-based analysis to detect and reconstruct transmitted signals, leading to serious leakage of confidential information. In order to protect data privacy at the physical layer, we redefine covert communication as an active data protection mechanism. We propose a new parasitic covert communication framework in which communication signals are embedded into dynamically generated interference by generative adversarial networks (GANs). This method is implemented by our CDGUBSS (complex double generator unsupervised blind source separation) system. The system is explicitly designed to prevent unauthorized AI-based strategies from analyzing and compromising signals. For the intended recipient, the pretrained generator acts as a trusted key and can perfectly recover the original data. Extensive experiments have shown that our framework achieves powerful covert communication, and more importantly, it provides strong defense against data reconstruction attacks, ensuring excellent data privacy in next-generation wireless systems.

In this paper, we propose a task-oriented semantic communication system based on the cloud-network-end (C-N-E) architecture to improve the energy efficiency of image transmission. Within the system, a cloud server provides storage and computation resources for image data collected by multiple cameras. The semantic information of an image is modeled as a scene graph, enabling the analysis of end-user interests. To reduce communication overhead, only useful semantic information relevant to user interests is transmitted. Considering the delay constraint, we formulate an optimization problem to minimize the total energy consumption by jointly selecting semantic information and allocating computation and communication resources. To solve this problem efficiently, an iterative algorithm based on optimal matching and sequential convex approximation is developed. Comparative simulations validate the efficacy of our algorithm.

In fully decoupled networks with separate uplink/downlink (UL/DL) base station (BS) deployments and high user mobility, ensuring efficient UL/DL user association remains a critical challenge. The dynamic environment and complex channel conditions necessitate state perception for optimal association strategies, while the densification of nodes demands scalable solutions to handle increased combinatorial complexity in UL and DL transmissions. This paper introduces a novel framework leveraging unmanned aerial vehicle (UAV) sensing-assisted to predict user mobility and channel dynamics, combined with multiple association mechanism to address dense node interactions. Accordingly, a joint optimization problem is formulated, where Kriging-based prediction is adopted to assist the user association for both UL and DL. To solve it, a hierarchical matching game is developed to decompose the joint problem into decoupled UL and DL games. Particularly, a low-complexity Kriging prediction-based hierarchical matching algorithm is designed to obtain the solution. Simulation results in dynamic network scenarios demonstrate that the effectiveness of proposed approach and the superiority is validated by comparisons.

This paper proposes a parasitic covert communication framework for unmanned aerial vehicle (UAV), which embeds communication signals into dynamically adaptive interference. Legitimate receivers can extract target signals from parasitic interference, while the eavesdropper fails to accomplish. In a single-channel scenario, we propose a complex dual-generator unsupervised blind source separation (CDGUBSS) method, which implements a novel dual-phase adversarial architecture, namely, signal pre-training phase and adversarial separation phase. The former phase employs complex-valued generative adversarial networks (GANs) to learn latent representations of communication signals, while the latter one introduces a dual-generator dynamic learning mechanism to separate communication signal from the parasitic signals. Extensive experiments demonstrate the superiority of our proposed scheme, which validate its capability to enable robust parasitic covert communication.

To address the critical security challenges posed by the inherent broadcasting nature and heterogeneous service demands in cognitive satellite-terrestrial networks (CSTN), this paper presents a groundbreaking framework that integrates reconfigurable intelligent surfaces (RIS) with sparse code multiple access (SCMA). This framework aims to maximize the achievable secrecy rate by jointly optimizing transmit beamforming, the RIS reflection matrix, and SCMA codebook configurations, while adhering to power constraints and the quality-of-service requirements of legitimate users. To tackle the non-convex optimization problem in complex environments, we develop an intelligent decision-making mechanism based on a modified multi-agent two-delay deep deterministic (MMTD3) algorithm, which introduces a breakthrough mechanism by decoupling continuous beam control from discrete codebook selection, offering a new paradigm for AI-driven cross-domain security optimization in CSTN. Simulation results demonstrate that the proposed framework significantly outperforms existing benchmarks, verifying its potential in supporting wireless endogenous security and meeting massive heterogeneous service demands in CSTN.

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing task offloading schemes with DT empowerment are faced with challenges, such as the spatio-temporal heterogeneity of edge server (ES) resources, resource-constrained ESs, and the explosive growth of data in emerging applications. This paper investigates the issues of task offloading and resource allocation under the assistance of DT and multiple ESs parallel collaboration. One novel scheme, abbreviated as Mes-PCORA, is proposed. With comprehensive information within the digital space, the Mes-PCORA scheme dynamically adjusts task allocation ratios across multiple ESs to achieve collaborative task offloading. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem transformed into a bilevel optimization problem. Then, a bilevel iterative optimization approach is proposed. Specifically, the upper-level optimization problem is formulated as a multi-agent Markov Decision Process, and a deep reinforcement learning-based resource allocation algorithm is designed to solve it. Subsequently, for the lower-level optimization problem, it is solved by the interior point method. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 27.16%–63.44% and decreases the task offloading failure rate by 35.83%–73.95%, compared to state-of-the-art baselines.

Cell-free massive MIMO (CF mMIMO) systems overcome inter-cell interference, enhancing overall communication rates for next-generation networks. However, the pilot contamination exacerbates channel estimation errors and the complex connectivity makes it difficult to deal with resource allocation optimization problem. In this paper, we investigate the robust beamforming problem under channel uncertainty with the goal of improving the minimum quantile rate. Specifically, we introduce hypergraph neural network (HGNN) into the wireless resource allocation of CF mMIMO ststems for the first time, leveraging hypergraph modeling to capture the many-to-many relationships between Access Points (APs) and User Equipments (UEs). Furthermore, we significantly reduce the search space of the optimization problem by applying optimal interference suppression beamforming theory. In order to soften the sorting process, we adopt the Monte Carlo sampling strategy for data augmentation. Simulation results demonstrate that the proposed algorithm outperforms conventional schemes, achieving 14.1% performance gain and converging more than twice as fast as the state-of-the-art machine learning models.

Aiming to circumvent the low energy efficiency (EE) dilemma of multiple-input multiple-output (MIMO) systems induced by employing hundreds of antennas, this paper investigates the potentials of stacked metasurface (SM) and semantic communications (SemCom) for achieving energy-efficient holographic communications in MIMO systems. Specifically, SM enables hybrid beamforming with increased degrees of freedom (DoFs) and reduced energy consumption, while SemCom transmits dramatically compressed key informantion that comes with low power consumption and high EE. To this end, we formulate a worstcase semantic EE (Sem-EE) maximization problem in terms of the transmit beamformer and SM's phase shifts. By proposing a semantic majorization-minimization to handle the fractional and quasi-convex Sem-EE form, quadratically constrained quadratic programs and cyclic coordinate descent can be exploited to solve the optimization variables with low computational complexity. Numerical simulations demonstrate the enhanced EE performance of SMaided semantic beamforming scheme compared to the conventional MIMO systems.

The rapid development of 5G technology leads to increasing energy consumption in base stations (BSs). For the vision of green and sustainable communications, we propose a scheme aimed at reducing BS energy consumption through symbol shutdown. This approach reduces BS energy consumption while ensuring the quality of service (QoS) for user equipments (UEs). Our scheme considers highly dynamic channel conditions and formulates a joint optimization problem, including BS shutdown and power allocation, constrained to the long-term average rate demand of all UEs. To address this problem, we design a Lyapunov method-based algorithm (LMBA) that transforms the mixed integer and dynamic optimization problem into a more tractable form and solves it using the Karush-Kuhn-Tucker conditions. Simulations indicate that our proposed strategy significantly reduces the BS energy consumption and the LMBA outperforms several benchmark algorithms.

Space-air-ground integrated vehicular networks (SAGVN) play a crucial role in the 6G system, offering global coverage and ultra-wide-area broadband access. Meanwhile, advancements in artificial intelligence (AI) have led to a significant increase in model inference demands, which come with stringent latency requirements. In this paper, considering the intelligent services in SAGVN, we explore the co-inference problem and improve the inference efficiency by performing model splitting, vehicle association, and resource allocation. This problem is solved iteratively by decomposing it into multiple sub-problems. In particular, the model splitting problem is addressed by systematically searching for the optimal split points. Besides, the joint vehicle association and resource allocation problem are reformulated into a monotone submodular function without satellites, and then a low-complexity submodular optimization algorithm is proposed. To further utilize the computing power of the satellite, we further introduce a resource reallocation algorithm based on the existing optimization results. These two subproblems can iterate alternately until the optimization goal converges. Simulation results show that the proposed algorithm can achieve better latency performance in several inference tasks.

Space-Air-ground integrated vehicular network (SAGVN) can provide substantial advantages for the Internet of Vehicles (IoV) with broad coverage and long-distance communications. However, efficient task offloading in SAGVN is difficult due to the dynamic and multi-dimensional characteristics of IoV. In this paper, we address a task offloading problem in SAGVN, where unmanned aerial vehicles (UAVs) and low Earth orbit (LEO) satellites collaborate to offer mobile edge computing (MEC) services to vehicles. Our goal is to jointly design service placement and task offloading strategies for each UAV to minimize overall system latency, subject to mobility, coverage, energy, and bandwidth constraints. The problem can be reformulated as a multi-agent Markov decision process (MAMDP), where each vehicle and UAV can act as an agent, and the actions taken by agents correspond to the optimal UAV trajectory, subchannel selection, task partition ratio, and offloading destination. Then, we decompose the problem into three sub-problems of service placement, task offloading, and subchannel selection. Given the extensive observation and action space, an iterative best response multi-agent proximal policy optimization (IBR-MAPPO) algorithm is proposed. Finally, simulation results show that our approach converges rapidly and achieves lower execution delay than baseline algorithms.

ABSTRACT Software‐defined networking (SDN) is an essential trend in the future development of networks. Due to hardware limitations, its progress undoubtedly counts on distributed management with multiple controllers to oversee the global network. The multiple network controllers heavily rely on each other to provide network services. However, concerns regarding issues like commercial privacy leakage have given rise to a conflict between the imperative of privacy protection and the necessity of data sharing. This paper focuses on studying the conflict between privacy protection and collaborative sharing. By mapping blockchain nodes to corresponding controllers and deploying them independently, the trusted sharing of domain information is achieved. Leveraging 6G technologies, significant latency issues introduced by uplink and download transmissions are addressed. Abstract network data is encrypted using attribute‐based encryption, ensuring sensitive information protection while reducing blockchain overhead. Furthermore, we have incorporated the Provenance Data model and established an alliance blockchain system for cross‐SDN network operator management. Finally, a prototype system is implemented, and through rigorous testing, the system's functional effectiveness and high efficiency are demonstrated.

Abstract Traffic prediction serves as a critical foundation for traffic balancing and resource management in Low Earth Orbit (LEO) satellite networks, ultimately enhancing the efficiency of data transmission. The self‐similarity of traffic sequences stands as a key indicator for accurate traffic prediction. In this article, the self‐similarity of satellite traffic data was first analyzed, followed by the construction of a satellite traffic prediction model based on an improved Long Short‐Term Memory (LSTM). An early stopping mechanism was incorporated to prevent overfitting during the model training process. Subsequently, the Diebold‐Mariano (DM) test method was applied to assess the significance of the prediction effect between the proposed model and the comparison model. The experimental results demonstrated that the improved LSTM satellite traffic prediction model achieved the best prediction performance, with Root Mean Squared Error values of 18.351 and 8.828 on the two traffic datasets, respectively. Furthermore, a significant difference was observed in the DM test compared to the other models, providing a solid basis for subsequent satellite traffic planning.

The Internet of Vehicles (IoV) is playing a pivotal role in advancing intelligent transportation systems. Deploying the drone as edge nodes in IoV networks has emerged as a promising solution to enhance the communication coverage and energy efficiency (EE). However, the existing drone deployment and resource allocation strategies often lack the necessary intelligence and adaptability to respond to the dynamic traffic conditions. To address these challenges, we leverage machine learning (ML) technology to optimize EE by jointly optimizing small base station (SBS) dormancy and drones’ 3-D positioning—a problem recognized as NP-hard. To tackle this problem, we propose an energy-efficient multi-drone 3-D deployment with SBS dormancy (MUD-SBSD) algorithm, which decomposes the problem into two manageable phased issues. First, a dormant strategy based on the base station centrality (BSC) metric is developed to switch SBSs to a dormant state during low-traffic periods. Second, the horizontal positions of drones are optimized using the k-means algorithm, followed by determining the optimal drone heights via the genetic algorithm (GA). Extensive simulations validate the proposed algorithm can achieve a 41% improvement in EE and a 15% increase in communication coverage rate compared to the existing strategies. These results not only highlight the effectiveness of the proposed solution but also underscore its relevance in enhancing the performance and sustainability of IoV networks, paving the way for more intelligent and responsive transportation systems.

Constructing digital twin (DT) models of user equipments (UEs) efficiently is essential for enabling real-time monitoring of UEs, providing crucial support for optimizing the operation of Social Internet of Things (SIoT) systems. However, UE heterogeneity and UE mobility concerns impede the DT deployment in SIoT. In this article, we propose a real-time DT deployment (RDTD) scheme for SIoT systems, where the heterogeneous DT modeling and the DT migration are achieved based on personalized federated learning (PFL) ideas. Specifically, we decompose the DT model into the global generalization layers and the personalization layers, based on which we propose a hierarchical PFL (HPFL)-based DT model construction mechanism. The mechanism constructs customized DT models for heterogeneous UEs through a two-stage model parameter update process, involving end-edge-center collaboration training of all parameters and fine-tuning of the personalization layer parameters. Second, based on the above mechanism for DT model construction, a low-latency DT model parameter migration algorithm is proposed. This algorithm ensures real-time interaction by migrating only the personalized layer parameters of the DT model and reconstructing the DT model. Lastly, numerical experiments verify the effectiveness of the RDTD scheme, improving modeling accuracy by 13.91%, 41.06%, and 135.35% compared to the three baselines. Additionally, our proposed scheme significantly reduces interaction latency by 29.93% compared to the baseline.

The development of Industrial Internet of Things (IIoT) applications presents a critical challenge in terms of latency limitation, particularly considering the limited availability of resources that prevent a single fog device from fully executing large-scale computing tasks. In such scenarios, enabling distributed computing across multiple fog servers or collaborating with cloud servers holds promising potential. To improve the efficiency of task offloading while accounting for the crucial role of movable fog devices (e.g., robots and unmanned cars), we formulate a joint optimization problem as a partially observable Markov decision process (POMDP), incorporating offloading decisions, computing resource allocation, and trajectory optimization under constraints related to available resources and collision avoidance. Due to the nondeterministic polynomial-time hardness (NP-hardness) in the problems of task offloading and resource allocation, we reformulate a matroid-constrained submodular maximization problem and propose an iterative low-complexity algorithm to find solutions. Subsequently, extracting better solutions from submodular optimization, we propose a multiagent reinforcement learning (MARL)-based algorithm to solve the trajectory optimization problem for the movable fog devices acting as agents, making decisions based on their local observations. Finally, simulation results have validated that the proposed scheme has a superior performance compared to the baselines.

Due to the users' overlapping channels and the open nature of the wireless medium, inter-user interference and malicious jamming attacks deteriorate the performance of unmanned aerial vehicle (UAV) communications. With this focus, this paper proposes a novel integration of dual polarization, rate-splitting multiple access (RSMA), and stacked reconfigurable intelligent surface (RIS) transceiver into UAV networks, thus simultaneously mitigating the inter-user interference and malicious interference by fully exploiting their potentials in the power, space, and polarization domains. Building upon this architectural framework, a generalized sum rate maximization problem is formulated under the jammer's imperfect angular channel state information and unknown cross-polarization discrimination. To efficiently tackle the challenges posed by the intractable non-convex design problem with both high-dimensional variables and the multiple QoS constraints, a low-complexity optimization framework is presented, where a discretization method combined with quadratic property, a reduced-majorization-minimization algorithm, and two computationally efficient algorithms using block successive upper-bound minimization are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify the superiority and validity of our proposed architecture and optimization framework over benchmarks.

The development of the next generation ubiquitous network puts forward higher requirements for the connection density in the communication network, e.g., massive IoT and UAV swarm, which has led to a lot of research on link management. With the expansion of network scale, the weaknesses of existing algorithms in computing efficiency, performance, and realizability have become prominent. The emerging graph neural network (GNN) provides another way to solve this problem. In this paper, we design a cross-receptive distributed GNN structure from the perspective of communication system, combining measurable index of the actual scene with message passing frame. This new GNN structure and the additional input feature dimension work together to provide richer and more comprehensive information for network training. After the initial deployment of the power decision from GNN, we select some links to shut down and others to reduce their transmit power to further improve system performance and save energy. Simulation results show that our proposed method reaches 83.1% performance of the centralized mechanism. In addition, the discussion on scalability suggests that in order to save training cost, small-scale scenes with the same density can be selected for training in the application of large-scale scenes.

Traditional cell-free massive multiple-input multiple-output (CF-mMIMO) systems face challenges of resource scarcity, cognitive limitations, and coverage blind spots, which primarily stem from the extensive deployment of long cables connecting each access point to the central processing unit in the system. To maximize the minimum achievable user rate and enhance the performance of a downlink CF-mMIMO system, we propose an innovative scheme that jointly integrates user association, unmanned aerial vehicle (UAV) placement, and transmission power allocation, with UAV-satellite assisted. The scheme also considers stringent constraints, including maximum power capacities, cross-layer interference limitations, and essential coverage demands. Confronting the complexity of the initial non-convex optimization challenge, we dissect it into more tractable sub-problems that encompass user association, UAV placement, and power allocation. Our approach employs an iterative algorithm, systematically resolving these sub-problems in sequence. Simulation results conclusively demonstrate the efficacy of the proposed scheme in optimizing system resource allocation and achieving comprehensive coverage.

Studying the characteristics of the satellite-toground (S2G) channel model is essential for the development and assessment of satellite communication systems. In this study, we introduce a unique approach by combining a low-earth-orbit (LEO) random geometric satellite channel model with line-of-sight (LoS) and single-bounced (SB) non-line-of-sight (NLoS) components for the S2G multiple-input multiple-output (MIMO) channel. By utilizing the coaxial cylinders reference model in a lightly shadow environment occluded by terrain features, we compute the space correlation function (SCF) and time correlation function (TCF). To simplify the simulation process, we present a deterministic simulation model using the finite number of scatterers and analyze the various factors that influence channel characteristics. The findings from the simulation indicate that the orientation of both the satellite and terrestrial receiver antennas, as well as their respective movement directions, distances, and the density of scatterers’ azimuth angles, all play a significant role in shaping the statistical properties of the channel model.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) can greatly enhance the capabilities of IoT networks by providing massive connectivity and timely computation. The paper presents a model and analysis of the performance for a large-scale grant-free (GF) SCMA aided MEC network. Firstly, stochastic geometry is used to derive closed-form solutions for offloading probability and SCMA ergodic rate. Then, the impact of SCMA on task completion time and energy cost in MEC networks is studied using queueing theory. Simulation results verify the validity of the theoretical expression and demonstrate that SCMA has advantages over orthogonal multiple access (OMA) in improving the offloading probability and ergodic rate, and reducing task latency and energy cost.

Abstract Traffic volume is increasing dramatically due to the quick development of technologies like online gaming, on‐demand video streaming, and the Internet of Things (IoT). The telecommunications industry's large‐scale expansion is increasing its energy usage and carbon footprint. Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. In this regard, a convolutional neural network‐based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. This method can more effectively lower energy consumption and carbon emissions when compared to other hierarchical algorithms based on conventional deep neural networks and non‐hierarchical algorithms. The suggested method is tested in three typical complicated networks with different network parameters to show its suitability in different network scenarios.

How to improve the coverage capability of network including connectivity and throughput is vital for enabling the Internet of Everything (IoE) in B5G/6G. However, the traditional terrestrial networks are confronted with the high-cost and inflexible challenges especially in the remote area and emergency applications. In order to solve these challenges, we consider a heterogeneous aerial network (HetAN), where some low-altitude base stations (LBSs) are deployed as access points for wireless coverage and a high-altitude base station (HBS) hovers as the hub for backhaul of LBSs. Furthermore, we apply the non-orthogonal multiple access (NOMA) to uplink transmission for the terrestrial users, which enable massive connectivity in the IoE. To maximize connectivity and throughput, we jointly optimize the LBSs’ deployment, power control, channel allocation, and rate control by fully exploiting the potential of the HetAN in wide-area coverage. For solving the formulated problem efficiently, we propose an iterative algorithm based on the methods of graph theory, bionic algorithm, and theoretical analysis. Simulation results are provided to reveal the influence of the control variables on network performance and indicate that our algorithm can greatly improve the connectivity and throughput with the other schemes.

Unmanned aerial vehicles (UAVs) are widely recognized as crucial supplementary component of 6G networks. Owing to the key attributes of UAVs (mobility, flexibility, and adjustable altitude), UAVs can serve as flying base stations (BSs), flying relays and mobile terminals in order to expand the service coverage and derive more applications. Softwarization is regarded as dominant attribute of network architecture of 6G, mainly realized by network function virtualization (NFV) and software defined networking (SDN). In this paper, we concentrate on researching the resource orchestration and allocation of end-to-end (E2E) slice services in softwarized UAVs-assisted 6G terrestrial networks. Problem models of UAVs-assisted 6G terrestrial networks and E2E slice are firstly introduced. Then, the problem formulation of resource orchestration and allocation of E2E slice is presented. Afterwards, one novel framework design, abbreviated as ReOrcAll-UAVs-6G, is detailed. When receiving one E2E slice, our ReOrcAll-UAVs-6G checks the available softwarized resources. If having available softwarized resources, our ReOrcAll-UAVs-6G turns to serving the slice and fulfilling slice’s tailored resource demands. During the orchestration and allocation phase, wireless and wired resource requests of this slice are considered and executed. Evaluation work and gained results of ReOrcAll-UAVs-6G and selected approaches are illustrated and analyzed. Gained results reveal that our ReOrcAll-UAVs-6G achieves apparent performance advantage, comparing with all selected approaches.

Network slicing (NS) is a highly promising paradigm in 5G and forthcoming 6G communication networks. NS allows for the customization of multiple logically independent network slices to provide tailored service for vertical applications with diverse quality of service (QoS) requirements. However, current research on NS primarily relies on the traditional modeling methods such as service function chaining (SFC) and task offloading, which have limitations in adapting to the evolving scenarios in 5G/6G networks. To address this, our study introduces one novel Self-adaptive Network Slicing (SNS) modeling method. In this approach, each service is abstracted as multiple SFC replicas originating from diverse access points. Based on the SNS modeling, we investigate a VNF configuration and flow routing (VCFR) problem for service provisioning in a multi-tier system. With the objective of achieving load-balancing with minimal slice operational expenditure, we formulate the VCFR as a mixed-integer linear programming. However, deriving an exact solution via MILP is computationally expensive due to its NP-hardness. To reduce computational complexity, we propose one Load Balancing-considered Community Detection-based Heuristic (LBCD-Heu), our divide and conquer approach, to solve the problem. In LBCD-Heu, we first design a load balancing-considered community detection method to divide the substrate multi-tier network into multiple independent communities. Following this, the MILP is employed in each community to obtain a near-optimal solution. Extensive evaluations justify that LBCD-Heu can effectively reduce the service operational cost and algorithm run-time while ensuring the load balancing of substrate network. Additionally, our results verify that the SNS modeling enables the provision of services at lower expenditures compared with traditional modeling methods.

Against the background of 6G communication and big data, more and more attention have been paid to the communication reliability and resource efficiency. Reconfigurable intelligent surface (RIS) is envisioned as a potential technology to improve the communication environment by controlling the electromagnetic wave propagation. A majority of related researches use the central limit theorem (CLT) to implement the analytical evaluation, which results in inaccuracy for the case with small number of reflective elements. In this letter, we investigate the outage behavior of RIS-enabled downlink cellular networks where exist several device-to-device (D2D) pairs under the general fading channels, i.e., Nakagami-m fading channels. We propose a comprehensive solution to evaluate the outage probability for all the cases with different numbers of reflective elements. Our solution utilizes the multivariate Fox's H-function and proposes the outage expressions in closed form. Finally, The accuracy of the closed-form outage probability is verified in simulation section for different cases of system configurations.

Network slicing is the most promising paradigm that enables the provision of services for aerial communication tasks, such as data traffic offloading, disaster relief, and data collection, with one shared physical network. However, current researchers primarily focus on the modeling of utilizing service function chaining (SFC) or task offloading, which have inherent limitations for areial communication tasks. While the former restricts the source-sink nodes to be fixed, the latter models network slicing as a whole. To further unleash the flexibility of network slicing, we introduce the hybrid network slicing (HNS) modeling in this paper. In our HNS, each aerial service is abstracted as multiple SFCs with dispersed source-sink nodes. These SFCs can be flexibly deployed in a unified or split manner based on the service workload and Quality of Service (QoS) requirements. Then, we formulate the HNS problem in a multi-tier network system as a mixed integer programming (MILP) problem and propose exact and community detection-based heuristic approaches to address the proposed problem. The simulation results demonstrate that the proposed heuristic approaches can effectively mitigate computational complexity than the exact method while providing near-optimal solutions for aerial communication tasks.

The emerging artificial intelligence (AI) puts forward high requirement for big data acquisition, which is difficult to be met with the existing communication technologies in real time. In this paper, we investigate new graph learning based access management scheme for supporting the real-time big data acquisition in the sixth-generation mobile communication system (6G). We model the network scene with a mass of communication links as a fully connected graph which takes into account the accumulative interference of all links. Then, the joint admission and power control problem is formulated as a combinatorial optimization problem. We propose a graph attention network (GAT) based algorithm which can learn the system features by weighted aggregation of neighbor nodes. In addition, we construct a differentiable loss function that can accurately express the optimization objective and train the network by the change of loss. Based on the output of the GAT, we iteratively optimize the link admission and power to active more links. Simulation results demonstrate that the proposed algorithm is superior to the traditional convex optimization based algorithms and the nonmodified GAT based algorithms in the number of activated links. Moreover, the training of the constructed network is unsupervised with high computational efficiency, which makes them suitable for the big data access management.

Integrated space-air-ground network (ISAGN) has been regarded as an important infrastructure of the next-generation network, which can offer massive access and seamless connections for users in a wide coverage area. This paper utilizes non-orthogonal multiple access (NOMA) technique to improve the spectrum efficiency of the ISAGN. Besides, two-way relay technique is introduced in ISAGN to boost the spectrum efficiency. Then, we conducted the ergodic capacity of two-way unmanned aerial vehicle (UAV)-aided ISAGN with NOMA. We first briefly establish a two-way UAV-aided ISAGN, by considering the imperfect channel state information (CSI) and successive interference cancellation (SIC). To obtain deeper insights, the closed-form expression of ergidic capacity for the considered system is derived. Finally, numerical simulations are provided to evaluate the performance of the system and reveal the impacts of imperfect factors.

The use of a large-scale antenna array has achieved significant performance gains in anti-jamming communications. However, due to the hardware cost and power consumption constraints, it is impractical to deploy such large-scale antenna array at the user side. Inspired by the remarkable advantages of reconfigurable intelligent surface (RIS), we propose an active-passive cascaded RIS-aided receiver architecture, which facilitate the deployment of a large-scale antenna array at the user side in a cost- and energy-efficient way and provides additional degree-of-freedom for beamforming design. Building upon this architecture and considering the practical angular channel state information (CSI) imperfection, a worst-case achievable rate maximization problem is formulated for anti-jamming communications. To handle the non-convex problem, a low-complexity optimization framework is proposed, where the new anti-jamming criterion, Pareto-dual scheme, unified unit-modulus zero-forcing scheme, and conventional-cyclic coordinate descent algorithm are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify that the proposed architecture and optimization framework are capable of achieving excellent performance with low complexity.

Integrated sensing and communication (ISAC) has been seen as a promising technology to satisfy the dual requirements of communication and sensing for the emerging applications in the next-generation wireless networks. In this paper, we research one down-link multi-cell orthogonal frequency division multiple access (OFDMA) ISAC system, in which a group of collaborative ISAC base stations send signals to their corresponding communication users, and concurrently work with multiple sensing receivers to estimate locations of multiple targets. Specifically, we investigate the joint sub-channel assignment and power allocation for users and targets to maximize the sum-rate, while ensuring the minimal signal-to-interference-plus-noise ratio (SINR) constraint for each user and the maximal Cramer-Rao lower bound (CRLB) requirement for each target. We propose a deep reinforcement learning (DRL) approach to address the above sub-channel assignment and power allocation problems. In our approach, we adopt the dueling deep Q network (DDQN) and the deep deterministic policy gradient (DDPG) network to output the sub-channel assignment policy and power allocation policy separately. Simulation results aim to prove the effectiveness of our proposed algorithm.

Space-aerial-ground integrated networks (SAGIN) is gaining eye-catching attention in 6G research. Comparing with terrestrial networks, SAGIN guarantees to provide three-dimensional (3D), seamless connectivity, global coverage and high resource usage efficiency. In addition, network softwarization (NetSoft) is recognized as the crucial attribute of 6G networks. With softwarization, traditional dedicated hardware will be decoupled into software blocks and general-purpose hardware. Tailored service requests can be implemented in the forms of chained software blocks (also called as slices) and coexist on top of these general-purpose hardware. The softwarization scheme can enhance the resource utilization and service diversity. Though SAGIN and NetSoft are separately studied well, their joint research is still in its infancy. In this paper, we focus on the research of softwarized SAGIN and propose one novel resource allocation and orchestration framework, labeled as Stice-Soft-SAGIN. The goal of our Stice-Soft-SAGIN framework is to provide reliable and efficient slicing service in quasi-static state. When receiving one slicing service request, our Slice-Soft-SAGIN will conduct the first procedure of available resource checking. After successfully doing the resource checking, our Stice-Soft-SAGIN will turn to conducting the slicing resource allocation and orchestration from three ordered parts (terrestrial part, aerial part, and satellite part). Take note that resources considered in Stice-Soft-SAGIN belong to wireless (spectrum) and wired (computing and storage) types. In order to validate the Stice-Soft-SAGIN, we conduct the evaluation in the simulation form. Evaluation results are illustrated and analyzed.

Satellite-terrestrial integrated networks (STINs) are troubled with the serious jamming threats in the counterwork environment. Non-orthogonal multiple access (NOMA) approach can not only improve the resource utilization by resource sharing, but also has the potential advantages to be used for anti-jamming. In this paper, under the threat of smart jammer with adaptive jamming policies, we investigate the NOMA-based anti-jamming problem in multi-cell STINs by jointly considering the NOMA-based user grouping in each cell and the beam allocation among multiple cells. Specifically, for each cell, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as the anti-jamming Stackelberg game and grouping game to obtain the equilibrium solutions. Then, an adaptive beam allocation algorithm with a low complexity is proposed to avoid allocation conflicts and achieve fairness among multiple cells. Finally, simulation results prove the performance of the proposed scheme.

By leveraging massive low-cost reconfigurable reflect array elements, intelligent reflecting surface (IRS) is recently proposed to exhibit the favorable wireless propagation environment of Internet of Things (IoT) systems. In this article, we provide a spectrum-efficiency approach by exploiting non-orthogonal multiple access (NOMA) as well as cognitive radio (CR) to form NOMA IRS-assisted CR system. In this IRS-based IoT, the active access point in secondary network transmits beamforming and the passive IRS elements are jointly operated to achieve different performance relying on demands of IoT devices, while maintaining the normal operation of primary network. Then, the exact closed-form formulas are introduced to evaluate outage probability at each IoT device. Moreover, to provide more insights of the system, a diversity order is considered aiming to look at limitation of outage performance when the system tries to increase average signal to noise ratio (SNR) at the secondary transmitter. Finally, we conduct numerical simulations to verify the superior performance of IoT systems with higher meta-surface elements at IRS over the necessary comparisons in practical scenarios.

Energy consumption is one crucial aspect in IoT and healthcare applications. Reconfigurable intelligent surface (RIS) is composed of man-made passive reflective elements which can configure the channel environment with lower energy consumption. In this paper, we focus on the RIS-assisted D2D networks. We obtain analytical closed-form expressions for the outage performance. Based on this, we then discuss the performance under high SNR case, as well as weak interference case. The corresponding closed-form simpler approximations are also presented. Due to the existence of interference, 0 order outage diversity is obtained. Numerical results show the agreement between Monte Carlo simulations and analytical results in various network configurations.

Wireless communication based on Unmanned aerial vehicle (UAV) is one of the important technologies in the future communication system. It is necessary to establish an accurate air-to-ground (A2G) wireless channel model. In this paper, a A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) is proposed. The internal vibration of the UAV is modeled as a sinusoidal random process, and the UAV wobble caused by the random air fluctuations is modeled as the uniform distribution random process. We derive the A2G channel temporal auto-correlation function (ACF) with UAV 3D wobbles, analyze the variation of the temporal ACF with different time instants, carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel ACF will decrease rapidly with the increase of the amplitudes of the wobble angles and the carrier frequency. This work contributes to the establishment of the next generation wireless channel model and the design of communication system.

In this paper, we study the coverage maximization problem in the aerial networks. Specifically, we propose a heterogeneous aerial network (HetAN) consisting of a high-altitude base station (HBS) acting as a hub to provide wireless backhaul and multiple low-altitude BSs (LBSs) acting as access points to provide on-demand wireless coverage. Besides, we adopt the non-orthogonal multiple access (NOMA) technique for the uplink transmissions of the terrestrial users so as to support massive connections. Then, we formulate a joint power control, channel assignment, and rate control problem with the objective to maximize user connectivity and network throughput. Based on the graph methods and theoretical analysis, we propose an efficient iterative algorithm to solve the formulated problem. Simulation results demonstrate that our algorithm outperforms the other schemes in terms of connectivity and throughput.

The Internet of Things (IoT) has gained rapid development, but due to the limited battery capacity and access capacity, there are many complex problems in the application. In this paper, we consider an air-and-ground cooperative wireless network, which can provide dynamic coverage for sensor equipments (SEs). Jointly considering the dynamic deployment of the aerial base stations (ABSs) and the admission-and-power control of the SEs, we formulate a two time-scale network control problem to minimize the long-term power consumption of all SEs under their individual rate requirement. On large time scales, we propose a particle swarm optimization algorithm (PSOA) to adjust the positions of the ABSs. On small time scales, we devise a joint admission-and-power control algorithm (JACA). Simulation results indicate that the air-and-ground network incorporated with the proposed algorithms can significantly reduce the total power consumption of the SEs compared with the other schemes.

With the continuous development of social science and technology, artificial intelligence identification has been widely used and plays a very important role in some special fields. Convolutional neural network has a good effect in image processing, so it is widely used in intelligent recognition scenario. Activation functions can help convolutional neural network better understand and fit complex function models, It is necessary to design an efficient activation function. This paper proposes a new convolutional neural network model based on improved activation function usage patterns, and the performances of three common used activation functions, including sigmoid function, tanh function and relu function, in centralized and decentralized training methods are detailed analyzed respectively. The experiment results show that the effect of repeated training with different activation functions is better than that of single linear rectification function in recognition accuracy and recognition of special cases, and the recognition speed is obviously faster than the traditional model. Furthermore, under the same activation function, when the number of training rounds and the training amount are small, the expected accuracy of centralized training is lower compared with that of decentralized training, but the detection accuracy is improved due to the detection mechanism.

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.

With the advent of the Internet-of-Things (IoT) era, more and more devices are connected to the IoT. Under the traditional cloud-thing centralized management mode, the transmission of massive data is facing many difficulties, and the reliability of data is difficult to be guaranteed. As emerging technologies, blockchain technology and edge computing (EC) technology have attracted the attention of academia in improving the reliability, privacy, and invariability of IoT technology. In this article, we combine the characteristics of the EC and blockchain to ensure the reliability of data transmission in the IoT. First, we propose a data transmission mechanism based on blockchain, which uses the distributed architecture of blockchain to ensure that the data is not tampered with; second, we introduce the three-tier structure in the architecture in turn; and finally, we introduce the four working steps of the mechanism, which are similar to the working mechanism of blockchain. In the end, the simulation results show that the proposed scheme can ensure the reliability of data transmission in the IoT to a great extent.

Summary Unmanned aerial vehicle (UAV) has a broad application prospect in the future, especially in the Industry 4.0. The development of Internet of Drones (IoD) makes UAV operation more autonomous. Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD. How to rationally allocate potential material resources has become an urgent problem to be solved. The main work of this paper is presented as follows: (a) In order to improve the optimization performance and reduce the computation time, we propose a multidomain virtual network embedding algorithm (MP‐VNE) adopting the centralized hierarchical multidomain architecture. The proposed algorithm can avoid the local optimum through incorporating the genetic variation factor into the traditional particle swarm optimization process. (b) In order to simplify the multiobjective optimization problem, we transform the multiobjective problem into a single‐objective problem through weighted summation method. The results prove that the proposed algorithm can rapidly converge to the optimal solution. (c) In order to reduce the mapping cost, we propose an algorithm for selecting candidate nodes based on the estimated mapping cost. Each physical domain calculates the estimated mapping cost of all nodes according to the formula of the estimated mapping cost, and chooses the node with the lowest estimated mapping cost as the candidate node. The simulation results show that the proposed MP‐VNE algorithm has better performance than MC‐VNM, LID‐VNE, and other algorithms in terms of delay, cost and comprehensive indicators.

5G communication technologies and networks help researchers and engineers look into intelligent transportation systems (ITS) with a new eye, including vehicular ad hoc networks (VANET) application. Network function virtualization (NFV) and network slicing (NS) are accepted as two most promising technologies towards the agile and elastic network architecture of 5G and beyond 5G (B5G). However, previous researchers studied NFV and NS separately. In addition, learning technologies, such as reinforcement leaning (RL), graph-based learning, emerge so as to enhance the network intelligence and resource allocation in recent years. Inspired from these, we jointly explore intelligent resource allocation issue within B5G-enabled VANETs. At first, the novel virtual resource allocation framework supporting NFV and NS for providing quality of service (QoS)-guaranteed slices is constructed. Then, we formulate the virtual resource allocation of slices as the optimization problem, having the goals of providing guaranteed QoS performance and maximizing the net profit. Considering the non convex attributes of the formulated optimization problem, we propose one intelligent and feasible algorithm instead, including the details of the proposed intelligent algorithm. We record the results in order to validate the feasibility and highlights of our proposed algorithm. For example, our intelligent algorithm has the slice acceptance advantage of 5%, comparing with the best existing work.

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism.

It is an irreversible trend to build a green and sustainable vehicular network facing with the dramatic increase in urban traffic. Reducing energy consumption has been an important aspect for green transportation. Reconfigurable intelligent surface (RIS) is considered as a promising technology to enhance the communication quality with higher energy efficiency. In this paper, we focus on the RIS-assisted vehicular networks. We obtain the closed-form analytical expressions for outage probability, ergodic achievable rate and average energy efficiency. A series of insights are further explored. Based on these, we discuss the performance under high SNR case, as well as, weak interference case. And then, the approximations in simpler form expressions are provided for each case, respectively. Outage diversity order and high SNR rate slope are also investigated. In addition, we propose a power allocation algorithm to maximize the ergodic achievable sum rate guaranteeing the outage probability and average energy efficiency. Numerical results show that our analytical results agree well with the Monte Carlo simulations in various network configurations. Besides, our proposed power allocation scheme significantly enhances the ergodic achievable sum rate compared with the equal power strategy.

The unbalanced traffic distribution is a severe problem in cellular networks, which leads to congestion and reduces spectrum efficiency. To tackle this problem, we propose an unmanned aerial vehicle (UAV)-assisted wireless network architecture in which UAV acts as relay to divert the traffic from the overloaded cell to its neighbor underloaded cell. Considering that UAV communications are easily eavesdropped, we use the secrecy capacity to evaluate the performance of the network. To fully exploit the advantages of the proposed architecture, we formulate a joint UAV position optimization, user association, and time allocation problem to maximize the sum-log-rate of all users in two adjacent cells. To tackle the complicated joint optimization problem, we first design a genetic-based algorithm to optimize the UAV position, and then use the branch-and-bound method to devise a low-complexity algorithm to get the optimal user association and time allocation schemes. The simulation results indicate that the proposed UAV-assisted wireless network architecture is superior to the terrestrial network, and the proposed algorithms can further improve the network performance in comparison with the other schemes.

In this paper, we focus on the energy saving problem for the wireless sensor networks (WSNs). Specifically, we propose a UAV-assisted wireless network architecture, where the cell-edge sensor devices (SDs) can access the aerial access points (AAPs) instead of the terrestrial access point (TAP). Since the transmitter-to-receiver distance is shortened and the ground-to-air channel is usually line-of-sight, the SDs can use lower power to transmit data and thereby prolong their lifetime. To fully exploit the potential of the network architecture, we jointly optimize the AAPs' position, channel allocation, and power control to minimize the total transmission power of all SDs. In order to solve the complex joint optimization problem, we reformulate it as three tractable subproblems and use the methods in graph theory to design low-complex algorithms. Simulation results indicate that the proposed network architecture greatly outperforms the traditional WSNs, and the proposed algorithms can further reduce the total power consumption.

With the rapid development of intelligent transportation, various computation-intensive applications have e-merged to improve the safety, efficiency, and comfort on the road. However, due to the mobility and resource dynamics, it is still a challenge for the resource-constrained vehicles to timely process computation-intensive tasks. Fortunately, the computation offloading in the Internet of Vehicles (IoV) greatly eases the contradiction between resource constraints and computing requirements. In this paper, we first present a collaborative computing architecture based on Edge-Cloud (EC) and Mobile-Edge-Platooning-Cloud (MEPC). Then, considering the priority of the Delay-Sensitive Tasks (DSTs), preemptive scheduling is introduced to deal with the hybrid tasks, comprised of DSTs and Delay-Tolerant Tasks (DTTs). Finally, a computation offloading problem based on the collaborative EC-MEPC architecture is established by jointly optimizing the decision-making and resource allocation issue. To solve the above problem, a distributed computation offloading and resource allocation algorithm is designed to achieve the optimal solution. Simulation results show that the proposed collaborative computing architecture and the distributed algorithm can effectively improve the delay and energy consumption performance of this system.

Buffer-aided relay technique can improve the diversity order and offer secrecy provision. To further improve secrecy performance, this paper proposes a secure link selection for relay networks where a new link selection policy is first designed under the constraint on the buffers and channel states using a Markov chain. The stationary state and the corresponding state transition matrix can be derived, and they are used to analyze the secrecy performance. Through the derivation of secrecy outage probability, we can get its closed-form expressions. Numerical results demonstrate that the proposed secure transmission scheme has a better performance than the conventional buffer-aided secure transmission schemes in terms of secrecy outage probability.

In this paper, we consider an unmanned aerial vehicle (UAV)-assisted relay network for B5G, where the UAV and the base station (BS) cooperated with each other to serve the cell-edge users. Aiming at this network, we propose a new decode-and-forward (DF) relay protocol incorporated with Non-orthogonal multiple access (NOMA) and maximum ratio combining (MRC), by which the coverage for the cell-edge users is enhanced. Theoretical analysis demonstrates that the proposed NOMA based relay protocol is superior to the traditional orthogonal multiple access (OMA) based relay protocol in terms of channel capacity. In order to make full use of the advantages of the proposed protocol, we formulate the 3D spatial position optimization problem of the UAV relay with the objective to maximize the sum-rate of all the cell-edge users. Based on the genetic algorithm, we propose an effective algorithm to solve the formulated problem. simulation results indicate that the proposed relay protocol greatly outperforms the traditional protocols, and the proposed algorithm achieves orders of magnitude speedup in computational time with only slight loss of performance.1

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.

Recently, novel tactile and flexible network services and applications emerge, along with their explosive growth of mobile data traffic. However, current internet cannot fulfill the demands of these network services. The upcoming 5G network, including the tactile internet, is designed by adopting softwarization and virtualization technologies, aiming at removing the rigidity of dedicated network hardware and implementing various network services in a flexible manner. One key technical issue in 5G network is the virtual resource allocation, known as virtual network embedding (VNE). However, existing studies focus on allocating virtual resource in the fixed underlying network, ignoring the effect of the mobile end nodes. As unmanned aerial vehicles (UAVs) will play an important role in 5G era, we incorporate UAVs into the 5G network in order to expand the coverage and agility of novel network services. In this paper, we conduct a research on the virtual resource allocation in UAVs-integrated 5G networks. The formal problem model for UAVs-integrated 5G networks is involved. A novel profit model, quantifying the UAV mobility for telecommunication service provider (TSP), is proposed. Then, we propose one virtual resource allocation algorithm, labeled as UAV-5G-VNE. Our UAV-5G-VNE consists of initial allocation part (UAV-5G-VNE-Ini) and re-allocation part (UAV-5G-VNE-Re). Our UAV-5G-VNE enables to predict all possible connecting access nodes of virtual UAV and makes implemented network services continued. In order to validate our UAV-5G-VNE efficiency, we conduct the experiments. Experiment results demonstrate that UAV-5G-VNE outperforms two benchmark algorithms, in terms of TSP profit and virtual service acceptance.

In the foreseeable future, data transmission and information exchange play important roles in intelligent transportation (ITS) systems. Heterogeneous networks (HetNets) currently emerge in order to provide large amount of data flow and customized network services. Virtualization technology is considered as one most promising approach towards HetNets implementation, aiming at managing virtualized HetNets resources in a convenient manner. In the virtualization research, the virtual resource allocation is another core technical issue. Though the issue has been well addressed in recent years, the secure virtual resource allocation has not been fully investigated yet. Hence, we research the secure virtual resource allocation for HetNets in this paper. Following the introduction of the security model for HetNets, we propose one effective heuristic strategy, incorporating the greedy and isolation methods. Experiment work is conducted so as to validate the efficiency of the heuristic strategy. Experiment results vividly reveal that our heuristic strategy, incorporating the greedy and isolation methods, performs better than the counterpart without using the isolation method, in terms of average virtual network service acceptance.

In this paper, we consider a downlink cell-free massive multi-input multi-output (mMIMO) system, assuming few-bit analog-digital converters (ADCs) and digital-analog converters (DACs) are implemented at the access points (APs). Leveraging on the linear additive quantization noise model (AQNM), we derive a tight approximate rate expression, which provides insights into the impacts of the imperfect quantization error and channel estimation error. Thanks to the trackable result, we quantitatively compare the performance differences between the two quantization models, namely the AQNM and the Bussgang theorem. In particular, the AQNM can offer analytical tractability for few-bit quantization while the Bussgang theorem only characterizes 1-bit quantization since the multi-bit quantization under the Bussgang theorem is difficult to deal with. Simulation results show that under the same 1-bit quantization, the rate performance with the Bussgang theorem is roughly identical to the case of the AQNM.

The penetration of electric vehicles (EVs) embedded with information and communication technology (ICT) devices and tools form a huge connected network that can be viewed as Internet-of-EVs(IoEV). The huge data gathered in IoEV network needs to be processed at cloud-based infrastructure which has abundant resources. However, due to the high mobility of the EVs, resource management from the remote cloud service providers has become one of the most difficult tasks to be performed in this environment. In this regard, data analytics fused with fog or edge computing can be leveraged to increase the resource availability in V2G environment where resources are provided to the EVs on the edge of the network. Keeping these points in mind, this paper presents a new framework for integration of cloud computing and IoEV on the edge of the network which provides flexibility to the end users for smooth execution of various applications. In addition, a resource allocation and job scheduling strategy for EVs at the edge of the network is presented in the paper. The results obtained with respect to various performance metrics confirm the applicability of the proposed scheme for future applications in V2G scenario.

Recently, network function virtualization (NFV) technology is strongly emphasized by the telecommunication industry, aiming at virtualizing physical resources, providing more agile and high-quality virtual network services and reducing telecommunication provider costs. In NFV environment, each network service (NS) is usually represented by a sequences of service function chains (SFCs). The issue of SFC composition, embedding and scheduling is regarded as the key issue in NFV research. Most of previous researchers focus on solving SFC embedding (SFC-E) problem and proposing effective embedding algorithms. Correspondingly, there exist dozens of embedding publications in the literature. However, few researchers studied the secure SFC-E problem. Hence, we research this topic and discuss the typical security risks in NFV-enabled networks. Then, we propose one novel and secure framework, labeled as No-Sec-SFC-E, in this paper. The goal of No-Sec-SFC-E is to ensure secure network function deployment and resource allocation in NFV-enabled networks. In No-Sec-SFC-E framework, virtual network functions (VNFs), having high security probabilities, are usually preferred. With the aiming of validating the proposed No-Sec-SFC-E framework, we conduct the experiment. Recorded experiment results vividly reveal the feasibility and effectiveness of NoSec-SFC-E.

In this study, the uplink performance of cell‐free massive multi‐input multi‐output (mMIMO) system with multi‐antenna access points (APs) and users is investigated, assuming low‐resolution analogue–digital converters (ADCs) are employed at the APs. By exploiting the additive quantisation noise model, a tight closed‐form rate expression is derived. This tractable finding characterises the impacts of the multi‐antenna APs and users, the imperfect quantisation error and the channel estimation error. In order to maximise the uplink sum‐rate, a joint quantisation bit and power control problem is formulated, subjecting to the backhaul capacity and each user power constraints. The original resource optimisation problem is non‐convex and it is decomposed into two sub‐problems, namely quantisation bit design and power allocation problem, to alleviate the difficulties. In particular, the resultant two sub‐problems can be efficiently determined by utilising the Lagrange Multiplier and sequential convex approximation methods, respectively. Finally, numerical simulations are presented to examine the analytical findings and evaluate the effectiveness of the proposed algorithm.

Future network based on software-defined networking (SDN) and network function virtualization (NFV) technologies is the main evolution tendency of current Internet, enabling telecommunication service providers (TSPs) to share their virtualized network resources with their contracted users in a flexible and economical manner. One key technical issue is virtualized resources allocation. In order to solve this issue, multiple mapping algorithms have been proposed. However, prior mapping algorithms focus on solving the allocation problem in one centralized underlying substrate network (SN), having the only goal of maximizing the TSPs' mapping revenue. As energy cost accounts for more than half of the total underlying network cost, it is crucial to minimize the total energy cost, while keeping high mapping revenue. In addition, in the real networking environment, multiple geographically distributed SNs, called interdomain networks, coexist. Hence, it is essential to embed each virtual network (VN) service among the interdomain SNs. Based on this, we first propose the formal problem model and the energy cost model. Then, we propose a novel and efficient mapping strategy, labeled EERID. Our EERID is able to map each VN service among interdomain SNs within polynomial time. The experimental results vividly reveal that EERID significantly reduces energy cost by approximately 18% over the existing energy-aware algorithms. At the same time, our EERID achieves higher embedding revenues than the existing energy-aware mapping algorithms, up to 23%.

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Virtualization is the fundamental attribute of next generation network. Embedding customized virtual networks (VNs) onto the underlying physical network (PN) is the core issue in virtualization research. This issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the re-embedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be re-embedded. The dynamic embedding algorithm ensures flexible VN assignment and improves the physical resource utilization. Finally, simulation results are illustrated in order to validate the strength of our proposed dynamic algorithm. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Virtual network embedding (VNE) refers to the resource allocation problem for network virtualization. Since its inception, multiple mapping algorithms have been proposed for embedding virtual networks (VNs) effectively and efficiently. However, prior mapping algorithms mostly complete the VN embedding in two separated stages: first node embedding and subsequent link embedding. Certain mapping algorithms embed the VN in one stage by using mixed integer linear programming method or subgraph isomorphism approach, involving high embedding completion time. Meanwhile, prior researchers conduct the VN embedding, on the basis of one underlying substrate network (SN). While in future VNE application, each VN must be mapped among multiple geographically distributed SNs. On above backgrounds, we propose a location aware and node ranking value driven embedding algorithm, labeled as LANRVD. The LANRVD enables to conduct the embedding in two coordinated embedding stages within polynomial time. In addition, the LANRVD embeds the VN among multiple geographically distributed SNs. Numerical results reveal that the LANRVD significantly improves VN acceptance ratio by 10% over existing typical two-separated-stages algorithms.

We investigate the downlink rate performance of cell-free massive multiple-input multiple-output (mMIMO) network with conjugate beamforming precoder when the access points (APs) are equipped with one-bit analog-digital converters (ADCs) and digital-analog converters (DACs). Based on Buss-gang decomposition theory, we derive a rigorous closed-form rate expression, which covers the impact of multi-antenna APs, the imperfect quantization error, and channel estimation error. Then, by exploring this closed-form result, we show that the quantization interferences resulted from one-bit quantization can significantly decrease the downlink rate performance. In addition, we also analyze the performance gain provided by adding the total number of antennas or increasing the total transmitted power. We observe that, adding the total number of antenna arrays is a promising way to compensate for the quantization losses. However, these losses cannot be compensated by infinitely increasing the total transmitted power.

In this paper, a multigroup multicast cell-free massive MIMO (CF-mMIMO) system with conjugate beamforming (CB) precoding is considered. In this system, M N-an-tennas access points (APs) distribute in the serving area and coherently serve J×K single-antenna users. All users are randomly divided into J multicast groups. A novel closed-form downlink rate's expression for any M, N, J, and K is derived, which motivates us to propose a weighted max-min power optimization algorithm. In designing this algorithm, the high rate requirements of the high priority groups and the egalitarianism principle are considered. Numerical results demonstrate that the performance improvement achieved by increasing N is visibly better than increasing M. Furthermore, the proposed weighted maxmin fairness algorithm performs well in many respects.

In the area of network virtualisation, virtual network embedding (VNE) refers to the resource allocation problem. In the literature, researchers have proposed multiple VNE algorithms. These algorithms have the goal of accommodating as many requested virtual networks (VNs) as possible. However, most of prior embedding algorithms belong to the two‐stage (separated node and link embeddings) mapping algorithm category. Certain embedding algorithms embed each VN in one mapping stage by using mixed integer linear programming approach or graph theory, having very high computation time. There is a lack of heuristic algorithms, enabling to embed nodes and links per VN in one mapping stage. In addition, each requested VN embedding needs to be completed in polynomial time so as to be promoted to future dynamic VN service application and real‐time VNs embedding. Based on these backgrounds, the authors propose a novel real‐time and one‐stage heuristic mapping algorithm (VNE‐RTOS). Numerical evaluations are conducted to strengthen that VNE‐RTOS earns more embedding revenues by 8% over typical two‐stage heuristic embedding algorithms (e.g. VNE‐TAGRD) while achieving the same substrate resource utilisation.

Virtual network embedding (VNE) refers to the resource allocation problem in network virtualization (NV). In the literature, researchers have proposed multiple VNE algorithms. These algorithms aim at embedding more and more requested virtual networks (VNs) onto the underlying networks and maximizing embedding revenues. Prior VNE algorithms mostly belong to the two-stage (separated node and link embedding) mapping algorithm category. Some other VNE algorithms embed each VN in one stage by using mixed integer linear programming (MILP) approach. There is a lack of one-stage heuristic algorithm, enabling to embed nodes and links in one mapping stage. In addition, each requested VN needs to be mapped in polynomial time so as to be promoted to future dynamic VN service application and real-time VNs embedding. Therefore, we propose a real-time and one-stage heuristic mapping algorithm (VNE-RTOS). Numerical simulations are conducted to validate that our VNE-RTOS earns more embedding revenues by approximately 3.4% over typical two-stage heuristic embedding algorithms (e.g. GRD-VNE) while achieving the same substrate resource utilization.

Network virtualization (NV) is a promising approach to remove the ossification of current Internet. Virtual network embedding (VNE) is the key issue in NV which efficiently and effectively maps various of virtual networks (VNs), with different node and link resource requests, onto the shared substrate network(s) with finite underlying resources. Previous VNE algorithms in the literature are mostly heuristic. Single network topology attribute and each node's local resources are assisted to rank nodes in most heuristic algorithms, leading to inefficient resource utilization of substrate network in the long run. To deal with this issue, we propose the network topology attribute and network resource-considered algorithm (VNE-NTANRC). The VNE-NTANRC algorithm adopts a novel node-ranking approach to rank all substrate and virtual nodes before embedding each given VN. The novel node-ranking approach has two subapproaches and considers five important network topology attributes and global network resources altogether. One subapproach is able to calculate all node values (NoV) directly. The other subapproach, stimulating from the Google PageRank website algorithm, enables to calculate NoVs in a stable state. Simulation results reveal that VNE-NTANRC algorithm outperforms typical and latest heuristic algorithms, only considering single network topology attribute and local resources.

Network virtualization (NV) is widely accepted as one enabling technology for future network, which enables multiple virtual networks (VNs) with different paradigms and protocols to coexist on the shared substrate network (SN). One key challenge in NV is VN embedding (VNE), which maps a VN onto the shared SN. Since VNE is NP-hard, existing efforts mainly focus on proposing heuristic algorithms that try to achieve feasible VNE in reasonable time, consequently the resulted embedding is not optimal. To tackle this difficulty, we propose a candidate assisted (CAN-A) optimal VNE algorithm with lower computational complexity. The key idea of the CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding. This reduces the mapping execution time substantially without performance loss. In the following embedding, four types of node and link constraints are considered in the CAN-A algorithm, making it more applicable to realistic networks. Simulation results show that the execution time of CAN-A is hugely cut down compared with pure VNE-MIP algorithm. CAN-A also outperforms the typical heuristic algorithms in terms of other performance indices, such as the average VN request acceptance ratio and the average virtual link propagation delay.