Using solar power to drive ground sensors in a UAV-IoT MEC system deployed in inaccessible or hazardous areas provides a sustainable solution to battery replacement for IoT sensors. Nevertheless, this approach faces two critical challenges. Firstly, terrain variations and landscape shadowing cause uneven light distribution, leading to significant disparities in solar energy harvesting among nodes, which subsequently affects system throughput fairness due to unequal energy availability for data computation and task offloading; Secondly, atmospheric attenuation dynamics introduce stochastic variations in solar panel output, resulting in energy conversion instability and potential temporal battery outages. These challenges are further aggravated by the randomness of data arrival, which can destabilize the data queue. To address these difficulties, in this paper, we first design an α-fairness utility function to tackle the throughput fairness issue. After that, to handle the randomness of energy and data arrivals, we employ a Lyapunov-based optimization approach to maximize the long-term system utility function, formulating the problem as a multi-stage online stochastic optimization, with time average constraints on solar energy supply, data queue stability, and energy consumption of the sensor. We then decompose the original problem into a series of deterministic per-slot optimization problems to decouple control solutions across slots. Afterward, we iteratively optimize the data admission control, communication and computation resource allocations, and the UAV’s trajectory in each slot. The proposed scheme has low computation complexity for online execution. Extensive simulations demonstrate its effectiveness in achieving application-specific throughput fairness while maintaining energy and data queue stability under fluctuating working conditions. In addition, compared with benchmark algorithms, our scheme achieves higher system throughput through more judicious resource management and trajectory control strategies.

In emergency communications constrained by bandwidth limitations, unmanned aerial vehicle (UAV)-based coded caching presents a promising approach for the efficient dissemination of high-bandwidth-demanding services. This paper focuses on content download and content repair in aerial caching networks, where UAVs deliver contents to both ground users and invalid UAVs. To address potential data loss due to limited power and high mobility, fault-tolerant codes are utilized to maintain data availability and reliability. Initially, we derive the expressions of communication cost and success rate for content download and content repair. The size of coded fragments, determined by the coding design, affects both the success rate and transmission cost, while the resource allocation, which influences the cooperative relationships, also impacts these two aspects. The interplay between coding design and resource allocation is thus established to jointly optimize the overall performance. Then, we design a joint optimization problem of erasure coding schemes, coding parameters, matching relations, and UAV trajectories to maximize the overall success rate. Moreover, we propose a hierarchical multi-agent parameterized deep Q-network (H-MA-PDQN) algorithm integrating a dual-component structure for long-term coding and immediate resource allocation to solve the mixed integer nonlinear programming (MINLP), and each agent employs a PDQN with hybrid discrete-continuous action space. Simulation results demonstrate that our proposed H-MA-PDQN algorithm increases the success probability by 26.7% and 66.7% and reduces the transmission cost by 27.3% and 42.9% compared with the DQN and greedy-based strategies, respectively.

In emergency scenarios, unmanned aerial vehicles (UAVs) show significant potential as aerial base stations (BSs) to establish reliable communication links and provide localization services through integrated air-ground collaboration. This paper proposes a novel energy-efficient collaborative framework based on the solo-UAV-rescuer cooperative (SURC) paradigm, which synergistically enhances both communication capacity and localization accuracy. From a system optimization perspective, we formulate an optimization problem using a normalized combination of three critical metrics: achievable data rate, localization accuracy, and energy consumption. Specifically, to maximize the system’s utility, we design a signal perception-based localization method that incorporates angle-of-arrival (AOA) localization information for guidance, and develop a beamforming scheme to facilitate high data rate communication. Building on these methods, we propose a deep reinforcement learning (DRL)-based synergistic communication and localization reinforcement (SYNCORE) approach that dynamically optimizes three key operational parameters: UAV trajectory planning, flight time, and transmission power control, achieving reliable services with energy-efficient operation. Based on the simulation results, we validate that the proposed scheme enhances communication and localization performance, while also improving energy efficiency, surpassing the baseline schemes.

Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This makes RSMA particularly suitable for high-mobility autonomous driving, where ultra-reliable and low-latency communication (URLLC) is essential. To address the stringent requirements, this study enables RSMA finite blocklength (FBL) transmissions and explicitly evaluates the ergodic performance. We derive the closed-form lower bound for the ergodic sum-rate of RSMA, considering vital factors such as the vehicle velocities, vehicle positions, power allocation of each stream, blocklengths, and block error rates (BLERs). To further enhance the ergodic sum-rate while complying with quality of service (QoS) rate constraints, we jointly optimize the global power coefficient, private power distribution, and common rate splitting. Guided by gradient descent, we first adjust the global power coefficient based on its sum-rate solution. This parameter regulates the power state of the common stream, allowing for dynamic activation or deactivation: if active, we optimize the private power distribution and adjust the common rate splitting to meet minimum transmission constraints; if inactive, we use the sequential quadratic programming for private power distribution optimization. Simulation results confirm that our RSMA scheme significantly improves the ergodic performance, reduces blocklength and BLER, surpassing the RSMA counterpart with average private power and space division multiple access (SDMA). Furthermore, our approach is validated to guarantee the rates for users with the poorest channel conditions, thereby enhancing fairness across the network.

In mountainous environments, terrain occlusion causes non-line-of-sight (NLoS) transmission, significantly reducing the signal propagation range. To improve emergency rescue efficiency, a mobile unmanned aerial vehicle (UAV)-based integrated localization and communication (ILAC) network should be deployed to achieve optimal performance through adaptive trajectory planning and resource allocation. However, irregular and unpredictable terrain occlusions, coupled with dynamic users, make traditional optimization ineffective and reinforcement learning (RL) inefficient. To address these challenges, this paper proposes a hybrid action space soft Actor-Critic with geographic information-based state aggregation (GeoAgg-HSAC) decision-making scheme. First, an RL state aggregation method based on graph contrastive learning is designed. Through a pre-trained graph neural network (GNN), the UAV network states experiencing the same occlusion are mapped to similar low-dimensional representations. This method reduces the state dimension and allows similar states to share policy experience, thereby improving sample efficiency and accelerating convergence. A hybrid action space SAC network is then designed, which simultaneously makes decisions for continuous UAV trajectories and discrete resource allocation. Finally, a simulation environment based on real mountain terrain and wireless data is built for the experiment. The experimental results show that the proposed scheme has significant advantages for optimizing communication and localization performance.

Using solar power to drive ground sensors in a UAV-assisted IoT MEC system deployed in inaccessible or hazardous areas offers a sustainable solution to battery replacement for IoT sensors. However, the uneven distribution of solar power leads to unbalanced throughput among sensors. Additionally, fluctuations in solar energy and the stochastic nature of data arrivals destabilize the energy and data queues. To address these issues, we first design an α-fairness utility function to ensure throughput fairness. Then, to stabilize the system queues, we employ Lyapunov optimization to maximizing the utility by formulating it as a multi-stage online stochastic optimization problem. We decompose the original problem into a series of deterministic per-slot optimizations and iteratively optimize data admission control, resource allocation, and the UAV’s trajectory in each time slot. The proposed scheme achieves the desired level of throughput fairness in time-varying environments. Moreover, compared to benchmark algorithms, it attains higher system throughput and energy efficiency.

Next-generation mobile networks are expected to facilitate fast AI model deployment on end devices (EDs). By enabling collaborative model caching across EDs, mobile networks can efficiently support distributed AI inference services through device-to-device (D2D) cooperation. In this paper, we investigate a D2D-assisted model caching and collaborative computing framework that aims to balance the trade-off among inference delay, accuracy, and energy consumption by managing model caching, data offloading, and computation resources efficiently during the provisioning of diverse AI services. Specifically, considering the AI performance is constrained by multi-dimensional resources, a new metric named Service Hit Rate (SHR) is proposed to decouple the joint impacts of computation, communication, and caching resources on service success. Aiming to maximize the SHR, we propose a matching-aided multi-agent reinforcement learning (MARL) framework. First, a hierarchical bipartite matching algorithm is utilized for model deployment and helper assignment. Then, an attention-based MARL algorithm is employed to allocate computation resource for models cached on the same EDs. Simulation results demonstrate that the proposed algorithm significantly improves the system SHR for AI services.

Integrated sensing and communication (ISAC) has been recognized as a key technology in the low-altitude economy. Leveraging the flexibility and high maneuverability of unmanned aerial vehicles (UAVs), we propose a multi-UAV enabled ISAC system to provide communication and tracking services for multiple ground mobile targets (GMTs). By jointly optimizing the communication scheduling and UAV trajectory, we aim to maximize the system rate while guaranteeing tracking demands, subject to anti-collision and energy consumption constraints. Specifically, we decompose the original non-convex optimization problem into two subproblems and develop an efficient approach based on successive convex approximation (SCA) to solve them iteratively. Numerical results demonstrate that the proposed multi-UAV enabled system achieves superior communication performance through the joint optimization of scheduling and trajectories, while fulfilling real-time tracking requirements.

In mountainous emergency rescue operations, due to terrain occlusion, signals are often in a non-line-of-sight (NLoS) transmission state, resulting in a significant reduction in signal propagation distance. It is necessary to deploy an integrated positioning and communication (ILAC) network based on unmanned aerial vehicles (UAVs) to achieve optimal performance through trajectory planning and resource allocation. However, the complex and unpredictable terrain occlusion, coupled with dynamic user behavior, makes it difficult for traditional optimization methods to work, while the direct application of reinforcement learning (RL) is inefficient. To address these challenges, this paper proposes a hybrid action space soft actor-critic (GeoAgg-HSAC) decision framework based on geographic information state aggregation. First, a state aggregation method based on graph contrastive learning is designed. In this method, a pre-trained graph neural network (GNN) is used to map the UAV network state under similar occlusion conditions to a corresponding low-dimensional representation, thereby reducing the state dimension and allowing similar states to share policy experience, which improves sample efficiency. Then, a hybrid action space SAC network is constructed that can simultaneously make decisions on continuous UAV trajectories and discrete resource allocation. Experiments based on real mountain terrain and wireless data show that the proposed approach has significant advantages in optimizing communication and positioning performance.

Edge-cloud collaborative inference is a widely adopted approach in edge intelligence, especially for latency-sensitive tasks such as drone inspection, augmented reality, and disaster relief. To adapt to varying network bandwidth and limited computational capacity, model splitting methods divide Deep Neural Networks (DNNs) at specific split points into edge and cloud segments. However, current methods suffer from high switch latency and memory overhead, and are incompatible with Feature Pyramid Network (FPN)-based multi-branch models. To address the above issues, we propose a dynamic model splitting framework (DMSF) for edge-cloud collaborative inference. We design switchable compress–recover modules after each layer, enabling a single model to support all split points and significantly reduce memory overhead. The optimal split point is selected based on bandwidth and edge computational capacity, and the corresponding compress–recover path is activated for fast switching. For FPN-based multi-branch models, we remove the multi-scale branches from the backbone to the FPN and present hierarchical compensation modules in DMSF, making the model more suitable for splitting while maintaining perception accuracy. Experimental results show that DMSF reduces memory overhead by 75%, achieves millisecond-level switching, and outperforms the existing method with 32.0% lower latency and 12.2% higher perception accuracy.

In complex environments such as forested and mountainous regions, traditional Kalman filtering (KF) and its derivative methods for localization suffers from severe performance degradation due to inaccurate state transition model and time-varying non-zero-mean observation noise. To address these challenges, this paper makes two key contributions. First, we construct real-world UAV-based dynamic localization datasets collected in representative forest environments of China using integrated sensing and communication (ISAC) mesh devices. Second, we propose a novel framework, termed learning-enhanced adaptive Kalman filtering with non-line-of-sight (NLoS) ranging compensation (LAKF-NRC). This framework preserves the theoretical optimality of the KF in fusing predictions with measurements, while integrating two neural networks into the filtering process to jointly improve measurement and prior prediction accuracy. For measurement correction, a state-feedback TimesNet is designed to extract frequency-domain features for correcting NLoS-induced ranging errors and predicting the observation noise covariance. For prior prediction optimization, a state-feedback long short-term memory (LSTM) is developed to learn nonlinear state transition model and process noise covariance. Extensive experiments demonstrate that LAKF-NRC significantly outperforms extended Kalman filter (EKF), unscented Kalman filter (UKF) and advanced KalmanNet method in dynamic localization tasks, providing a physically consistent and generalizable solution for real-world state estimation.

The Longest Common Subsequence (LCS) problem is fundamental in bioinformatics and file difference comparison algorithms, yet its conventional dynamic programming (DP) approach has a time complexity of$\mathcal{O}(m n)$, making it computationally expensive for large-scale inputs. Parallelization offers a promising solution to accelerate the LCS computation. Existing methods parallelize its computation along the antidiagonal direction of the DP matrix but suffer from poor cache utilization and high communication overhead. In this paper, we propose a novel parallel LCS algorithm that combines antidiagonal tiling with lightweight semaphore-based synchronization to enhance cache locality and reduce inter-thread communication costs. Our approach partitions the DP matrix into localized tiles along anti-diagonals and employs lightweight semaphores to guarantee dependency constraints, significantly improving the computation efficiency. Experimental results on sequences of length$\mathbf{1 0 k}$demonstrate that our algorithm achieves a$6.9 \times$speedup ratio over the serial version, outperforming both well-known anti-diagonal parallelization method and task-queue-based method.

Model-based shallow machine-learning methods and data-driven deep-learning (DL) methods have been advanced to address hyperspectral and multispectral image fusion (HS–MS fusion). Nonetheless, model-based approaches, which meticulously craft regularization terms within optimization models using hand-engineered priors, often struggle to pinpoint the optimal solution efficiently. DL-based methods, which train on extensive datasets to learn a non-linear mapping for generating a high spatial and spectral resolution image (HS2I), exhibit limited generalization capabilities when applied to novel and diverse test datasets. To improve the generalization ability and optimization efficiency of the existing HS–MS fusion methods, a novel deep low-rank prior (DLRP)-based self-supervised HS–MS fusion approach is devised. It incorporates a low-rank learning paradigm to produce the fused HS2I, subject to the constraints imposed by loss functions. Instead of deriving the solution via solving a low-rank approximation optimization problem, deep image prior (DIP) learned by a two-dimensional CNN and a one-dimensional CNN are integrated as the low-rank prior.

Radio maps enrich radio propagation and spectrum occupancy information, which provides fundamental support for the operation and optimization of wireless communication systems. Traditional radio maps are mainly achieved by extensive manual channel measurements, which is time-consuming and inefficient. To reduce the complexity of channel measurements, radio map estimation (RME) through novel artificial intelligence techniques has emerged to attain higher resolution radio maps from sparse measurements or few observations. However, black box problems and strong dependency on training data make learning-based methods less explainable, while model-based methods offer strong theoretical grounding but perform inferior to the learning-based methods. In this paper, we develop a deep unrolled low-rank tensor completion network (DULRTC-RME) for radio map estimation, which integrates theoretical interpretability and learning ability by unrolling the tedious low-rank tensor completion optimization into a deep network. It is the first time that algorithm unrolling technology has been used in the RME field. Experimental results demonstrate that DULRTC-RME outperforms existing RME methods.

The increasing deployment of unmanned aerial vehicles (UAVs) as mobile communication relays in urban environments necessitates accurate 3-D modeling of complex urban areas for optimal communication. Current practices involve LiDAR-based 3-D scanning to generate point cloud data; however, sensor limitations and adverse weather conditions may compromise data quality. This study proposes a new multimodal point cloud and image fusion completion network (PIFC-Net) based on a generative adversarial network (GAN), specifically tailored for large-scale urban environments. The experimental study tested five different building shapes and various objects, and the results commend the network for its effectiveness in enhancing the quality and efficiency of point cloud completion.

Electricity data plays a pivotal role in power management systems. Smart meters, as key tools for recording this data, often encounter anomalies due to meter malfunctions, operational errors, or unauthorized electricity usage, all of which jeopardize the stability of power grids. To this end, we propose the future-guidance anomaly detection network, called FG-Net, designed for real-time analysis of electricity time series data. FG-Net is designed to memorize historical data and assimilate future data, ensuring comprehensive learning of complete data information. Specifically, we leverage the comprehensive data insights gained from a complete information network to guide the predictions of the historical information network. Subsequently, we developed a self-matching feature guidance (SFG) strategy that harnesses the strengths of the complete information network to offset the limitations of the historical information network, thus providing effective guidance. The experimental results on two power grid time series datasets with different anomaly volatility, the Low Carbon London dataset and the Ausgrid Solar Home dataset, demonstrate the proposed anomaly detection method’s accuracy and efficiency.

Drone-based remote sensing has become pivotal for high-resolution dynamic monitoring. However, the differences between day and night modes will trigger a mismatch in multi-scale object features under extreme lighting conditions. In this paper, we propose a dual-stream hierarchical interaction network for multimodal drone object detection, called DHANet, which enhances the distinguishability between multi-scale objects and background for each modality. Specifically, DHANet is designed with a Modality-Adaptive Asymmetric Attention Module (M-AAM) that enhances object-level semantic representations through global and local attention mechanisms. The M-AAM employs global context attention and local positional attention to replace conventional multi-scale context extraction, thereby effectively integrating spatial-channel information of objects. Furthermore, the network is equipped with a Multimodal Scale-Attentive Convolution (M-SC) module that dynamically generates modality-specific feature aggregation weights. This design enables global cross-modality information fusion while reducing computational complexity. Experimental results on two multimodal remote sensing benchmark datasets (DroneVehicle and VEDAI) and two natural datasets (LLVIP and FLIR) demonstrate the robustness and generalizability of DHANet. The codes will be openly and freely available at https://github.com/Victoria-xin1009/-IEEE TGRS DHANet for the sake of reproducibility.

The evolution of future network and control technologies has enabled unmanned aerial vehicles (UAVs) to collaborate across diverse geographical areas and task domains, enhancing task execution efficiency through data and resource sharing. In response to the increasing demand for cross-domain task allocation and operations for UAVs, establishing robust authentication mechanisms within trusted domains has become a critical foundation for ensuring secure cross-domain access. Despite significant progress in UAV identity authentication and cross-domain access, challenges persist, such as cumbersome and inefficient processes, UAV resource limitations, and establishing trust relationships across different domains. To address these challenges, this paper introduces a dual blockchain-assisted trusted authentication scheme for UAVs' cross-domain access. Our approach utilizes a certificateless signcryption algorithm for lightweight UAV authentication, thereby eliminating the need for certificate management. Then, an efficient credit-based trust model is designed to measure the trustworthiness of data-in-transit and cross-domain entities. Furthermore, blockchain technology is introduced to store the relevant information of UAVs and credibility to assist cross-domain authentication. Theoretical security analysis and extensive simulations have been conducted, demonstrating the effectiveness and efficiency of our proposed scheme.

Formal verification becomes increasingly critical to ensure system functionality, reliability and safety as they grow in complexity. Existing methods tend to focus on a single dimension of system aspects—such as control flow, data flow or timing constraints—or, at most, consider two of these perspectives without integrating all three. In addition, data flow models generally represent high-level data abstraction without including operational details within underlying contexts. The inability of these models to capture system behavior undermines their reliability, ultimately increasing the likelihood of the corresponding systems malfunctioning. To address these issues, we propose a formal verification method based on a timed Petri net with database tables (TPDT-net). First, we model the system using TPDT-net and generate its state reachability graph (SRG). Next, we extend timed computation tree logic (TCTL) by introducing database-related data element operators, thus proposing a database-oriented TCTL (DTCTL) model checking method. In addition, we formalize the system correctness problem as corresponding DTCTL formulas, which are analyzed based on the SRG. This approach transforms correctness verification into a satisfiability problem of DTCTL formulas within the SRG. Finally, we validate the practicality and effectiveness of the proposed method through case studies and experiments.

In this paper, we investigate a full-duplex (FD) cell-free massive multiple-input multiple-output (mMIMO) system. To cope with the cross-link interference, especially the interaccess point (inter-AP) interference, we formulate a sum spectral efficiency maximization problem by jointly optimizing the digital and analog beamforming at FD AP and digital precoder at the uplink user equipment. To solve it, we present a fully digital design first by utilizing successive convex approximations and majorization-minimization to obtain a closed-form solution in each iteration. After that, we exploit the Riemannian manifold to get a hybrid design, which is shown to approximate the fully digital one well. Simulation results show that the proposed design enhances the sum spectral efficiency as well as uplink performance effectively when compared to other benchmarks.

Distributed DNN inference is becoming increasingly important as the demand for intelligent services at the network edge grows. By leveraging the power of distributed computing, edge devices can perform complicated and resource-hungry inference tasks previously only possible on powerful servers, enabling new applications in areas such as autonomous vehicles, industrial automation, and smart homes. However, it is challenging to achieve accurate and efficient distributed edge inference due to the fluctuating nature of the actual resources of the devices and the processing difficulty of the input data. In this work, we propose DistrEE, a distributed DNN inference framework that can exit model inference early to meet specific quality of service requirements. In particular, the framework firstly integrates model early exit and distributed inference for multi-node collaborative inferencing scenarios. Furthermore, it designs an early exit policy to control when the model inference terminates. Extensive simulation results demonstrate that DistrEE can efficiently realize efficient collaborative inference, achieving an effective trade-off between inference latency and accuracy.

In this paper, we present a communication and localization co-design (CLCD) scheme tailored for unmanned aerial vehicle (UAV) assisted emergency networks, with the goal of enhancing rescue operation efficiency and network resource utilization. Specifically, we delve into the mechanism of mutual benefit between communication and localization in UAV wireless networks, establishing a utility function that combines communication rate and localization error. Building on this, we develop a beamforming scheme to facilitate high data rate communication, incorporating angle of arrival (AOA) localization information for guidance. A deep reinforcement learning (DRL)-based communication and localization coordinated optimization (CLCO) algorithm is further proposed to optimize the UAV trajectory and the transmit power in real-time, guaranteeing reliable communication and localization services. Extensive simulation results validate our approach, showcasing up to a 40% improvement in joint utility compared to baseline schemes.

With bandwidth constraints in emergency communications, device-to-device (D2D)-based coded caching emerges as a solution for efficiently transmitting high-bandwidth-demanding services. In this article, we investigate content sharing between emergency vehicles and mobile users via D2D communications in the emergency networks by exploiting coded caching schemes. The joint optimization of coding schemes, coding parameters, and matching relations is proposed to maximize the overall success probability of content sharing while minimizing the overall transmission cost. The interplay between coding parameters optimization and resource allocation is investigated by both download and repair process. Moreover, we propose a multi-agent parameterized deep Q-network (MA-PDQN) algorithm to solve the mixed integer nonlinear programming (MINLP), with each agent employing a PDQN with hybrid discrete-continuous action space. Simulation results show the effectiveness of the proposed algorithm in improving success probability and reducing transmission cost.

In this paper, we propose a multi-dimensional resource allocation scheme for Internet of Remote Things (IoRT) data collection in a high altitude platform (HAP)-assisted unmanned aerial vehicle (UAV) network. Considering the quality of service (QoS) requirements of delay-sensitive IoRT data, we propose a UAV-HAP double relay data transmission mode to reduce the transmission delay for delay-sensitive data. Since the resources of the UAV are limited, we jointly optimize communications, computing and storage resources to maximize the utility of the considered system. Due to the high dimensionality of the solution space, we design a Twin Delayed Deep Deterministic policy gradient-based multi-dimensional resource allocation (TD3-MDRA) algorithm to find the optimal resource allocation strategy. Extensive simulation results are presented to demonstrate the superior performance of TD3-MDRA for IoRT data collection with delay and resource constraints.

In achieving effective emergency response, the timely acquisition of environmental information, seamless command data transmission, and prompt decision-making are crucial. This necessitates the establishment of a resilient emergency communication dedicated network, capable of providing communication and sensing services even in the absence of basic infrastructure. In this paper, we propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I). The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment. E-SC3I ensures rapid access to a large user base, reliable data transmission over unstable links, and dynamic network deployment in a changing environment. However, these advantages come at the cost of significant computation overhead. Therefore, we specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning. Experimental results demonstrate our method's ability to achieve rapid inference of AI models with constrained computational and communication resources.

Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation.

Integrated sensing and communication (ISAC) has emerged as a new paradigm for the sixth generation (6G) mobile communication systems. However, embedding ISAC into a conventional communication system may degrade the mutual benefit of radar sensing and communication due to the low-spectral efficiency and weak interference management. Therefore, in this article, we propose a full-duplex (FD) non-orthogonal multiple access (NOMA)-enabled ISAC framework in which a dual functional base station (BS) operates simultaneous target detection and uplink/downlink communication with the same temporal and spectral resources. To exploit some insights into the benefit of such a framework, we investigate the sensing signal processing procedure and communication model. Towards this end, a joint transmit and receive beamforming design is studied in the cases where single-target detection with perfect channel state information (CSI) and multitarget detection with imperfect CSI are considered, respectively. The corresponding optimization problems aim to maximize the sensing signal-to-interference-plus-noise ratio (SINR), subject to uplink communication SINR requirement for each uplink user equipment (UUE) and downlink communication SINR requirement for each downlink user equipment (DUE). We propose an alternating-optimization algorithm to solve the formulated non-convex optimization problems efficiently. Specifically, at each iteration, the closed forms of the optimal sensing and uplink communication receive beamforming vectors are obtained, respectively. Then, the sub-optimal transmit beamforming vector is solved by equivalent transformation and semi-definite relaxation (SDR) method. Numerical results demonstrate that the proposed FD-NOMA ISAC system outperforms the orthogonal multiple access (OMA)-based ISAC in terms of both sensing and communication performances. Notably, the efficacy of the proposed scheme is heavily dependent on factors, such as self cancelation (self interference) and CSI uncertainty.

Utilizing the unmanned aerial vehicle (UAV) for task offloading over a large geographic area offers a promising solution to guarantee information freshness, i.e., Age of Information (AoI), in many of Internet of Things (IoT) applications. However, the energy limitations of both ground devices (GDs) and UAV wireless networks necessitate intelligent management of energy resources, as continuous energy consumption is involved in data sensing, transmission, and computation. Incorrect decision-making can exhaust the UAV’s energy prematurely, endangering the efficacy of task offloading missions and potentially causing damage to the UAV itself. In this article, we investigate the problem of task offloading in an UAV-aided wireless powered edge computing system with a focus on enhancing information freshness while ensuring the UAV’s energy-safety. To minimize the average AoI, we propose to jointly optimize GD wireless charging power, UAV flight trajectory, and offloading decisions. To prevent premature energy depletion in UAV operations, we formulate the optimization problem as a constrained Markov decision process (CMDP). Then, we introduce a novel safe deep Q-network (SDQN) algorithm, leveraging Lyapunov equations to derive an optimal strategy, which can strictly ensure that the actions of the UAV does not exceed its energy consumption limit. Through extensive simulations, we demonstrate the effectiveness of our proposed algorithm in minimizing AoI under energy consumption constraints.

The advanced Internet of Things (IoT) technique has provided a promising vision in emergency response issues with its convenience in environment cognition and communication. However, the resource-poor emergency rescue scenarios hinder the applications of IoT because of the volume constraints of portable devices. Integrated sensing and communication (ISAC) would be an ideal solution, but still faces technical challenges, such as signal integration and environment adaption. To this end, this work resorts to pilot optimization to design a dual-function ISAC signal for sensing and communication. Specifically, we derive the closed-form expressions of channel estimation and ranging performances in the OFDM system and formulate a weighted optimization to minimize the bit error ratio (BER) and average ranging error (ARE) with varying requirements of communication and ranging guaranteed. On this basis, a particle swarm optimization algorithm is developed to solve the problem, and a channel feedback framework is proposed to facilitate implementation. For emergency environment adaption, we conduct simulations with the SUI channel model to validate the efficiency of our algorithm. Simulation results demonstrate that our approach has a lower BER and ARE than typical pilot schemes under the same signal-to-noise ratio (SNR).

In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in crowded environments and are unable to identify obscured objects. To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities. Based on these datasets, we propose a multimodal collaboration network for dense and occluded vehicle detection, MuDet for short. MuDet hierarchically enhances the completeness of discriminable information within and across modalities and differentiates between simple and complex samples. MuDet includes three main modules: Unimodal Feature Hierarchical Enhancement (Uni-Enh), Multimodal Cross Learning (Mul-Lea), and Hard-easy Discriminative (He-Dis) Pattern. Uni-Enh and Mul-Lea enhance the features within each modality and facilitate the cross-integration of features from two heterogeneous modalities. He-Dis effectively separates densely occluded vehicle targets with significant intra-class differences and minimal inter-class differences by defining and thresholding confidence values, thereby suppressing the complex background. Experimental results on two re-labeled multimodal benchmark datasets, the 4K-SAI-LCS dataset, and the ISPRS Potsdam dataset, demonstrate the robustness and generalization of the MuDet.

In emergency scenarios, strong mobility and serious interference cause unstable transmission of on-site information such as close-up photos and high resolution videos, which requires a robust temporary communication network. In this paper, we focus on a UAV-assisted wireless cooperative communication and coded caching network, where emergency command vehicles and a UAV serve as content providers (CPs) to cache and transmit coded fragments or complete files for rescuers regarded as content requesters (CRs). The delivery success probability and content hit ratio are theoretically derived by incorporating the physical connectivity and social relationship between CPs and CRs. Aiming at maximizing the overall content hit ratio, we propose a multiagent two-timescale deep reinforcement learning (MA2T-DRL) algorithm to jointly optimize the transmission power and caching strategies for CPs. Specifically, we develop a two tier deep-Q networks (DQNs) framework integrating a slow-timescale DQN (ST-DQN) and a fast-timescale DQN (FT-DQN) for caching decision-making and power decision-making respectively, and then the QMIX framework is leveraged to aggregate all the outputs from local ST-DQNs. Considering the cooperative characteristics of coded caching, we further propose a novel clustering method for CPs such that CPs in the same cluster have the same willingness to serve CRs, and each cluster is regarded as the agent for training which further reduces the aggregation scale of the mixing network. Simulation results show that the proposed MA2T-DRL algorithm is efficient in model training, and presents the advantages in performance and complexity compared with the single-agent centralized training and the multiagent independent distributed training.

The advancements in information and communication technology have led to the emergence of innovative edge computing models that incorporate the computing power of vehicles into the energy sector. Electric vehicles (EVs), functioning as edge computing nodes, offer flexible computing offloading services for charging stations (CS). However, coordinating EV computing and charging should consider the interdependence with CS's specific computing requirements due to information asymmetry. Additionally, it is crucial to consider EV's charging demands and their social distance to computing tasks. In this context, it is natural to view EVs and CSs as self-interested prosumers who prioritize their individual utilities. To address the integration of strategic EV-CS interactions and uncertainties into the joint computing scheduling and energy trading, this paper proposes a parameterized deep Q-network-based reinforcement contract design framework, which employs a hybrid action space to design contracts that facilitate CSs in pairing computing tasks and charging resources with EVs. The objective is to incentivize EV participation and maximize long-term social welfare by incorporating incentive compatibility, individual rationality constraints, and capacity constraints into the contract design. Experimental results demonstrate that the proposed framework surpasses parameterized deep deterministic policy gradient-based and greedy-based contract designs, and achieves near-optimal solutions by solving deterministic optimizations.

The distributed inference paradigm enables the computation workload to be distributed across multiple devices, facilitating the implementation of deep learning based intelligent services on extremely resource-constrained Internet of Things (IoT) scenarios. Yet it raises great challenges to perform complicated inference tasks relying on a cluster of IoT devices that are heterogeneous in their computing/communication capacity and prone to crash or timeout failures. In this paper, we present RoCoIn, a robust cooperative inference mechanism for locally distributed execution of deep neural network-based inference tasks over heterogeneous edge devices. It creates a set of independent and compact student models that are learned from a large model using knowledge distillation for distributed deployment. In particular, the devices are strategically grouped to redundantly deploy and execute the same student model such that the inference process is resilient to any local failures, while a joint knowledge partition and student model assignment scheme are designed to minimize the response latency of the distributed inference system in the presence of devices with diverse capacities. Extensive simulations are conducted to corroborate the superior performance of our RoCoIn for distributed inference compared to several baselines, and the results demonstrate its efficacy in timely inference and failure resilience.

Channel State Information (CSI) feedback, powered by Deep Learning (DL) methodologies, exhibits significant promise in enhancing spectrum efficiency within massive MIMO systems. However, DL-based approaches typically necessitate substantial CSI datasets for each specific scenario, and managing multiple learned models demands considerable storage and updating bandwidth. To overcome this costly barrier, we develop a solution for efficient training and deployment enhancement of DL-based CSI feedback, which involves a lightweight translation model to cope with new CSI environments and introduces a novel dataset augmentation based on domain knowledge. Specifically, we first develop a deep unfolding CSI feedback network, SPTM2-ISTANet+, which incorporates spherical normalization to mitigate the challenge of path loss variation. Additionally, SPTM2-ISTANet+ integrates a trainable measurement matrix and residual CSI recovery blocks to enhance efficiency and accuracy. Employing SPTM2-ISTANet+ as a foundational feedback model, we introduce an adaptive CSI feedback architecture termed CSI-TransNet. CSI-TransNet features a scenario-adaptive plug-in module for CSI translation, composed of a sparsity aligning function and a compact DL module, facilitating the reuse of pretrained models in unencountered environments. To accommodate the small datasets, we propose a lightweight and general augmentation strategy based on domain knowledge. Test results demonstrate the efficacy and efficiency of the proposed solution for accurate CSI feedback given limited measurements for unseen CSI environments.

An effective communication is crucial yet challenging due to the damage sustained by ground communication infrastructure. A promising solution for enhancing emergency communication involves leveraging long-endurance fixed-wing UAVs to act as communication relays for ground rescuers. However, the disastrous environment and dynamic characteristics of UAVs may cause intermittent connectivity states, hindering efficient data transmissions. This study addresses data transmission issues in the fixed-wing UAV networks by proposing a routing scheme that leverages pre-defined UAV trajectories and tackles the dynamic topology using a graph-based time expansion method. Specifically, we propose a graph-based modeling approach to unfold network topology over time incorporating the packet transmission constraints. Then, the time-averaged delay minimization routing issue is transformed into a static linear programming problem, which is solved at each time step to minimize the packet delay and improve the network reliability. The experiment results reveal that the proposed approach outperforms the existing methods in terms of average packet delay and packet delivery ratio.

Given the energy storage capacity and rapid response capabilities, electric vehicles (EVs) hold the potential to offer auxiliary services, including peak shaving and frequency regulation, to the power grid during emergencies. Nevertheless, effectively coordinating EVs presents a complex challenge for multiple charging stations (CSs), which must integrate dynamic traffic fluctuations into a schedule while ensuring sufficient power resources for these auxiliary services. To tackle these challenges, this paper proposes an across-realm decision-making framework to dispatch EVs to CSs for peak shaving considering dynamic traffic conditions and power constraints. We develop a cooperative multi-agent reinforcement learning (MARL) strategy, which capitalizes on the collaboration among CSs by formulating the EV dispatching problem as a Markov Game. The CSs are regarded as learning agents and a QMIX network with bidding mechanism is applied to train the joint action towards maximizing long-term team rewards. The proposed method has been tested and compared to the existing reinforcement learning and non-reinforcement learning methods, and the simulation results have demonstrated the efficiency of the proposed approach considering real-world scenarios.

The rapid growth of cloud computing has led to high energy consumption in data centers (DCs), significantly burdening the safe operation of the power grid. As DC loads are seen as emergency demand response (EDR) resources, they can be aggregated into virtual power plants (VPPs) for the energy market trading, enhancing the renewable energy consumption capacity and providing grid benefits. However, VPPs might opt to maintain the profits they gain from EDR as confidential, driven by self-interest. Additionally, the incompatibility between the EDR transactions and task scheduling can result in revenue loss for DCs. To address these challenges, we propose a joint energy trading and computation scheduling (JETCS) framework for geo-distributed DCs participating in the EDR of VPPs. We first formulate DCs' energy and revenue computation as a maxi-mization problem to balance task delay and energy consumption. Then, we employ contract theory to facilitate the energy trading between DCs and VPPs against the information asymmetry. Due to nonlinearity and the infeasibility of obtaining an analytical result, we propose the proximal Jacobian alternating direction method of multipliers (PJADMM) algorithm to find an optimal solution with low complexity. The simulation results demonstrate that our proposed framework successfully encourages VPPs to reveal their actual profits and enhances the benefits for both parties.

The advancement of mobile edge computing (MEC) is driving the utilization of mobile devices (MDs) for real-time video detection. Nonetheless, during emergency scenarios, video detection tasks encounter two primary challenges: 1) time-varying channels over time between the MDs and the unmanned aerial vehicle (UAV); 2) the need for precision while managing energy consumption on the MDs. In this paper, we collaboratively address the optimization of computation offloading, data compression, and resource allocation challenges in the context of UAV-mounted MEC networks, where the UAV offers computational support for MDs. To improve accuracy and reduce energy consumption in time-varying environments, we propose an energy-efficient Deep Deterministic Policy Gradient based computation offloading algorithm (DCOA). By pursuing the long-term goal, DCOA learns to adapt to continuously changing conditions and thus improve accuracy and energy efficiency. Additionally, we introduce a convex optimization algorithm using the Lagrange multiplier method to solve resource allocation issues for offloading tasks, further reducing energy usage. Experimental results show that DCOA achieves high accuracy with low energy consumption compared to existing algorithms.

It is essential to locate the victims timely for efficient rescue after the disaster occurs in the global positioning system (GPS) denied forest area due to the influence of the tree shading. Existing works have studied optimizing the trajectory of the unmanned aerial vehicle (UAV) to exploit the wireless signal from the ground users for localization. However, current works mainly focus on optimizing the localization accuracy while paying limited attention to the localization task completion time, which makes it challenging to meet the timeliness requirements of emergency rescue missions. To provide accurate localization services for the ground victims quickly, we propose a multi-agent deep reinforcement learning (MA-DRL)-based UAV trajectory planning algorithm, which can provide high-efficiency cooperation between the multiple UAVs by exploiting the prior information and measurements from the partners. Specifically, a low-resolution trajectory planning algorithm is proposed to reduce redundant flight distances in the pre-fly stage to localize the victim’s quantity and dispersion. Furthermore, to provide high localization accuracy and energy-efficiency victims location services quickly, we exploit the coarse user information from the pre-fly stage, integrate an adaptive forest channel model and UAV energy consumption model, and propose an MA-DRL-based UAV trajectory planning algorithm which can perform decentralized execution for the high-efficiency cooperation localization. Simulation results show that our method can finish localization missions faster with less energy consumption while guaranteeing the localization accuracy compared to other benchmark algorithms.

In this paper, we propose a UAV network adaptation scheme driven by joint communication and positioning service provisioning in an emergency scenario, where massive rescuers’ concurrent and time-varying service demands are guaranteed with a scarce spectrum. Particularly, we establish a utility function that integrates communication rate and positioning error by jointly considering the single coverage constraint for data communication and the triple coverage constraint for positioning. Based on it, we propose a genetic algorithm based multi-agent deep deterministic policy gradient (GA-MADDPG) approach that adapts the UAV deployment, role switching, and user association strategies in a hierarchical manner to accommodate the rescuers’ demands for communication and positioning services in real-time. Specifically, the MADDPG module is applied for communication and positioning UAV network deployment based on the rescuers’ spatial distribution and their service demands, while the reward-based fitness is calculated and fed in the GA module periodically to optimize the UAV roles. Extensive simulation results show that our approach improves communication-positioning utility by up to 38% among comparison schemes.

The advances of communication technology and edge intelligence are deriving new computation offloading modes in the energy Internet by integrating computing capabilities of cloud servers, edge gateways (EGs), and terminal nodes into forecasting the renewable energy generation. However, the largely dispersed data generated by abundant photovoltaic (PV) stations and limited transmission capacity will degrade the collaboration of clouds, edges, and end nodes and, as a result, fail to satisfy the delay requirements of tasks. Owing to the similarity of power data generated by PV stations with akin geographical positions and weather conditions, we can reuse and offload the selected and representative power data so as to reduce transmission costs and overloads. In this article, we propose a similarity-aware stable matching approach (SASMA) to efficiently offload prediction tasks to EGs or cloud platforms with reusing the computing results. Specifically, we analyze task similarity and build the reuse strategy for the power data, and propose a similarity graph algorithm (SGA) to select representative PV stations and derive reuse relations. We also propose a similarity-based Gale–Shapley algorithm to match reused PV stations, computing nodes with prediction models. The objective is to maximize the prediction accuracy with a stable match. Simulation results show the effectiveness of the proposed approach while examining the tradeoff between the prediction accuracy and the system delay.

Electric power grid intelligence and automation are inseparable from the support of computing resources. Electric vehicles (EVs) can provide low-cost and flexible computing offloading services for nearby grid nodes. In this paper, we propose a collaborative model between EVs and intelligent charging stations (CSs) for energy and computation trading, where EVs can contribute their computation resources to CSs during charging at CSs. However, due to selfishness, CSs may refuse to reveal their computing requirements to the EVs, leading to information asymmetry. To cope with this limitation, the contract theory is employed to incentivize interaction between potential CS-EV pair for resource trading. Furthermore, we propose a stable-matching-based algorithm to match CSs and EVs into cooperative groups to achieve mutual-beneficial utilities. Simulation results verify the effectiveness of our algorithm.

High-accuracy position information is essential for the emerging applications of Internet of Things, where relative localization is often more pertinent in many cooperative tasks. In this paper, we propose a distributed relative localization scheme for large-scale 3D networks where the relative position relationships of the entire network as well as the subnetwork are concerned. In particular, we first design the prioritized geometry merging procedure with merging confidence evaluation of the geometry pairs for the entire network localization. Then we specifically extend this priority-based methodology for the algorithm design of subnetwork-aimed localization. Numerical results demonstrate that the proposed schemes significantly outperform existing algorithms.

In this paper, we propose a massive access control scheme in machine-type communications (MTC) aided smart grid, which prioritizes and clusters the devices based on the latency requirement and the distance between devices. The considered use case features a single cell in the massive connectivity smart grid and a large number of devices with different priority types. For the delay-sensitive devices, a dynamic random access channel (RACH) resource allocation scheme is proposed, where a back-off mechanism is used to defer the access requests of delay-tolerance devices. In addition, we propose a cluster-based congestion control algorithm, which clusters the devices to establish local collaboration. Simulation results show the proposed scheme reduces the average blocking probability, while significantly reducing access delay for delay-sensitive devices compared with state-of-the-art access control methods.

When signals propagate through forest areas, they will be affected by environmental factors such as vegetation. Different types of environments have different influences on signal attenuation. This paper analyzes the existing classical propagation path loss models and the model with excess loss caused by forest areas and then proposes a new short-range wireless channel propagation model, which can be applied to different types of forest environments. We conducted continuous-wave measurements at a center frequency of 605 MHz on predetermined routes in distinct types of forest areas and recorded the reference signal received power. Then, we use various path loss models to fit the measured data based on different vegetation types and distributions. Simulation results show that the proposed model has substantially smaller fitting errors with reasonable computational complexity, as compared with representative traditional counterparts.

The flexible and controllable mobility makes Un-manned Aerial Vehicle (UAV) useful in collecting data from distributed wireless sensor nodes, especially in emergency situations where infrastructures are destroyed or absent. However, due to the limited battery capacity, the mission duration of UAV is severely restricted, which makes the trajectory design become challenging. In this paper, a data-collection oriented multiple UAVs path planing algorithm is proposed to minimize the data collection time. Specifically, an enhanced particle swarm optimization (E-PSO) algorithm is first proposed to optimize the visiting sequence of sensors, which can shorten the flight time. Moreover, a novel hover-on-edge algorithm is designed by jointly considering wireless communication ranges and locations of sensors in traversal sequence. The proposed algorithms are compatible with UAV flying directly above the sensor (E-PSO) and UAV hovering on the communication range of sensor (HE-PSO) which enhance the robustness of the system. Simulation results demonstrate path distance and flight time reduction of our proposed algorithm.

Virtual Power Plant (VPP) has become an important means of low-carbon development and the forecasting of user Adjusted Power Consumption (APC) is the key part of VPP. The main challenge of APC forecasting is how to extract more APC-related features under the limitation of feature dimension for lower communication latency. In this paper, we propose a Feature-based APC forecasting (F-APC) framework for reducing forecasting error and communication latency. In the F-APC framework, firstly, we propose the method to extract APC-related features using the Classification-based Auto-Encoder (CAE) neural network. Secondly, we formulate the trade-off problem between forecasting error and communication latency and the optimal dimension of feature vector is solved by the closed-form solution. Experimental results on real data show that, compared with the benchmark, the forecasting error and communication latency of our scheme with the optimal setting is reduced by 45.26% and 33.33%, respectively.

To support multifarious vehicular applications, content sharing among vehicles or between vehicles and infrastructures can enable efficient service provisioning. In this work, we investigate joint communication, caching, and computing (3C) resource allocation to support efficient content sharing between content providers (CPs) and content requesters (CRs) in vehicular networks. To tackle the high complexity of joint 3C resource allocation, we decouple the problem into a long-term content caching strategy to allocate caching resources plus a method for short-term CP-CR pairing and corresponding communication-computing resource allocation. Specifically, a popularity and social similarity (P-SS) based caching strategy is proposed by incorporating both physical and social information. As selfish CRs may refuse to reveal their quality of service (QoS) requirements to the CPs and lead to the information asymmetry, we adopt contract theory to allocate communication and computing resources for each potential CR-CP pair. We then propose a stable-matching based algorithm to match CPs and CRs for efficient content sharing. Simulation results verify that the proposed scheme can effectively solve the problem with low complexity.

The UAV deployment as well as power allocation is critical in emergency rescue with the practical diverse re-quirements of data applications. In this paper, a UAV 3-D deployment scheme driven by multi-level quality of service (QoS) is first presented. Specifically, to harvest more performance gain, a fuzzy clustering-based initialization method is proposed, which can achieve more reliable accuracy of clustering compared with traditional clustering methods. Further, considering more comprehensive effect in terms of particle diversity and iteration, a novel inertia weight update method is developed to accelerate convergence. Simulation results demonstrate that our proposed scheme outperforms other schemes.

To support multifarious vehicular services with differentiated quality-of-service (QoS) requirements, space-air-ground integrated vehicular networks (SAGVNs) are envisioned as a promising solution to provide global network connectivity, enhance network flexibility, and improve network reliability. In this paper, we investigate cooperative content delivery in the SAGVN, where vehicular content requests can be simultaneously served by multiple access points (APs) in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular QoS requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay comparing to the benchmark schemes.

As an emerging distributed learning paradigm, Federated Learning (FL) allows smart meters to collaboratively train a load forecasting model while keeping their private data on local devices. However, two critical issues hinder the deployment of ordinary FL algorithm in load forecasting: (i) one global model cannot fit all users well due to their heterogeneous load patterns; (ii) the training speed of FL severely depends on a few stragglers with scarce communication and computing resources. In this work, we propose a novel multi-center FL framework for load forecasting to learn multiple models simultaneously by grouping the users according to their model dissimilarity and training time. Specifically, a problem is formulated to jointly optimize the grouping strategy and forecasting model parameters, which is resolved by integrating the matching algorithm into the update process of model parameters in FL. Simulation results on real load data show that, compared with the existing load forecasting methods based on FL, the prediction error of our scheme is reduced by 8.11%, and the training time is reduced by 90.37%.

In the aftermath of disasters, where the communication infrastructure is often impaired or completely unavailable, unmanned aerial vehicle (UAV) assisted Mobile Ad Hoc network(MANET) is a promising choice to recover wireless communication. However, affected by the dynamic topology and time-varying channel quality caused by the nodes' mobility in emergency scenarios, it is difficult to guarantee the quality of service (QoS) of various types of packets in terms of transmission delay. In this paper, a dynamic priority packet scheduling scheme is proposed to maintain high QoS in post-disaster UAV assisted MANET, where not only the occurred packet delay has experienced but also the impact that will occur in the future transmission is taken into consideration on the priority assignment. Specifically, to characterize the dynamic features of nodes, the Gauss-Markov Mobility Model is exploited. Then to incorporate the impacts of the node's movement, as well as the instability of the topology and the time-varying channel quality into the packet's priority assignment, we estimate the packet's transmission delay where the device-to-device(D2D), device-to- UAV(D2U), and UAV-to-UAV(U2U) channels are all specified, and theoretically analyzed the probability of link duration in the future transmission. Simulation results show that the proposed dynamic priority scheme outperforms the static priority and (first in first out) FIFO scheme in terms of the overall packets transmission success ratio and transmission delay.

Nowadays, there is an ever-increasing interests in federated learning, which allows end devices to collaboratively train a global machine learning model in a decentralized paradigm without sharing individual data. Despite the advantages of low communication cost and preserving data privacy, federated learning is also facing with new challenges to address. Practically, end devices will consider the resources cost and willingness caused by machine learning model training when they are invited to participate a federated learning task. So, how to assign the preferable tasks to the devices with high willingness has to be considered. Besides, the end devices have the property of high mobility, which means the time of devices localizing within the network is limited. Therefore, to reduce the task execution time is necessary. To address these problems, we first analyze and formulate the latency minimization problem for multitask federated learning in a multiaccess edge computing (MEC) network scenario. Then, we model the corresponding problem as a matching game to find the optimal task assignment solutions. Moreover, considering the large-scale Internet-of-Things (IoT) scenario, it is almost impossible for two sides to know the details of every individual of the other side so that the complete preference list (CPL) cannot be built in reality. Therefore, we propose an algorithm for large-scale matching with the incomplete preference list to address the problem. Finally, we conduct the numerical simulation in various cases to demonstrate the effectiveness of our proposed method. The results show that our approach can achieve similar performance with the CPL case.

Sharding is a promising solution to achieving scalability within the blockchain network. A sharded blockchain network consists of a beacon chain and several committees powered by the participants (i.e., validators) through the Proof-of-Stake (PoS) consensus protocol. Efficient and scalable as it can be, the sharded blockchain based on PoS is vulnerable to discouragement attack. A discouragement attack occurs when malicious validators censor messages to discourage validators from participating in the network. Furthermore, no rate-limiting validator rotation (enter/exit quickly) makes it more challenging to detect such an attack. In this paper, considering the undetermined rotation and the discouragement attack, we render the beacon chain an intermediary, allowing the beacon chain to interact with validators and the cyber-insurer, aiming to encourage the validators' stable rotation through insurance compensation. Specifically, we utilize a two-stage hierarchical game-based model to formulate the complicated interactions under the cyber insurance framework. In the first stage, the beacon chain develops compensatory strategies according to the insurer's profile. In the second stage, the beacon chain designs a series of contracts for validators, including insurance items, compensatory strategies, and rotation requirements. Consequently, the proposed scheme incentivizes validators to remain online by transferring risk to the cyber insurer and enables the sharded blockchain network to weaken the attack's impact through validators' stable rotation. This paper presents closed-form solutions for the proposed model, in which the beacon chain and the cyber insurer can gain maximized profits. The simulations demonstrate the feasibility and superiority of the proposed model.

Having stepped into the era of information explosion, storing, processing and analyzing the vast data sometimes are quite intractable problems. However, it is impossible for personal computer or devices to tackle with such heavy workloads. Then, companies that provides cloud computational services come into business. From the perspective of companies, the cost for providing fog computing services is much higher than the traditional computing services. Consequently, the price for real-time requests is more expensive than the reserved services. Aiming at minimizing the expenditures, the most important part is how many cloud services the customers should reserve in advance because different amounts they consume will yield different expenses and both of insufficient and excess consumption result in wastes. The emerging machine learning method provides a powerful tool to address such a prediction problem. In this paper, we propose a hierarchical Pythagorean fuzzy deep neural network (HPFDNN) to forecast the quantity of requisite cloud services. On account of obtaining the better interpretations of original data, beyond the employment of fuzzy logic, the neural representation is also utilized as a complementary method. The information or the knowledge acquired from fuzzy and neural perspectives are coalesced as the final transformed data to be put into the learning systems, so that the useful information concealed in the enormous contents can be effectively described. On the basis of the anticipation of the deep neural network, the consumers are able to decide the amount of cloud services to purchase. Numerical results based on the real data set from Carnegie Mellon University demonstrate that the proposed model yields the economical predictions and outperforms the prediction by the traditional deep neural network.

Edge caching can effectively reduce backhaul burden at core network and increase quality-of-service at wireless edge nodes. However, the beneficial role of edge caching cannot be fully realized when the offloading link is in deep fade. Fortunately, the impairments induced by wireless propagation environments could be renovated by a reconfigurable intelligent surface (RIS). In this paper, a new RIS-aided edge caching system is proposed, where a network cost minimization problem is formulated to optimize content placement at cache units, active beamforming at base station and passive phase shifting at RIS. After decoupling the content placement subproblem with the hybrid beamforming design, we propose an alternating optimization algorithm to tackle the active beamforming and passive phase shifting. For active beamforming, we transfer the problem into a semidefinite programming (SDP) and prove that the optimal solution of SDP is always rank-one. For passive phase shifting, we introduce the block coordinate descent method to alternately optimize the auxiliary variables and the RIS phase shifts. Further, a conjugate gradient algorithm based on manifold optimization is proposed to deal with the non-convex unit-modulus constraints. Numerical results show that our RIS-aided edge caching design can effectively decrease the network cost by improving the quality of offloading links.

Coded caching can improve the robustness of wireless caching networks. This paper investigates the joint caching and communication optimization in terminal based wireless coded caching networks. Social characteristics of private terminals are considered to further harvest more performance gain in terms of hit ratio. To tackle the problem of unknown popularity distribution, we adopt deep reinforcement learning. Since the joint caching and communication optimization of all Content Providers (CP) results in larger-scale action and state space, we propose a novel cluster based deep reinforcement learning (CB-DRL) scheme. We present simulation results to demonstrate complexity reduction and effectiveness of the proposed algorithm.

The advance of edge computing pushes computing functionalities to the network edge and brings lucrative opportunities for edge operators (EOs) to cater the users with low latency requirement. Unlike in cloud computing, edge servers have limited computing capacity and require a proper resource planning. To avoid loss of potential profit, a promising way is to outsource cloud resources from a public cloud with additional cost when the edge computing capacity is insufficient to meet the real-time demands. Besides, the uncertainty of future demands also affects EOs' profits. It's essential to consider the interaction among market participants with different risk attitudes. To this end, we study multiple risk-averse EOs with one risk-neutral Cloud Provider (CP) in an edge computing market, where each EO competes to serve the users by determining the optimal resource provision strategies given the demand and the outsource price charged by the CP, and the CP sets the price based on the best responses of the EOs. We model the interaction between EOs and CP as a two stage Stackelberg game, and employ a data-driven optimization approach to characterize the uncertainty. We explore the existence and uniqueness of subgame Nash equilibrium, and find the equilibrium based on the Sample Average Approximation (SAA) method. Extensive simulations using real-world cluster data traces verify the effectiveness of the proposed method.

Deploying unmanned aerial vehicles (UAVs) as flying base stations (BSs) is a promising solution to alleviate the burden of communication infrastructure during the peak-traffic hours. However, when a UAV is deployed as a flying BS to serve the vehicle users in the hotspot, and the vehicle-to-vehicle (V2V) communication is introduced to further improve the network capacity, the performance analysis and optimization problems have not gained well investigated. In this paper, aforementioned problems are carefully studied from a statistical point of view, where the system performance is captured by the users' successful service probability. Specifically, we first derive the successful service probability for the UAV-to-vehicle (U2V) transmission. Meanwhile, taking those important factors, i.e., vehicle mobility and social proximity, into account, we estimate the successful service probability for the V2V transmission. The average successful service probability for the considered scenario is then derived. Based on the mathematical analysis results, we further improve the system performance by adjusting the UAV's altitude position, where the UAV deployment problem is formulated as a service probability maximization problem. To find the optimal solution, a particle swarm optimization algorithm is proposed. Finally, numerical simulations are conducted to verify the theoretical analysis and the efficiency of our proposed scheme.

As the basis of the most existing blockchain networks, Proof of Work (PoW) consensus protocol highly relies on the computational resources, and thus causing a huge waste of energy. Proof of Stake (PoS) is the alternative to relieve the PoW dilemma. However, it is also under threat, i.e., discouragement attack, which is a way to bring down the blockchain networks without any effective defense against it. To prevent the discouragement attack, the founders of Ethereum argue that the system should set a withdraw delay instead of allowing the validators entry/exit quickly. But how to determine the delay is still an open question. In this paper, we adopt the cyber insurance idea and propose the insurance contract to help determine the withdraw delay, as well as the insurance claim to relieve the loss of victims. Specifically, instead of requiring the insurance premium from the validators, the cyber insurer first signs the contract with the blockchain representative (e.g., beacon chain). Then the blockchain representative would sign a series of contracts with the validators. By such design, the validators can obtain the insurance claim without paying the premium, while the blockchain networks can keep the validators staying online to resist the discouragement attack. Finally, through the simulations, we demonstrate that the proposed model is capable of providing adaptive insurance contracts for the different validators and keeping the profits of the blockchain network and the cyber insurer.

Multi-access edge computing (MEC) is viewed as a promising technology to improve the real time video service in vehicular networks. However, in the traditional vehicular networks, the road side units (RSUs) are usually only equipped with communication modules, and the unmanned aerial vehicles(UAVs) are seldom used. In this paper, a new UAV-aided time-varying vehicular network is introduced for vehicle users (VUEs) to obtain better experience, where the RSUs and the UAV are equipped with MEC servers for the real time video transcoding. Considering that the video service always lasts for a period of time, we investigate the user association policy from a long-term perspective. Specifically, to characterize the time-varying features of communication links and the heterogeneity of available resources, we theoretically derive the achievable video chunks and link reliability based on the vehicle mobility model and content caching model. Then, the user association problem is formulated as the utility optimization problem, where both the VUE’s quality of experience (QoE) and handover cost are taken into consideration. Furthermore, we propose an improved Dijkstra algorithm to solve the original NP-hard problem after it is transformed to a shortest path selection problem. Finally, by numerical results, we verify that the proposed scheme outperforms existing schemes in terms of the VUE’s QoE and the handover numbers.

Cooperative multiaccess edge computing (MEC) is a promising paradigm for the next-generation mobile networks. However, when the number of users explodes, the computational complexity of the existing optimization or learning-based task placement approaches in the cooperative MEC can increase significantly, which leads to intolerable MEC decision-making delay. In this article, we propose a mean field game (MFG) guided deep reinforcement learning (DRL) approach for the task placement in the cooperative MEC, which can help servers make timely task placement decisions, and significantly reduce average service delay. Instead of applying MFG or DRL separately, we jointly leverage MFG and DRL for task placement, and let the equilibrium of MFG guide the learning directions of DRL. We also ensure that the MFG and DRL approaches are consistent with the same goal. Specifically, we novelly define a mean field guided Q -value (MFG-Q), which is an estimation of the Q -value with the Nash equilibrium gained by MFG. We evaluate the proposed method's performance using real-world user distribution. Through extensive simulations, we show that the proposed scheme is effective in making timely decisions and reducing the average service delay. Besides, the convergence rates of our proposed method outperform the pure DR-based approaches.

In the emerging Internet-of-Things (IoT) paradigm, the lifetime of energy-constrained devices (ECDs) cannot be ensured due to the limited battery capacity. In this article, unmanned aerial vehicles (UAVs) are served as carriers of wireless power chargers (WPCs) to charge the ECDs. Aiming at maximizing the total amount of charging energy under the constraints of the UAVs and WPCs, a multiple-period charging process problem is formulated. To address this problem, bipartite matching with one-sided preferences is introduced to model the charging relationship between the ECDs and UAVs. Nevertheless, the traditional one-shot static matching is not suitable for this dynamic scenario, and thus the problem is further solved by the novel multiple-stage dynamic matching. Besides, the wireless charging process is history dependent since the current matching result will influence the future initial charging status, and consequently, the Markov decision process (MDP) and Bellman equation are leveraged. Then, by combining the MDP and random serial dictatorship (RSD) matching algorithm together, a four-step algorithm is proposed. In our proposed algorithm, the local MDPs for the ECDs are set up first. Next, using the RSD algorithm, all possible actions can be presented according to the current state. Then, the joint MDP is built based on the local MDPs and all the possible matching results. Finally, the Bellman equation is utilized to select the optimal branch. Finally, simulation results demonstrate the effectiveness of our proposed algorithm.

In this article, we investigate the UAV-aided edge caching to assist terrestrial vehicular networks in delivering high-bandwidth content files. Aiming at maximizing the overall network throughput, we formulate a joint caching and trajectory optimization (JCTO) problem to make decisions on content placement, content delivery, and UAV trajectory simultaneously. As the decisions interact with each other and the UAV energy is limited, the formulated JCTO problem is intractable directly and timely. To this end, we propose a deep supervised learning scheme to enable intelligent edge for real-time decision-making in the highly dynamic vehicular networks. In specific, we first propose a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content placement strategy, we devise a time-based graph decomposition method to jointly optimize the content delivery and trajectory design, with which we then leverage the particle swarm optimization (PSO) algorithm to further optimize the content placement. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make fast decisions online. The network density and content request distribution with spatio-temporal dimensions are labeled as channeled images and input to the CNN-based model, and the results achieved by the CBTL algorithm are labeled as model outputs. With the CNN-based model, a function which maps the input network information to the output decision can be intelligently learnt to make timely inference and facilitate online decisions. We conduct extensive trace-driven experiments, and our results demonstrate both the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model.

A probabilistically robust transmit beamforming problem is referred, when the wireless downlink (DL) communication is supported by a robust non-orthogonal transmission (NOT)-aided design. Realistic imperfect channel state information (CSI) is considered in the face of rapidly fluctuating vehicular wireless channels, when the road side unit (RSU) communicates with multiple vehicles. Our design objective is to keep the probability of each vehicle's signal-to-interference-plus-noise ratio (SINR) outage below a given threshold. Minimizing the outage probability presents a significant analytical and computational challenge, since it does not lend itself to tractable closed-form expressions. Assuming a Gaussian CSI uncertainty distribution, we provide an approximation method by resorting to the semidefinite relaxation (SDR) and then apply a convex restriction to the original SINR outage constraints. Furthermore, the infinite constraints are reformulated into linear matrix inequalities (LMIs) by exploiting the popular S-procedure. As a benefit, the reformulated program can be solved efficiently using off-the-shelf solvers. Computer simulations are performed for benchmarking our convex method both against the non-robust non-orthogonal as well as the classical orthogonal designs. The results show that our robust beamforming design offers excellent high-mobility performance.

Computation offloading manages resource-intensive and mobile collaborative applications (MCA) on mobile devices where much processing is replicated with multiple users in the same environment. In this article, we propose a novel hybrid multicast-based task execution framework for multi-access edge computing (MEC), where a crowd of mobile devices at the network edge leverage network-assisted device-to-device (D2D) collaboration for wireless distributed computing (MDC) and outcome sharing. The framework is socially aware in order to build effective D2D links. A key objective of this framework is to achieve an energy-efficient task assignment policy for mobile users. Specifically, we first introduce the socially aware hybrid computation offloading (SAHCO) system model, which combines of MEC offloading and D2D offloading in detail. Then, we formulate the energy-efficient task assignment problem by taking into account the necessary constraints. We next propose a Monte Carlo Tree Search based algorithm, named, TA-MCTS for the task assignment problem. Simulation results show that compared to four alternative benchmark solutions in literature, our proposal can reduce energy consumption up to 45.37 percent.

Edge caching has become a promising technology in future wireless networks owing to its remarkable ability to reduce peak data traffic. However, the storage resource can be limited in practice hence only a small amount of files can be cached. How to improve the cache hit ratio in finite-buffer caching based on the prediction of user demands has become an important problem. In this paper, we study caching policies with finite buffer by exploiting the prediction of a user's request time, referred to as request delay information (RDI). Based on RDI, we maximize the average cache hit ratio through a Markov decision process (MDP) approach. Specifically, we formulate an MDP problem and apply a modified value iteration algorithm to find an optimal caching policy. Moreover, we provide an upper bound and a lower bound for the cache hit ratio, as well as an analytical cache hit ratio with small buffers. To address the issue that the state space can be prohibitively large in practice, we present a low-complexity heuristic caching policy that is shown to be asymptotically optimal. Simulation results show that introducing RDI may bring significant cache hit ratio gain when the buffer size is limited.

To enable ever-increasing vehicular applications, heterogeneous vehicular networks (HetVNets) are recently emerged to provide enhanced and cost-effective wireless network access. Meanwhile, edge caching is imperative to future vehicular content delivery to reduce the delivery delay and alleviate the unprecedented backhaul pressure. This work investigates content caching in HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations are considered to cache contents and provide content delivery. Particularly, to characterize the intermittent network connection provided by Wi-Fi RSUs and TVWS stations, we establish an on-off model with service interruptions to describe the content delivery process. Content coding then is leveraged to resist the impact of unstable network connections with optimized coding parameters. By jointly considering file characteristics and network conditions, we minimize the average delivery delay by optimizing the content placement, which is formulated as an integer linear programming (ILP) problem. Adopting the idea of student admission model, the ILP problem is then transformed into a many-to-one matching problem and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity.

In this paper, we investigate the issue of resource allocation for secure energy efficient communication in a multiuser orthogonal frequency division multiplexing (OFDM) based full-duplex (FD) relaying network in the presence of a passive eavesdropper whose channel state information (CSI) is not perfectly known. Our goal is to maximize the overall secure energy efficiency (SEE), which presents the relationship between energy consumption and secrecy performance. In the context of multiuser communications, such a resource allocation strategy jointly combines subcarrier permutation, subcarrier pair allocation, as well as power allocation altogether. The considered optimization problem is formulated as a mixed integer nonconvex programming problem, which is generally NP hard. Analyzing the property of such a problem, we first use the Dinkelbach's method to eliminate the fractional form and then exploit Generalized Benders decomposition to decouple the original problem into a master problem for pure integer programming and a primal problem for nonlinear programming. More specific, given the nonconvexity of the primal problem, we accordingly transform it into an equivalent relaxed convex problem by applying dual decomposition, alternative convex search, and difference of convex function programming. The numerical results are provided to validate the theoretical analysis and to demonstrate the effectiveness of the proposed algorithm.

Content delivery in wireless caching networks via device-to-device (D2D) communications can effectively offload the traffic burden and reduce the delivery delay and the energy consumption. This paper develops a distributed content delivery scheme to leverage D2D communications for content sharing by using belief propagation (BP) approach. With the target of minimizing average energy consumption for content delivery, the optimization of content delivery is formulated as a fractional programming which is NP-hard. By jointly considering channel conditions, cache status, and asynchronous demands, a distributed energy-balancing algorithm based on BP framework is proposed to facilitate the content requesters (CRs) to select the best content helpers (CHs). Furthermore, an implementation method is designed to motivate the practical deployment of the distributed scheme in caching-enabled D2D networks.

The widespread application of unmanned aerial vehicles (UAVs) urgently requires an effective tracking algorithm as technical support. Particle filter has been widely applied in maneuvering target tracking, however, there has been no suitable solution to the trade-off between weight degeneracy and particle diversity during the process of resampling. In this paper, we propose an improved particle filter algorithm based on systematic resampling with additional random perturbation. This method ensures that particle filter maintains particle diversity and reduces weight degeneracy under environments with different noise types, simultaneously. The simulation results demonstrate that the proposed algorithm generates more accurate filtered trajectory than generic particle filter, especially under the environment with low noise.

Obtaining accurate content popularity in caching-enabled cellular networks can not only increase the caching profits in a large scale but also effectively improve quality of service (QoS). This paper investigates the content popularity based caching strategy optimization problem by maximizing the successful delivery probability under the premise of meeting the QoS. Based on the Dirichlet distribution, we developed a common interest model (CIM) by which the common interest properties of the mobile users (MUs) and the content popularity can be extracted from the content delivery history. In order to estimate the parameters of the CIM, a machine learning (ML) model is proposed by using the Gibbs sampling algorithm. Then, the content caching problem is transformed into a decision making problem which is solved by the branch and bound method. Numerical results demonstrate the effectiveness of the proposed scheme.

The emergence of diversified services in the Internet of Things (IoT) poses new challenges to the datacenter, which plays an important role in data transmission. For example, urban monitoring system no longer takes delay as the only performance measure, but also needs to consider the freshness of information. Therefore, modern datacenters need to support multiple services of IoT. However, many works for datacenter networks focus on optimizing the flow completion time (FCT). The Age of Information (AoI) which is a new and promising concept in many real-time applications has not been considered in datacenter. In this paper, we apply the concept of AoI to guarantee the information freshness and propose an age-optimized receiver-driven transport control scheme, referred to as ARTHost. In ARTHost, by utilizing the receiver-driven approach, we propose a priority flow scheduling mechanism, where we classify flows into AoI flows and normal flows, representing diverse services. Then, we design a Receiver-Driven (RD) sampling pattern for AoI flows to optimize the freshness of information. Meanwhile, the optimization of FCT for normal flows is also considered. Finally, the simulation results show that the proposed ARTHost can achieve significant performance gain for AoI flows, while the performance loss of normal flows is negligible.

To support ubiquitous connectivity and the rising demand of tele-traffic in the Internet of Things (IoT), we amalgamate nonorthogonal multiple access (NOMA) and full-duplex (FD) techniques to propose a new hybrid NOMA (FDH-NOMA) framework. We formulate an ergodic sum rate maximization problem under statistic channel state information (CSI). To simplify optimization, we derive upper and lower bounds to the ergodic sum rate and decompose the rate upper bound maximization problem into two subproblems, namely, user association and resource allocation. Since these two subproblems are coupled, we design an iterative algorithm for successively optimizing ergodic sum rate. In user association, we propose the mode selection criterion and NOMA mode pairing scheme, whereas for resource allocation, we optimize both interpair and intrapair resource allocation. Our numerical results demonstrate the strength and the effectiveness of our proposed FDH-NOMA framework and optimization algorithm.

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with n UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence, the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is θ(n/logn) times higher than that of the multihop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.

Fog radio access network (Fog-RAN), which pushes caching and computing capabilities to the network edge, is capable of efficiently delivering content to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in FogRAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum b-matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of O(M + N) is also proposed, where M is the number of users and N is the number of fog access points (Fog-APs). Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region and diversity-multiplexing region. Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching but also provides a systematic way to evaluate and optimize the performance of Fog-RANs.

This paper investigates the joint power allocation with spectrum sharing for vehicle-to-everything (V2X) communications with Long Term Evolution Unlicensed (LTE-U) technology in a heterogeneous network. The vehicle users (VUEs) are classified into safety VUEs and non-safety VUEs based on the corresponding services. With the purpose to maximize the total throughput of CUEs, safety VUEs, and non-safety VUEs in contend free period (CFP) based LTE-U mode, a matching based resource allocation scheme is proposed under the constraints of fairness coexistence. The ergodic sum rate is considered with respect to statistical channel state information (CSI), and a lower bound evaluation of the objective function is presented with reduced computation complexity. Numerical results demonstrate the analysis and performance of the proposed strategy.

In this information explosion age, processing and storing the vast data sometimes are intractable problems. To cope with the challenges, fog computing is proposed, which is good at handling the real-time tasks but the service price can be correspondingly more expensive. In order to harvest more profits and get more users, the companies that offer cloud services also carry out the reservation selling strategy, whose price is relatively cheaper. For the purpose of minimizing the cost of using cloud services, this paper proposes a fuzzy deep neural network (FDNN) based method to predict the demand of cloud resources. Besides the utilization of fuzzy logic, in the phase of network training, the back-propagation algorithm, adaptively varied learning rate and the dropout strategy are also employed. According to the network predictions, the customers are able to decide how many resources to reserve so as to minimize their expenses most. Simulation results based on the real data sets from Carnegie Mellon University show that the proposed method gives the economical predictions and outperforms the traditional deep neural network.

Full-duplex (FD) D2D communications have been recently applied in cooperative caching systems with advanced self-cancellation technologies. A FD mode relay can receive a content item from a Content Provider (CP), and forward it to a Content Requester (CR) simultaneously on the same band. However, FD mode relays and CPs are generally selfish, thus an effective incentive mechanism to encourage their mutual cooperation is important. Complicating the matter, there exist information asymmetries between the CP and relay. For example, a relay's channel state information is personal character information, which is called as hidden information. Also, the transmission power of relay is personal action information, which is called as hidden action. The asymmetric information problem may lead to deceptive behavior during cooperation. Therefore, an adverse selection and moral hazard model based incentive mechanism is proposed, which solves the hidden action and hidden information problems jointly. The capacity of this two-hop relay transmission serves as metric, and an effective power allocation method is designed based on the incentive contract.

To cope with the explosive vehicular content demands from various applications and services, it is imperative to cache the content files close to end users to reduce both the core network traffic load and the delivery delay. The heterogeneous vehicular networks provide multiple data pipes to access to the Internet for vehicles, and thus can be leveraged to cache the required content files on different access network edges and further improve the caching effectiveness. This paper focuses on the content delivery in heterogeneous vehicular networks that allow content caching in roadside WiFi APs, TV White Space Stations, and Cellular Base Stations. With the objective of minimizing the content delivery delay, a Student Admission matching- based content caching scheme is proposed by considering the file popularity, vehicle mobility and cache capacity of the edge infrastructure in heterogeneous vehicular networks, whereby a stable result is further obtained via the Gale-Shapley algorithm. Our simulation results demonstrate the advantages of the proposed caching scheme.

Coded caching systems can facilitate traffic offloading from base stations and content sharing between content providers and content requesters via device- to-device (D2D) communications. In particular, dividing content items into mul-tiple pieces not only can enable the possibility of decreasing transmission delay and energy consumption, but also can contribute to secrecy enhancement since potential social outcasts can decode the content item only if they can obtain enough pieces. In this work, the tradeoff problem between content sharing efficiency and secure transmission in coded caching systems is investigated. First, to maximize the caching hit ratio with secrecy outage constraints, a joint optimization problem considering coding parameters, content placement, and power allocation is formulated. Further, a lower bound on the caching hit ratio and a upper bound on the secrecy outage probability are derived to simplify the original optimization problem. Numerical results demonstrate the effectiveness of the proposed scheme.

The emerging mobile crowd sensing has become a new paradigm where a crowd of mobile users utilize their smart devices to conduct complex computation and sensing tasks in mobile social networks, in which the content sharing plays a significant role. In this work, we propose a novel many-to-many content sharing framework by enabling users to exchange information through separate connections with different partners of multiple relationships simultaneously. Under the statistical channel statement information condition, the many-to-many pairing problem is formulated as a Stable Multiple Activity (SMA) matching game and solved by a distributive stable b-matching algorithm in multigraphs. Based on the matching model, we further perform the power allocation scheme to improve system performance. Simulation results demonstrate the superiority of our proposed SMA method with lower complexity.

Due to the fast increasing of mobile data demands and limitation of edge devices in computing capability, it is significant to exploit fog computing to support future edge networks' demands. This paper has investigated a cooperative computing problem. By borrowing the concept of fog computing, fog nodes are allowed to collaborate and provide computing services to the requesters. Considering the hidden information of fog nodes as computing capability and channel status, the adverse selection model based on contract theory is formulated to stimulate the fog nodes to provide high quality of services and realize collaborative optimization of computing resources and communication resources, targeting on maximizing the utility of both the requesters and fog nodes. To solve the proposed multi-requesters and multi-cooperators contract design problem, variety matching algorithms can be exploited to optimize solutions with different objections. Simulation results have proved the effectiveness of the proposed scheme.

It is envisioned that the Internet of Things (IoT) will provide promising opportunities to users, manufacturers, and service providers with a wide applicability in many fields. By employing social networking and device-to-device (D2D) communications in the IoT, the resulting social IoT can potentially provide services more effectively and efficiently. This paper focuses on content sharing among smart objects (devices) in the social IoT with D2D-based cooperative coded caching. Generally, complete content items or coded fragments are allowed to be delivered via multi-hop cooperative D2D communications. First, aiming at maximizing the overall success rate of multi-hop-based content sharing, the interplay between coding parameter optimization and wireless resource allocation is investigated by considering both physical and social characteristics. Moreover, a Roth and Vande Vate-based distributed scheme is proposed to solve the dynamic matching problem between the content helpers and content requesters. Numerical results demonstrate that the proposed scheme can achieve a good tradeoff between system performance and computational complexity.

This work investigates content sharing among smart objects (devices) via device-to-device (D2D) communications in the social Internet of Things (IoT) by exploiting distributed content coding schemes. Aiming at minimizing the overall transmission cost, the resource matching between content transmitters and content receivers is optimized by applying matching theory. Particularly, the dynamic distributed Roth and Vande Vate algorithm is employed to optimize the matching problem, achieving a lower computational complexity and a higher system stability. In addition, social characteristics among mobile users are also exploited in the optimization problem to further increase system reliability and robustness. Numerical results demonstrate the effectiveness of the proposed scheme.

This paper studies the application of non-orthogonal multiple access (NOMA) to a cooperative multicast system (termed CM-NOMA). In particular, the multicast subscribers are served as secondary users underlaying a primary user. To enhance the secondary user fairness and compensate for the primary user, a two-stage cooperative strategy is proposed by selecting a certain secondary user to perform NOMA transmission distributedly. The maximum ratio combining (MRC) is further employed to harvest spatial diversity. We explicitly formulate the primary outage probability and secondary ergodic throughput in closed form. Moreover, we derive the asymptotic expressions and accordingly investigate the power allocation optimization with a primary outage constraint to maximize the secondary throughput. Numerical results validate that the proposed scheme achieves superior secondary user fairness and primary outage performance as well as higher energy efficiency.

The surging of smartphone sensing, wireless networking and social media have enabled a great variety of applications, such as environment surveillance, marketing, health monitoring and so on. This new paradigm is typically referred to as mobile crowd sensing (MCS). Existing solutions on MCS are majorly based on text, image and video analysis using the distributively sensed data. One shortcoming of such approaches is the data processing delay. Considered as an enhanced function to the temporary social media services, for example Twitter, we propose a real-time event sharing framework that provides mobile users with the freedom of expressing their various interests regarding the event data. To achieve such goals, we introduce a novel Hashtag design, which is a combination of five types of information: time, location, keywords, data type and data size. Users, by comparing their own interests with the uploaded Hashtags in the Twitter server, can search for suitable partner users to share information with in real time. The formulated user sharing/pairing problem is modeled as the Stable Fixture (SF) matching game, and can be solved by the Irving's SF (ISF) algorithm in a distributive manner. The simulations have demonstrated the superiority of our proposed ISF method by comparing with both centralized method and heuristic algorithms.

Vehicle-to-everything (V2X) communications, enabled by cellular device-to-device (D2D) links, have recently drawn much attention due to its potential to improve traffic safety, efficiency, and comfort. In this context, however, intracell interference combined with demanding latency and reliability requirements of safety vehicular users (V-UEs) are challenging issues. In this paper, we study a resource allocation problem among safety V-UEs, non-safety V-UEs, and conventional cellular UEs (C-UEs). Firstly, the resource allocation problem is formulated as a three-dimensional matching problem, where the objective is to maximize the total throughput of non-safety V-UEs on condition of satisfying the requirements on C-UEs and on safety V-UEs. Due to its NP-hardness, we then exploit hypergraph theory and propose a local search based approximation algorithm to solve it. Through simulation results, we show that the proposed algorithm outperforms the existing scheme in terms of both throughput performance and computational complexity.

This paper investigates secrecy efficiency (SE) optimization in a friendly full-duplex (FD) decode- and-forward (DF) relay network with a passive eavesdropper. Targeting on SE maximization, the power allocation is optimized subject to the total power and minimum secrecy rate constraints adapting to the assumption of unknown eavesdropper's channel state information (CSI). By exploiting the properties of fractional programming (FP) and Difference of Convex functions (DC) programming, the resulting nonconvex optimization problem can be relaxed into a more tractable equivalent problem. Simulation results demonstrate that our proposed scheme can reach a better trade-off between security performance and energy consumption.

Internet of Things (IoT) is a promising technology to connect tremendous devices together, where the major challenges are that the available energy is limited and computing capability is low. In this paper, we propose an efficient IoT computing tasks offloading mechanism based on cooperative communication and mobile cloud computing (MCC) system. The problem will be formulated as a joint optimization problem of computation and radio resource allocation aiming to minimize the system energy consumption, under the constraints of latency and transmission power. We will first propose a joint iterative computation offloading and resource allocation algorithm to solve the non-convex optimization problem. To further reduce the computation complexity, we also propose a matching based sub- optimal algorithm to solve this problem. Simulation results demonstrate that the proposed iterative algorithm achieves the goal to substantially reduce energy consumption by offloading computation. Moreover, the sub-optimal algorithm significantly reduces the computation complexity with only a small portion of performance loss.

In emerging fifth generation (5G) systems, mobile social networks are expected to play an important role to enable proximity-based content distribution among devices. In this paper, we address social-awareness aspects and device-to-device (D2D) communications for information diffusion solutions in emergency scenarios. Context-aware information is collected from a set of devices deployed in the environment and received data are integrated and elaborated at the cellular base station before being delivered. In such a framework, we model the expected information diffusion time by taking into account both networking- and sociality-related metrics. In particular, we introduce the so-called social intercontact time which is able to model the interaction frequency between the user and a generic social platform. The proposed approach is compared with alternative solutions where the dissemination process is either managed through direct links from the central base station, as a conventional multicast scheme, or with the support from proximity communications, as a D2D-enhanced multicast scheme. The results of a performance assessment study show that the proposed framework achieves considerable gains, up to 50%, in terms of overall information diffusion time and data rate per user equipment.

At the time of writing, vehicle-to-vehicle (V2V) communication is enjoying substantial research attention as a benefit of its compelling applications. However, the ever-increasing tele-traffic is expected to result in overcrowding of the available band. As a first resort, multiple input multiple output (MIMO) can be utilized to enhance the attainable bandwidth efficiency or link reliability. However, in hostile V2V wireless propagation environments, the achievable multiple-antenna gain is eroded by the channel correlation. As a promising MIMO technique, spatial modulation (SM) only activates a single transmit antenna (TA) in any symbol interval and, hence, completely avoids the inter-antenna interference, hence showing robustness against channel correlation. As a further powerful solution, non-orthogonal multiple access (NOMA) has been proposed for improving the bandwidth efficiency. Inspired by the robustness of SM against channel correlation and the benefits of NOMA, we intrinsically amalgamate them into NOMA-SM in order to deal with the deleterious effects of wireless V2V environments as well as to support improved bandwidth efficiency. Moreover, the bandwidth efficiency of NOMA-SM is further boosted with the aid of a massive TA configuration. Specifically, a spatio-temporally correlated Rician channel is considered for a V2V scenario. We investigate the bit error ratio performance of NOMA-SM via Monte Carlo simulations, where the impact of the Rician K-factor, spatial correlation of the antenna array, time-varying effect of the V2V channel, and the power allocation factor is discussed. Furthermore, we also analyze the capacity of NOMA-SM. By analyzing the capacity and deriving closed-form upper bounds on the capacity, a pair of power allocation optimization schemes are formulated. The optimal solutions are demonstrated to be achievable with the aid of our proposed algorithm. Again, instead of simply invoking a pair of popular techniques, we intrinsically amalgamate SM and NOMA to conceive a new system component exhibiting distinct benefits in the V2V scenarios considered.

Wireless caching using device-to-device (D2D) communications is a promising approach for reducing delivery delay and improving spectrum efficiency and energy efficiency. In addition to the physical link condition, social behaviors are also of great importance for effectiveness enhancement of the D2D-based wireless caching schemes. In this work, we focus on resource allocation including power and spectrum to improve the system efficiency in terms of jointly considered spectrum efficiency and energy efficiency, by leveraging both physical and social characteristics. Furthermore, a heuristic-based mixed integer nonlinear programming algorithm is proposed to solve the joint optimization problem, including the establishment of D2D links, spectrum allocation and power optimization. Numerical results demonstrate the effectiveness of the proposed scheme in terms of different pairing algorithms.

This work investigates the performance for D2D enabled wireless caching in distributed storage system by taking the typical erasure correcting code namely maximum distance separable (MDS) into consideration. Targeting on the minimization of the transmission cost for content sharing, the spectrum resource allocation is optimized based on their statistical channel state information (CSI). To evaluate the quality of D2D links, two cases are considered with different assumptions for whether those physical links are stable or not regarding to mobility impacted user behaviors. Numerical results demonstrate the effectiveness of the proposed scheme.

This work studies secrecy enhancement for socially enabled communications in cellular device-to-device (D2D) underlay through resource allocation. By analyzing the impact of social characteristics of those involved D2D users (DUs), proper selection of transmitters and friendly jammer nodes with appropriate power and spectrum allocation can facilitate secrecy transmission. First, we select D2D transmitter by characterizing the link to guarantee the desired success rate without downgrading the service requirement for impacted cellular users (CUs). Furthermore, jammers' trustiness based on their own social interaction is considered for secrecy enhancement. Numerical results verify the performance of our proposed scheme.

Distributed storage that leverages cellular device-to-device (D2D) underlay has attracted rising research interest due to its potential to offload cellular traffic, improve spectral efficiency and energy efficiency, and reduce transmission delay. This paper investigates the overall transmission cost minimization problem based on a content encoding strategy to download a new content item or repair a lost content item in D2D-based distributed storage systems while guaranteeing users' quality of service. In addition to the optimization of the coding parameters, the cost minimization problem also considers the distribution of content items, the selection of content helpers for each content requester, and the spectrum reuse for establishing D2D links in between. Formulating a hypergraph-based three-dimensional matching problem among content helpers, requesters, and cellular user resources, we present a local search based algorithm with low complexity for optimization. Numerical results demonstrate the performance and the effectiveness of our proposed approach.

This paper analyzes the effect of distributed time-reversal (DTR) transmission scheme on the signal-to-noise ratio (SNR) at its intended and unintended receivers. By focusing the temporal and spatial signal energy on the intended receiver, DTR can effectively maintain a satisfactory SNR level while lowering received signal level at passive eavesdroppers or unintended co-channel users. The DTR performance is analyzed in terms of a SNR gap between the desired and unintended receivers. The SNR gain of DTR is also analyzed over traditional distributed direct transmission and several cooperative beamforming transmission schemes without time-reversal. Numerical results demonstrate the performance improvement of the time-reversal transmission and the validity of our analytical results.

This paper investigates the problem of resource and power allocation in device-to-device (D2D) underlays given a specific secrecy rate constraint. The objective is to optimize the pairing of D2D links with cellular user equipment (CUE) uplink channel resources, and to allocate their respective powers to combat against eavesdroppers for secrecy rate improvement. The proposed method first determines a set of candidate D2D links with the required signal-to-interference-plus- noise ratio level for each CUE to narrow the number of combinatorial sharing options. Afterwards, an optimization problem is formulated for maximizing the overall secrecy rate under user power constraints and minimum required secrecy rates. Finally, numerical results demonstrate the resulting performance.

Device-to-device (D2D) communications recently have attracted broad attention owing to its potential ability to improve spectrum and energy efficiency within the existing cellular infrastructure. Lacking sophisticated control, D2D user equipments (DUEs) themselves are not powerful enough to resist eavesdropping or fight against security attacks. This work investigates selection of jamming partners for D2D users to thwart reception by social outcasts in D2D overlay, by exploiting social relationship to improve secrecy rate. We aim to maximize the secrecy rate of worst case, eavesdropping by any outcast, through selecting jammer node while allocating transmit power for both source and jammer. We present a heuristic genetic algorithm based solution to evaluate the problem directly. In addition, we also propose approximated optimization solutions by considering power allocation of upper and lower bounds to simplify the problem, by leveraging the fractional programming (GFP) oriented Dinkelbach-type algorithm. Numerical results show that the proposed schemes can achieve better performance through finding an appropriate partner.

Exploiting direct transmissions between geographically close mobile users without passing through the base stations, device‐to‐device (D2D) communications contribute significant improvement to the spectral efficiency of cellular networks. In D2D‐assisted cellular networks, the social interaction of mobile users is an important property that will affect the practical performance and should be seriously accounted in the network resource allocation, which is yet to be fully explored. In this study, the authors investigate the social interactions for D2D transmissions and develop a contact time model to characterise the D2D links. A D2D link can be considered for resource allocation only when the two users encounter and their contact time is enough long to complete a meaningful transmission. They formulate and compare both sociality‐blind and sociality‐aware optimisation problems for resource allocation in D2D‐assisted cellular networks. Extensive numerical results are presented, validating that the sociality‐aware resource allocation can achieve higher performance than that of the sociality‐blind approach.

Multi-radio multi-channel (MR-MC) networks contribute significant enhancement in the network throughput by exploiting multiple radio interfaces and non-overlapping channels. While throughput optimization is one of the main targets in allocating resource in MR-MC networks, recently, the network energy efficiency is becoming a more and more important concern. Although turning on more radios and exploiting more channels for communication is always beneficial to network capacity, they may not be necessarily desirable from an energy efficiency perspective. The relationship between these two often conflicting objectives has not been well-studied in many existing works. In this paper, we investigate the problem of optimizing energy efficiency under full capacity operation in MR-MC networks and analyze the optimal choices of numbers of radios and channels. We provide detailed problem formulation and solution procedures. In particular, for homogeneous commodity networks, we derive a theoretical upper bound of the optimal energy efficiency and analyze the conditions under which such optimality can be achieved. Numerical results demonstrate that the achieved optimal energy efficiency is close to the theoretical upper bound.

This paper proposes a joint cooperative relaying and jamming scheme based on distributed beamforming to enhance the secrecy rate of a wireless channel in the presence of an eavesdropper. Specifically, we consider the scenario that a source node transmits messages to a destination with the help of multiple cooperative relays, where a relay might be compromised to become an eavesdropper as an inside attacker. Our joint relaying and jamming approach is to assign some relay nodes to act as jammers to interfere the eavesdropper, while the remaining ones continue relaying information to the destination. We propose a protocol to implement the joint cooperative relaying and jamming. Our protocol further considers the particular challenge brought by the inside attacker: it may know the jamming signal and can use it to remove the interference from the jammers. The issue of phase and frequency synchronization in the distributed beamforming is also taken into account. A mixed integer programming problem is formulated to maximize the secrecy rate with the proposed joint relaying and jamming, by which the optimal role assignment to a relay and the associated optimal power assignment can be solved. Numerical results are presented to demonstrate the efficiency of the proposed joint relaying and jamming in improving the secrecy rate, with comparison to existing approaches.

Device-to-device (D2D) communication brings significant benefits to improve resource utilization and users' throughput as an underlay to cellular networks. This paper first proposes an efficient admission policy based D2D clustering scheme to increase the system rate. By analyzing the interplay between the D2D clusters and the arrival user who intends to join a D2D cluster, we present two attraction functions describing the mutual suitability by considering social interaction, energy balance, and location as well. Further, we formulate the probability of the arrival user joining a certain D2D cluster based on Chinese Restaurant Process (CRP) and utilize a matching function to assign an optimal D2D cluster for each arrival user. On the other hand, we also illustrate how the cluster head can be selected in a D2D cluster. Finally, numerical results demonstrate that our clustering scheme efficiently leads to a good performance on the system rate and the stability of D2D clusters.

This study proposes a security‐oriented cooperation (SOC) scheme, which first determines one of the three possible cooperation scenarios, namely the jammer only, relay only and the relay–jammer pair, according to the security requirement and network's operational conditions. After determining a cooperation scenario, then, the selection of a relay or/and a jammer as well as their transmit power are jointly optimised with the objective to attain a good trade‐off between security performance and energy consumption. The performance of the networks employing the proposed SOC is investigated here over finite‐state Markov channels. The studies and results explain that the proposed SOC scheme constitutes one of the promising SOC schemes. It belongs to a generalised cooperative security scheme, which adapts according to the specific security requirement and communication environments.

In order to make a thorough evaluation of the security against eavesdropping in heterogeneous radio access network (RAN) with relays, this paper proposes a novel secrecy graph-based secrecy evaluation scheme for distributed wireless relay networks with eavesdropping present. Our scheme firstly models user and eavesdropper with the nonuniform distributions in a discrete system and decides the best path choice of users and relays for the purpose of security. Secondly, we derive the average eavesdropping effect (AEE) based on the known distribution and arrangement of uplink path, and investigate the derived results and the different relay deployment plans. Taking into account various factors (fluctuation of user traffic, burden limitation of relay etc), we efficiently utilize the available resources to simulate the real scenarios to implement the accurate secrecy evaluations. Finally, the simulation results prove that our proposed scheme outperform the other existing ones.

This paper proposes a cluster-based optimal relay and jammer selection (CB-ORJ) scheme to deal with interception and improve the secrecy rate in wireless multi-hop networks in the presence of both intra-cluster and inter-cluster eavesdroppers. We first present a novel mathematical model of cluster-based secure transmission. Furthermore, we propose an on-demand relay and jammer selection approach based on the proposed model with power allocation to optimize the secrecy rate subject to a total transmit power constraint under different security requirements. To confuse the intra-cluster eavesdroppers, the cluster heads assign appropriate cluster members as jammers, while for the inter-cluster eavesdroppers, the transmitting cluster and the receiving cluster jointly select their cluster members as jammers. Finally, simulation results demonstrate that our scheme outperforms existing ones in terms of secrecy outage probability (SOP) while guaranteeing a lower desired energy consumption.

This paper proposes a novel amplify-and-forward with cooperative-jamming (ACJ) scheme. In order to make a balance between secrecy capacity (SC) and energy consumption, we use RSεwhich is defined as the maximum equivalent SC for each unit of energy consumption to evaluate the performance of wireless communications. This paper shows that although SC increases with energy consumption, RSεis not a monotone increasing function. That is to say, when energy consumption exceeds a specific value, the efficiency of each unit of the energy consumed decreases in aspects of the security. In this paper, we not only study the variation tendency of SC changing along with energy consumption, but also use the eigenvalue method to derive a precise equation of RSε. Moreover, simulation results also demonstrate that our model performs quite well in terms of lowering energy consumption to the least, while guaranteeing the security requirements in wireless cooperative networks.

In order to reduce the energy consumption, we propose a novel Macro Base Station (MBS) sleep scheme based on the density of Pico Base Station (PBS) including three sleep approaches for current heterogeneous networks (het-net). Our scheme consists of two parts. In the first part, by setting thresholds for PBS density according to the total coverage of PBSs in a macro-coverage, we divide the network into three scenarios where PBSs are deployed densely, sparsely, and commonly correspondingly. In the second part, the MBS chooses one sleep approach of our scheme by comparing its PBS density with the thresholds. In each approach, minimizing the system energy consumption and meeting the QoS requirements are both considered as our goals. Particularly, the offset value is designed to guarantee blocking probability, especially when the neighboring MBSs are in charge of taking over the migrated users from the sleep MBS. Finally, simulation results demonstrate that the proposed scheme is much more efficient than the other existing schemes in terms of the power consumption and the blocking probability.

In order to conserve energy and achieve better quality of service (QoS) provisioning, this paper proposes a novel security-oriented cooperative (NSOC) scheme for distributed wireless relay networks in presence of eavesdroppers. Our scheme includes two parts. In part one, we model the wireless relay network and analyze the secure connectivity by exploiting the secrecy graph. Then, when the result of the analysis is inferior to the security demands, our developed cooperative jamming strategy is kicked in to further assist the secure communications, which plays the important role in secure communications. In the cooperative jamming strategy, the channels established by candidate nodes are modeled as finite-state Markov channels (FSMCs) owing to the property of time-varying, and the remaining energy of nodes is considered as well. In addition, a jammer selection policy using simple priority index is depicted, taking secrecy capacity (SC) and energy balance into account. The major superiority of this scheme is to reduce unnecessary energy costs and system complexity, while assuring the security and reliability. Finally, the obtained simulation results show that our proposed scheme outperforms the existing ones especially in terms of secrecy capacity and system reward.

To further enhance the quality of service (QoS), this paper proposes a novel multi-objective relay-jammer pair selection scheme in wireless cooperative networks where the wireless channels are modeled as first-order finite-state Markov channels (FSMCs). The FSMC model is used to approximate the time variations of the average received signal-to-noise ratio (SNR), channel power gain, and remaining power of nodes. In addition, the candidate cooperative nodes can be selected as relay and jammer, respectively, to assist the transmission or disturb the eavesdropper. Thus, we can formulate a restless bandit problem to model and analyze how to select the optimal relay-jammer pair according to the desired QoS optimization goals, in terms of energy balance, mitigating error propagation and increasing secrecy capacity (SC). The obtained simulation results show that our proposed schemes outperform the other existing schemes.

To cope with the too much energy consumption of base station (BS), this paper firstly gives a green heterogeneous network architecture, in which some BSs could be switched off or set as sleep mode when its local traffic is low, while the resident users rely on the mobile relay network composed by intelligent mobile nodes to connect to one of its neighboring BSs. Moreover, in order to achieve better performance, a mathematical model for relay selection is proposed by considering multiple factors, such as secrecy capacity (SC) for selected channels, remained energy¿iRE¿j for relays and location information. Accordingly, a novel dynamic relay selection approach based on Principal Component Analysis (PCA) is brought forward. Using this approach,the related factors which affect the quality of service (QoS) provisioning most could be found out by processing the observation data, and the their weights could be computed in a real-time way as well. The significant property of this approach is to reduce the useless energy loss by removing the redundant parameters. Finally, simulation results prove that the proposed approach outweights the existing ones in terms of system rewards, average throughput, and network lifetime.

An adaptive resource allocation scheme for QoS oriented OFDMA system, which schedules two different utility functions for the Real-time/Non Real-time traffic simultaneously, is proposed in this paper. Instead of partial consideration of uniform kinds of QoS, we introduce an updating ratio factor to schedule users of heterogeneous traffic. Due to the complex optimization objective, the general convex optimal methods are no longer feasible. We are motivated to study a heuristic natural genetic approach to solve this problem. Due to the weak convergence of Genetic Algorithm (GA), we improve it by a well-selected initial population. Numerical results are presented to illustrate that our scheme not only tackles the diverse QoS requirement but also alleviates the unfairness between real-time and non-real-time services under various traffic loads.

This paper firstly puts forward a two dimensional interworking authentication model between 3GPP and WiMAX. In the proposed model, EAP-AKA method is adopted, and the first registered 3GPP AAA server in home domain (i.e. HPLMN, Home Public Land Mobile Network) takes charge of the responsibility of authentication for 3GPP/WiMAX UE by using authentication vectors (AV) obtained from HSS (Home Subscriber Server). Furthermore, a dynamic periodic distributing scheme for AVs is brought forward. Meanwhile, an improved authentication protocol based on EAP-AKA (EAPIAKA) is proposed, and the relative security analysis is given as well. By using of the scheme, it is unnecessary to retrieve new Avs from HSS, if there are still unused AVs for the previous PLMN, when the 3GPP/WiMAX UE leaves and reenters the same PLMN within its available period of critical period interval (CPI). Finally, the simulation results prove that a long CPI results in fewer accesses to the HSS, and only through adjusting the CPI period dynamically in accordance with the system parameters, the signaling cost can be reduced further.