Next-generation edge communication systems are expected to support large-scale connectivity and service provisioning for billions of smart terminal devices. Edge blockchain-based computing architectures enable coordinated management of homogeneous and heterogeneous resources, offering a promising framework for drones collaboration and resource sharing. Confronting the dual challenges of computational constraints and energy limitations in drone networks, this work formulates an optimized multi-auction resource allocation markets framework integrated with blockchain, establishing a new paradigm for large-scale autonomous system resource coordination. Firstly, focusing on computational resource allocation, we establish a double-auction primary market where drone miners (as buyers) procure computational services for blockchain consensus process, while edge service providers (as sellers) supply computation offloading services. Through optimal bidder matching, the primary market system achieves social welfare maximization with equilibrium strategies. For successful computational buyers, we then design a VCG (Vickrey-Clarke-Groves) auction-based energy compensation scheme that provides optimal battery service pricing in the secondary market. Extensive simulations validate the effectiveness in maintaining truthfulness, individual rationality, and validity.

Unmanned aerial vehicles (UAVs) have emerged as pivotal components in next-generation communication systems due to their broad coverage and flexible deployment capabilities, enabling efficient connectivity with multiple ground users. Although the integration of nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) technologies in UAV communications has attracted growing research interest, existing studies remain insufficient for jointly optimizing beamforming and power allocation, particularly in terms of fully capturing the complex coupling among decision variables. This study investigates the joint optimization problem of beamforming, power allocation, and UAV position optimization in UAV systems, where the UAV communicates with multiple ground users using NOMA and MIMO technologies. The core objective is to maximize the achievable transmission rate of the system while complying with a total power budget constraint. Owing to the inherent nonconvexity of the formulated problem and the intricate coupling among decision variables, the original problem is decomposed into three subproblems: beamforming, power control, and UAV placement optimization. To address these subproblems efficiently, we propose an angle-sector-based iterative optimization framework by invoking the alternating optimization technique under the NOMA-MIMO system. This strategy not only enhances overall spectral efficiency but also ensures reliable connectivity for users at greater distances while maintaining high communication quality for those in proximity. The simulation results demonstrate that the adopted user grouping strategy, which incorporates a group matching mechanism, yields notable improvements in resource utilization. Compared with other solution strategies, the proposed alternating optimization algorithm exhibits superior performance in terms of achievable rate enhancement, thereby validating its effectiveness and practical value in complex UAV-enabled NOMA-MIMO systems.

The proliferation of delay-sensitive applications in the Internet of Vehicles (IoV) poses significant challenges to conventional vehicular edge computing (VEC), particularly in terms of limited coverage and constrained computing resources. To address these issues, this paper proposes a market-driven air–ground collaborative vehicular edge computing framework that unifies heterogeneous computing services provided by an unmanned aerial vehicle (UAV), roadside unit (RSU), and vehicle platoon (VP) within a common pricing-and-allocation mechanism. The interaction among service providers and the user vehicle (UV) is modeled as a multi-leader single-follower Stackelberg game, where UAV, RSU, and VP act as heterogeneous leaders that announce service prices, and the UV acts as the follower that determines task allocation ratios. The main contribution of this work lies in establishing a unified economic coordination model for heterogeneous air–ground edge services, together with a numerical equilibrium computation framework tailored to the resulting low-dimensional bounded pricing game. We show that the follower-side optimization problem is convex and admits a unique optimal response, and that the upper-level pricing game admits at least one Nash equilibrium. Based on this structure, we develop a coarse-to-fine grid-search-based Stackelberg equilibrium computation method (CFGS-SE), which directly verifies best-response consistency through numerical evaluation and local refinement. Simulation results show that the proposed method achieves high follower utility and low energy consumption while maintaining competitive delay performance and balanced task allocation. These results demonstrate that the proposed framework provides an effective solution for price-guided task allocation in air–ground collaborative vehicular edge computing.

This paper presents a Dual-Security-Assured Computation Offloading (DSACO) scheme for space-air-ground networks (SAGNs), which exploits the integrated sensing, communication, and computing (ISCC) capability of the low Earth orbit (LEO) satellite to support secure and efficient computation offloading. In the proposed scheme, the air-ground mode serves as the default edge-processing strategy due to its low latency and energy consumption, while the LEO satellite senses the malicious aerial eavesdropper and protects the ground device (GD)-to-UAV links through directional anti-eavesdropping jamming. When such protection becomes insufficient, the system switches to direct GD-to-LEO offloading via dedicated frequency bands. Accordingly, the secure offloading process is formulated as a worst-case average long-term energy minimization problem under sensing uncertainty. To solve the resulting dual-timescale mixed-integer nonlinear problem, we develop a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework for the joint optimization of sensing duration, UAV trajectories, computation offloading, and resource allocation. Simulation results demonstrate that the proposed scheme significantly enhances system security while maintaining offloading efficiency, and that the H-MADRL framework outperforms benchmark methods.

Owing to the on-demand deployment, low cost, and flexibility, unmanned aerial vehicles (UAVs) are capable of performing tasks such as data monitoring, collection, and sharing. However, the openness of UAV wireless networks makes data susceptible to security threats such as theft, tampering, and forgery during collaborative data sharing and mission execution. Additionally, the limited resources and high mobility of UAVs further exacerbate challenges related to data security and reliability. To address these issues, this paper proposes a blockchain-based secure data sharing scheme for cloud-assisted multi-UAV networks. Specifically, we leverage cloud-based infrastructure to undertake the storage of massive data, significantly offloading the computational and storage burdens on the UAVs. Simultaneously, blockchain is integrated to establish immutable and traceable trust for the shared data, ensuring strong security, integrity, and retrievability under the constraints of UAV resources. On this basis, a certificateless searchable encryption algorithm is employed to eliminates traditional certificates management overhead and enables lightweight, distributed, and efficient search based on predefined keywords. Furthermore, we introduce an access control list based on geofencing to specify the data sharing permissions of UAVs, strictly limiting the data access permissions within specific area, thereby reducing the misuse or abuse of data. This scheme is proven to achieve ciphertext and trapdoor indistinguishability against keyword guessing attacks. The performance analysis indicates that the efficiency advantages of the proposed scheme become increasingly prominent as the number of UAVs increases.

Backscatter communication is emerging as a promising solution for enabling low-power and large-scale IoT applications. However, it faces challenges in terms of widespread deployment, wireless resource management, and quality of service (QoS). In this paper, we first propose a secure transmission architecture to integrate the backscatter network with the existing 5G/IoT infrastructure. Next, we introduce a multi-device scheduling and time-switching strategy aimed at optimizing both capacity and secure throughput. In the time-switching scheme, BDs primarily operate in symbiotic mode without requiring additional spectrum, but can dynamically switch to opportunistic spectrum access mode when necessary, with adaptive time allocation to improve QoS. For multi-device scheduling, BDs are assigned to function as a master transmission node, cooperation node, or spoofing/jamming node, thereby enhancing the system’s resistance to proactive eavesdropping. The optimization problem is formulated to minimize spectrum resource usage while ensuring QoS and following the energy constraint. To solve this, we introduce an enumeration-based interior-point algorithm (EIA) and design a novel progressive greedy algorithm (PGA). The EIA method provides optimal solutions, while the PGA algorithm achieves high-quality suboptimal solutions with lower complexity. Extensive simulation results demonstrate that the proposed strategy stands out in ensuring QoS, enhancing security, and reducing spectrum resource usage.

Efficiently running services in the worldwide scattered spectrum bands is increasingly difficult as those bands get ever more congested. Unlicensed spectrum offers a promising solution but effective coexistence among access technologies is required to mitigate interference and ensure the always more stringent Quality of Service required by new services. Besides, the increasing number and complexity of security threats further challenge system reliability. To address these issues, this paper proposes a mechanism to enhance security and reliability , and a blockchain-based framework for spectrum sensing and sharing in Wi-Fi and Long Term Evolution-Unlicensed (LTE-U) coexistence networks. Performance under different channel fading models is analyzed, and obtained results show the effectiveness of the proposed scheme, improved sensing accuracy, and resistance to malicious behavior in both low- and high-density scenarios.

Integrated sensing and communication (ISAC) is pivotal for enabling simultaneous environment perception and data transmission in intelligent transportation systems (ITS). However, mission-critical ITS management applications, such as collision avoidance and autonomous driving, require stable and reliable ISAC services. Unfortunately, dense urban canyons, with their skyscraper-induced occlusions, create persistent coverage blind zones, posing significant challenges to these applications. To address these challenges, this paper explores a novel aerial intelligent reflecting surface (AIRS)-assisted ISAC system, where multiple AIRSs dynamically reconfigure the wireless propagation environment to enhance multi-vehicle sensing and base station (BS)-to-multiuser communication. To maximize the minimum achievable communication rate while ensuring sensing performance, we formulate a joint resource allocation problem considering BS beamforming, AIRS trajectory optimization, AIRS phase shift control, and user association. Given its highly coupled and nonconvex nature, we develop an alternating optimization framework tackling each subproblem sequentially. Specifically, we employ the Lagrangian dual transform and semi-definite relaxation (SDR) for BS beamforming, the successive convex approximation (SCA) method for AIRS trajectory optimization, matrix decomposition and equivalent rank-constrained transformation techniques for AIRS phase shift design, and a penalty dual decomposition (PDD)-based approach for user association. Furthermore, considering uncertainties in the vehicle’s angle of departure (AoD) due to urban mobility, we derive a worst-case sensing performance bound and generalize the proposed algorithm to a more complex scenario. Simulations validate the algorithm’s effectiveness, demonstrating superior communication rates and sensing performance over benchmark schemes, while ensuring robustness against AoD uncertainties.

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.

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.

Federated learning (FL) in vehicular networks offers the advantage of leveraging extensive distributed vehicle data while addressing data privacy and communication overhead concerns. However, conventional FL approaches encounter substantial difficulties due to the limited communication bandwidth and network dynamics in vehicular environments. To mitigate these challenges, this paper introduces a Semantic Communication-Enhanced U-Shaped Federated Split Learning (SC-USFL) framework tailored for image classification tasks in vehicular networks. The proposed framework incorporates a task-oriented semantic communication module, comprising semantic encoder/decoder and channel encoder/decoder components, to efficiently compress and transmit task-relevant features, thereby significantly reducing communication overhead. Additionally, a network status monitor (NSM) module is developed to dynamically adjust the compression ratio (CR) based on real-time network conditions, ensuring an effective balance between communication efficiency and classification performance. Extensive simulations on the CI-FAR10 dataset demonstrate that the proposed SC-USFL framework effectively manages the trade-off between communication overhead and classification accuracy. Furthermore, the results indicate robust performance over both additive white Gaussian noise (AWGN) and Rayleigh fading channel conditions, validating the effectiveness and adaptability of the proposed framework.

Unmanned aerial vehicle (UAV) and high-altitude platform (HAP)-enabled aerial edge computing (AEC) networks facilitate diverse Internet of Things (IoT) applications. In this paper, we investigate the average task completion time and energy consumption by jointly optimizing sensing, communication and computing in cooperative AEC networks facilitated by multiple UAVs and HAP. The sensing times, multi-UAV trajectories, transmission power control, offloading strategy and communication resource allocation are jointly optimized. We transform the original optimization problem into minimizing the average task completion time while ensuring energy consumption stability by introducing Lyapunov optimization theory. The problem is then decomposed into multiple subproblems, which are solved through numerical analysis, successive convex approximation, and the Dinkelbach algorithm, respectively. These algorithms are embedded into the proximal policy optimization (PPO)-based multi-agent deep reinforcement learning (MADRL) framework to speed up the convergence performance of the MADRL model. Simulation results demonstrate that the proposed algorithm achieves superior performance in terms of average task completion time and energy consumption.

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.

We propose a collaborative optimization framework for integrated sensing, communication, and computing (ISCC) in the unmanned aerial vehicle (UAV)-assisted space-air networks. This framework employs an UAV for data sensing and relay, providing wireless access to terrestrial sensing devices (TSDs), while a LEO satellite serves as an offloading edge computing server in space. Acknowledging the temporal criticality of tasks within the designated service area, we classify the service area with different priorities, with the UAV prioritizing service delivery to high-priority regions. Based on sensing satisfaction and service duration, we formulate a multi-objective problem with the goal of maximizing the total satisfaction while minimizing the service duration. We propose a PPO-based deep reinforcement learning (DRL) algorithm to find the optimal solutions. To address long-term dependencies in sequential data, we embed a long short-term memory (LSTM) module into the DRL algorithm. Compared to the baseline algorithms, the proposed algorithm achieves improvements in accumulated rewards of approximately 4.1%, 14.5%, and 21.2%, respectively.

Backscatter communication (BC) becomes a promising energy-efficient solution for future wireless sensor networks (WSNs). Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs), yet conventional UAVs rely on omni-directional fixed-position antennas (FPAs), limiting channel gain and prolonging data collection time. To address this issue, we consider equipping a UAV with a directional movable antenna (MA) with high directivity and flexibility. The MA enhances channel gain by precisely aiming its main lobe at each BD, focusing transmission power for efficient communication. Our goal is to minimize the total data collection time by jointly optimizing the UAV's trajectory and the MA's orientation. We develop a deep reinforcement learning (DRL)based strategy using the azimuth angle and distance between the UAV and each BD to simplify the agent's observation space. To ensure stability during training, we adopt Soft Actor-Critic (SAC) algorithm that balances exploration with reward maximization for efficient and reliable learning. Simulation results demonstrate that our proposed MA-equipped UAV with SAC outperforms both FPA-equipped UAVs and other RL methods, achieving significant reductions in both data collection time and energy consumption.

This paper introduces a novel approach named the Privacy-Preserving Socio-Aware Model (PSocLF) for Short-Term Residential Load Forecasting, which addresses the need for forecasting models tailored to district-specific socio-demographic characteristics. By leveraging sociodemographic characteristics and personalized socio-aware knowledge sharing, PSocLF develops district-level forecasting models that enhance load forecasting precision while safeguarding individual privacy. Within PSocLF, we propose a new model structure named the Self-Gating TSMixer (SGTSMixer), which integrates self-gating mixing procedures with stacked multi-layer perceptrons (MLPs). It can efficiently extract temporal patterns and incorporate socioaware information to improve prediction accuracy. Simulation results based on real-world data demonstrate the effectiveness of the proposed PSocLF framework, outperforming alternative training paradigms and benchmarks in model structure design, particularly in scenarios with varying sociodemographic characteristics among districts. This paper contributes to advancing federated residential load forecasting and highlights the practical benefits of integrating sociodemographic information for improved forecasting accuracy and effectiveness.

This paper investigates the challenges of user pairing and power allocation in unmanned aerial vehicle (UAV) systems that use nonorthogonal multiple access (NOMA) to communicate with multiple ground users. The main goal is to maximize the achievable transmission rate of the system while ensuring the quality of service (QoS) requirements of users, under a constrained total power budget. Considering the non-convexity of the original problem, a stepwise optimization approach is adopted. To improve resource utilization and solve the user pairing problem, a deep Q-network method assisted by group matching is proposed. Specifically, the group matching method is applied to allocate users, and an optimized deep Q-network (OP-DQN) is used to optimize power allocation strategies. The simulation results show that compared with other user pairing strategies, this method significantly improves resource utilization and fairness. In addition, the proposed scheme effectively enhances the system transmission rate and resource efficiency.

With the high-speed and low-latency connection capabilities brought by the wireless communications, the performance of unmanned aerial vehicle (UAV) swarm for executing tasks such as emergency response, smart farming, and aerial base station has been significantly improved. As the use of UAVs becomes more prevalent in various tasks, the demand for cross-domain task allocation and operations continues to grow. Cross-domain task allocation enables the UAVs to flexibly execute tasks over a wider range, work together in different task domains, thereby improving operation efficiency. However, due to variations in management policies, authentication protocols, and security standards across various domains, to ensure the secure and reliable interoperability of UAVs across diverse domains, cross-domain authentication has become the primary line of defense for UAVs. In this paper, we propose a lightweight and trusted blockchain-based cross-domain authentication scheme for UAV swarm. To achieve a lightweight authentication process, we apply certificateless authentication and avoid costly operations for resource-constrained UAVs. Additionally, the trustworthiness of UAVs and domains is evaluated through credibility, which is stored on blockchain. We analyze the theoretical security of the proposed scheme, and extensive experiments demonstrate its efficiency.

Artificial Intelligence Generated Content (AIGC) is a paradigm that utilizes AI models to automatically generate content. Within the framework of Internet of Vehicles (IoV) networks, mobile AIGC services present numerous advantages over conventional cloud-based solutions, notably enhanced network efficiency and enhanced data security and privacy. However, the implementation of AIGC services often demands significant resources, leading to challenges for Roadside Units (RSUs) in managing information uncertainty and effectively addressing all user service requests without compromising overall system performance. To address these challenges, in this paper, we propose a multi-to-multi contract-based incentive mechanism for AIGC services within vehicle edge computing. This approach leverages contract theory to design an optimal contractual framework between Mobile AIGC Service Providers (MASPs) and vehicles. We present the necessary and sufficient conditions for achieving an optimal contract and explore the simplification of the associated constraints. The simulation results demonstrate that our proposed method effectively provides incentives and outperforms existing benchmark schemes.

In this paper, we propose a novel integrated sensing and communication (ISAC) scheme for symbiotic radio (SR)-based IoT systems, addressing key challenges such as inter-symbol interference (ISI) and frequency-selective fading caused by wideband access. To tackle these issues, we develop a symbol-level delay alignment modulation (SL-DAM) precoding that lever-ages temporal degrees of freedom and delay-domain manipulation to suppress ISI and enhance sensing accuracy. A waveform optimization problem is then formulated to maximize sensing performance under primary and secondary communication rates and power constraints. A joint S-Lemma and successive convex approximation algorithm is proposed to solve this non-convex problem efficiently. Simulation results confirm the effectiveness of the proposed SL-DAM precoding in wideband SR-enabled ISAC systems.

Physical layer security (PLS) in symbiotic radio (SR) systems is primarily considered for passive eavesdropping scenarios. However, overlooking the impact of proactive eavesdroppers poses significant risks. In this paper, we focus on secure transmission in SR systems under proactive eavesdropping conditions. A PLS strategy is investigated for a full-duplex facultative symbiotic radio (FD-FSR) system. First, we introduce an innovative FSR protocol. It allows backscatter devices (BDs) to dynamically switch between cognitive and symbiotic patterns. Next, we develop a multi-device scheduling method. It adaptively assigns BDs as transmitters, cooperators, or jammers to enhance the secrecy rate. We formulate the pattern switching and BD scheduling as a mixed integer programming problem (MIP). To solve this, we first decompose it into binary decision-making and multi-variable optimization sub-problems. Then, a low-complexity two-stage optimization strategy is employed. Numerical results demonstrate that our proposed strategy significantly outperforms existing schemes.

In this paper, an age of information (AoI)-aware joint design framework of sampling, transmission, computation, and control is considered for industrial cyber-physical systems. To enhance the control performance, we investigate an edge-enabled control scheme, which allows a physical entity to select its sampling adaptively and processing strategies based on de-mand. By analyzing the impact of sampling and short-packet decoding errors, and the coupling relationship between control accuracy and AoI, the AoI -aware control metric is established. Subsequently, we formulate a joint sampling time, computation offloading, and bandwidth allocation optimization problem to minimize the system's control and energy costs. To tackle the formulated NP-hard problem, we develop a BCD-based algorithm leveraging convex and game theories to obtain a joint optimization strategy in an iterative manner. Finally, the performance of the proposed edge-enabled control scheme is verified in the simulation results.

The use of Unmanned Aerial Vehicles (UAVs) as aerial base stations has attracted increasing research interest in recent years. A key challenge in this field is determining how to deploy multiple UAVs in dynamic environments, particularly where mobile user demands fluctuate. To address this challenge, this paper presents an adaptive UAV deployment scheme in a dynamic multi-UAV wireless network, considering the mobility of UAVs and users, state variability, and adjustable UAV transmission power. By jointly optimizing the UAVs’ operational modes, transmission power levels, and movement strategies, our objective is to achieve a trade-off between minimizing power consumption and maximizing ground user coverage. A Deep Reinforcement Learning (DRL) approach is proposed to address these challenges. To capture the dynamic variations of users and UAVs in the environment, a multi-modal feature state space is designed, consisting of both a multi-channel image and vectors. The image component integrates real-time data on user distribution and the UAV coverage area, while the vectors represent UAV operational modes, position data, and system temporal information. These multi-modal features are processed using a combination of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for advanced feature extraction. To enhance training stability and efficiency, the proposed approach updates parameters using the Proximal Policy Optimization (PPO) method. Simulation results demonstrate the effectiveness of the proposed scheme in balancing power consumption and coverage while effectively managing system dynamics.

Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge computing (VEC) system. Federated learning (FL) stands as one of the fundamental technologies facilitating collaborative model training locally and aggregation, while safeguarding the privacy of vehicle data in VEI. However, traditional FL faces challenges in adapting to vehicle heterogeneity, training large models on resource-constrained vehicles, and remaining susceptible to model weight privacy leakage. Meanwhile, split learning (SL) is proposed as a promising collaborative learning framework which can mitigate the risk of model wights leakage, and release the training workload on vehicles. SL sequentially trains a model between a vehicle and an edge-cloud (EC) by dividing the entire model into a vehicle-side model and an EC-side model at a given cut layer. In this work, we combine the advantages of SL and FL to develop an adaptive split FL scheme for VEC (ASFV). The ASFV scheme adaptively splits the model and parallelizes the training process, taking into account mobile vehicle selection and resource allocation. Our extensive simulations, conducted on nonindependent and identically distributed data, demonstrate that the proposed ASFV solution significantly reduces training latency compared to existing benchmarks, while adapting to network dynamics and vehicles’ mobility.

With the increasing complexity of next generation network applications and the coexistence of diverse service requirements, Generative AI (GAI) and Large Models (LMs) based semantic communication are widely regarded as promising solutions to address these challenges. The goal of these systems is not only to reduce system burden by reducing transmission data, but also to adapt to new and complex requirements. In this paper, we propose a semantic communication system designed to meet diverse requirements of speech applications while enabling accurate speech transmission. The semantic encoder comprises an unsupervised model wav2vec 2.0 for learning universal speech representations to enable adaptability across various speech-related tasks. It also includes a prosodic feature encoder from the style embedding module of Global Style Tokens (GST) Tacotron. The semantic decoder integrates a phoneme recognition module and a GST-Tacotron-based text-to-speech (TTS) module to facilitate accurate and expressive reconstruction of the original speech signal, with the incorporation of prosodic features enhancing the naturalness and intelligibility of the synthesized speech. The proposed system has been tested in noisy channels. It demonstrates that the system maintains superior and robust performance even at Bit Error Rate (BER) of 10−1, as reflected by a stable Character Error Rate (CER) approximately 0.0940 and 0.0649 for the base and large versions of wav2vec 2.0 respectively in speech recognition, and consistent cFDSD scores approximately 0.9 in speech quality assessment. This performance surpasses that of the existing semantic communication systems, while also providing reliable support for a wider range of downstream speech applications.

With the advancement of vehicular networking technology, communication between vehicles, and between vehicles and cloudlets, is becoming increasingly frequent, leading to a growing demand for computing resources. This growing demand necessitates more robust and efficient computing solutions to handling the data exchange and processing requirements. Mobile edge computing (MEC) addresses computing demands by leveraging edge resources. In practice, numerous parameter constraints, such as task volumes and available resources, render optimal resource management challenging. This paper presents a vehicular networking communication scenario involving an MEC-enabled roadside unit and multiple vehicles. We propose a new method that jointly optimizes task offloading decisions along with power and bandwidth allocation, aiming to minimize system energy consumption. Given the non-convexity of the original problem, characterized by the complexity and interdependence of multiple optimization variables, we adopt a strategic approach to decouple it into two sub-problems. The problem can be solved using deep learning and subgradient methods separately. Finally, a refined solution can be obtained through iterative solving with the block coordinate descent (BCD) method. Simulations provide compelling evidence that our scheme significantly reduces system energy consumption, outperforming benchmarks and showcasing its superiority.

Unmanned aerial vehicles (UAVs) are vulnerable to interception and attacks when operated remotely without a unified and efficient identity authentication. Meanwhile, the openness of wireless communication environments potentially leads to data leakage and system paralysis. However, conventional authentication schemes in the UAV network are centered on the fixed trust boundary, ignoring potential internal threats and failing to flexibly respond to the dynamic requirements of UAV access and identity authentication. Additionally, UAVs are not subjected to periodic repetitive identity authentication, leading to difficulties in controlling access anomalies. Therefore, in this work, we consider a zero-trust framework for UAV network authentication, aiming to achieve UAV identity authentication through the principle of "never trust and always verify". We introduce a blockchain-assisted zero-trust authentication scheme, namely BAZAM, designed for multi-UAV wireless networks. In this scheme, UAVs follow a key generation approach using physical unclonable functions (PUFs), and cryptographic technique helps verify registration and access requests of UAVs. The blockchain is applied to store UAVs authentication-related information in immutable storage. Through thorough security analysis and extensive evaluation, we demonstrate the effectiveness and efficiency of the proposed BAZAM.

Mobile Edge Computing (MEC) is considered as a promising solution for augmenting the computational capabilities of Internet of Things (IoT) devices by offloading tasks to nearby edge servers (ESs). However, finite communication and computational resources at both IoT devices and ESs, coupled with escalating congestion as more devices connect, present critical challenges in maintaining low latency and high efficiency. In this work, we jointly design the task sensing, communication, and resource allocation for MEC with Non-Orthogonal Multiple Access (NOMA). To address the urgent need for timely data processing in IoT applications, we utilize the Age of Information (AoI) metric as a measure of data freshness. With the objective to minimize the system cost, we propose to jointly optimize sensing sampling intervals, sensing frequencies, offloading decision, and power allocation. Recognizing that this problem is NP-hard, we decompose it incrementally and propose a High-Dimensional Progressive Cost Optimization (HDPCO) algorithm to reduce overall system cost. Simulation results confirm the effectiveness of HDPCO, showing significant improvements in minimizing overall system cost compared to other proposed schemes.

Along with the rapid development of the fourth industrial revolution, industrial cyber-physical systems (ICPS) are anticipated to achieve precise mapping and management for the physical world by integrating digital sensing and automated control. However, the conflict between limited computing resources and extensive sampling data, combined with severe industrial interference, exacerbates the system’s processing burden and diminishes its accuracy, hindering its ability to meet the low-latency and high-reliability control requirements. To address this issue, this paper investigates an end-edge collaborative control framework to enhance control performance for a short-packet transmission ICPS by providing powerful computation capability. We utilize the age of information (AoI) to characterize the impact of information freshness on control accuracy and construct an AoI-aware control law to assist in data sensing, transmission, and computing strategy design. In addition, we consider the influence of sampling and short-packet decoding errors in AoI-aware control performance to enhance the reliability of sampling and transmission strategies design. A joint optimization scheme of sampling interval, sampling time, computation offloading, and bandwidth allocation based on the block coordinate descent method and game theory is proposed to achieve a tradeoff between the control cost and energy consumption. By considering a real-world trolley inverted pendulum manipulation model, numerical results verify the performance gain of the proposed end-edge collaborative framework and the effectiveness of the presented algorithm.

Unmanned Aerial Vehicles (UAVs) have gained great attention in Internet-of-Things (IoT) applications benefiting from the flexibility of deployment and line-of-sight (LoS) channel conditions. In this paper, we study a UAV-assisted Mobile Edge Computing (MEC) system for providing services to large-scale IoT nodes (INs). In the considered system, the UAV acts as an Aerial Base Station (ABS) that can selectively access large-scale INs to enable efficient data collection and computational offloading while ensuring data integrity. Specifically, we first derive the reconstruction error upper bound based on Graph Laplacian Regularization (GLR) as the data integrity metric. Considering that the UAV is usually limited in energy consumption, we propose an energy efficiency (EE) maximization problem that jointly optimizes the selection of INs, the scheduling of INs, the 3D flight and the computational resource allocation of the UAV, subject to constraints related to UAV motion, resources and data integrity. Due to the non-convex nature of the considered problem, a two-stage algorithm called GDA-3DNACRA is proposed, which adopts Gershgorin Disk Alignment (GDA), Convex Relaxation, and Successive Convex Approximation (SCA) for solving it efficiently. Simulation results have shown that the proposed approach can significantly improve the EE of the UAV while ensuring the data integrity.

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great potential of FL to enhance the architecture of IoMT, the need for effective interference management and the limited energy resources of IoMT devices make the integration of FL into IoMT environments particularly challenging. This study proposes an innovative framework to address these challenges by optimizing power allocation and client selection across participating IoMT devices in the FL process. By employing a Stackelberg game model, our approach orchestrates power allocation among IoMT devices to enhance communication efficiency while adhering to strict differential privacy (DP) standards. Regarding the availability of network state information, we propose non-uniform pricing and uniform pricing strategies, respectively. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. To tackle the issue of energy constraints in IoMT devices, we adopt Lyapunov optimization for adaptive client selection, ensuring sustainable device participation in the FL process over time. In addition, our approach integrates DP to protect patient data, carefully balancing between privacy and the accuracy of the learning model. Our extensive simulations demonstrate marked improvements in privacy preservation, communication efficiency, and energy management efficiency, highlighting the effectiveness of our proposed method over existing solutions.

Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented.

The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocation. This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges on the way of realizing autonomous driving in vehicular edge networks. U-SFL is able to enhance privacy protection by keeping both raw data and labels on the vehicular user (VU) side while enabling parallel processing across multiple vehicles. To optimize communication efficiency, we introduce a semantic-aware auto-encoder (SAE) that significantly reduces the dimensionality of transmitted data while preserving essential semantic information. Furthermore, we develop a deep reinforcement learning (DRL) based algorithm to solve the NP-hard problem of dynamic resource allocation and split point selection. Our comprehensive evaluation demonstrates that U-SFL achieves comparable classification performance to traditional split learning (SL) while substantially reducing data transmission volume and communication latency. The proposed DRL-based optimization algorithm shows good convergence in balancing latency, energy consumption, and learning performance.

Mobile Edge Computing (MEC)-enabled vehicular networks have emerged as a promising approach to enhancing the performance and efficiency of the Internet-of-Vehicles (IoV) applications. By leveraging some vehicles to act as transmission relays, multi-hop task offloading addresses the problem of intermittent connectivity between vehicles and edge servers to cope with the issues of network congestion or obstacles. However, two critical issues, i.e., uncooperative behaviors of selfish vehicles and network resource dynamics, resulting from multi-vehicle concurrent offloading are not fully considered in the existing work. To fill this gap, this paper proposes a novel and efficient task offloading scheme, namely MMTO, that exploits the multi-hop computational resources to maximize the system-wide profit, and supports incentive compatibility of vehicular users and concurrent offloading. Specifically, an iterative hierarchical estimation algorithm is designed to estimate the offloading delay and energy cost in order to iteratively optimize the offloading decisions. An energy-efficient routing approach is then proposed to schedule the transmission paths for the offloading vehicles. Furthermore, an effective reward-driven auction-based incentive mechanism is designed for incentivizing relayers and calculators to engage in collaboration. Both simulation and field experiments are conducted; extensive results demonstrate that MMTO outperforms the state-of-the-art approaches in terms of the system-wide profit improvement and overall task delay reduction.

To leverage the vast amounts of onboard data while ensuring privacy and security, federated learning (FL) is emerging as a promising technology for supporting a wide range of vehicular applications. Although FL has great potential to improve the vehicular edge intelligence(VEI), challenges arise due to vehicle mobility, wireless channel instability, and data heterogeneity. To mitigate the issue of heterogeneous data across vehicles in FL, artificial intelligence-generated content (AIGC) can be employed as an innovative data synthesis technique to enhance FL model performance. In this paper, we propose AIGCassisted Federated Learning for Vehicular Edge Intelligence (GenFV). We further propose a weighted policy using the Earth Mover's Distance (EMD) to measure data distribution heterogeneity and introduce a convergence analysis for GenFV. Subsequently, we analyze system delay and formulate a mixedinteger nonlinear programming (MINLP) problem to minimize system delay. To solve this MINLP NP-hard problem, we propose a two-scale algorithm. At large communication scale, we implement label sharing and vehicle selection based on mobility and data heterogeneity. At the small computation scale, we optimally allocate bandwidth, transmission power and amount of generated data. Extensive experiments show that GenFV significantly improves the performance and robustness of FL in dynamic, resource-constrained environments, outperforming other schemes and confirming the effectiveness of our approach.

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.

Due to its advantage of protecting data privacy and reducing communication overhead, Federated Learning (FL) is becoming a promising machine learning paradigm. However, resource limitations and unstable communication connections on the participating client end can lead to unintentional failures that degrade FL performance. Moreover, as FL systems scale and interconnect increasingly, they face growing exposure to intentional network risks. Furthermore, the assumption of continued trust in historically benign clients introduces vulnerabilities to potential internal betrayal within FL systems. In this paper, we enhance the robustness of FL by incorporating the zero-trust principle, which eliminates implicit trust in clients and mitigates unintentional failures, intentional attacks, and strategic betrayal risks. The framework incorporates dynamic client selection and aggregation weight allocation through trustworthiness evaluation and sustained skepticism toward each potential betrayal behavior. Specifically, a Dirichlet-based trust evaluation technique is presented to update clients' trustworthiness with evolving observations. Then, to reduce potential betrayal loss, we formulate a min-max optimization problem that minimizes the worst-case betrayal loss. Next, we transform the formulation into a convex programming problem for solution. Extensive simulations are conducted to demonstrate the efficacy of the zero-trust based FL in the accurate trust assessment and the system's betrayal-aware robustness enhancement.

In this paper, we investigate the spectrum sharing and resource allocation scheme for air-ground integrated wireless network which consists of multiple unmanned aerial vehicles (UAVs) and a high altitude platform (HAP). We consider the UAVs are required to provide services or execute certain missions in the area that HAP owns the spectrum and other resources. Correspondingly, we propose an energy efficient spectrum sharing and resource allocation scheme so that the UAVs can flexibly utilize the radio resources within the area without degrading the quality of service (QoS) of the HAP. In the proposed scheme, we jointly optimize pricing of spectrum and transmit power to maximize the utility of both the HAP and UAVs in the considered system in an energy efficient manner. A game theoretic approach is then presented to find the spectrum sharing and resource allocation strategies for both HAP and UAVs and the problem has been addressed via convex optimization. Our extensive simulations demonstrate marked improvements in system utility, spectrum and energy efficiency, and also highlight the effectiveness of the proposed scheme.

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. In the IoMT, federated learning (FL) can be applied, which can increase the utilization of patient data while protecting patient privacy. This work proposes a cutting-edge framework that combines differential privacy (DP) with FL and utilizes game theory to optimize power allocation and client selection in IoMT environments. Utilizing a Stackelberg game model, we orchestrate power allocation strategies among IoMT devices to enhance communication efficiency while meeting stringent privacy standards. We propose non-uniform and uniform pricing strategies based on the availability of network state information. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. In addition, our approach integrates DP to protect patients’ data, carefully balancing between privacy and the accuracy of the learning model. The conducted simulations show that our proposed method is effective in terms of communication efficiency, privacy preservation and FL performance.

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 the edge computing system, split learning (SL) is an emerging distributed learning approach that allows mobile users (MUs) and edge nodes (ENs) to train the model together without sharing the raw data of the MU. Although MU can preserve its privacy in SL, attacks on the intermediate data at the cut layer for model training can still lead to privacy leakage, which prevents privacy-sensitive MUs from participating in training. Therefore, it is important to implement an effective incentive mechanism to motivate MUs to join SL while preserving their privacy. In this work, from the perspective of maximizing the utility of edge service provider (ESP) while considering the privacy-sensitivity of different MUs, the incentive problem of ESP and MUs is transformed into the utility optimization problem, and an incentive mechanism based on the differential privacy (DP) and contract theory is established to model the interactions between the MUs and EN. The obtained convex optimization problem is obtained through the mathematical derivations, and the optimal contract is presented by solving this problem. The simulation results demonstrate the effectiveness of our proposed privacy preserved incentive mechanism.

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model’s robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients’ trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system’s betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model’s robustness to betrayal.

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 wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the Network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2 % to 99.1 % with an additional power consumption of 30%.

Using Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing services to ground users has received growing research interest in recent years. The dynamic deployment of UAVs represents a significant research direction within UAV network studies. This paper introduces a highly adaptable UAV wireless network that accounts for the mobility of UAVs and users, the variability in their states, and the tunable transmission power of UAVs. The objective is to maximize energy efficiency while ensuring the minimum number of unserved online users. This dual objective is achieved by jointly optimizing the states, transmission powers, and movement strategies of UAVs. To address the variable state challenges posed by the dynamic environment, user and UAV data is encapsulated within a multi-channel map. A Convolutional Neural Network (CNN) then processes this map to extract key features. The deployment and power control strategy are determined by an agent trained by the Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed strategy in enhancing energy efficiency and reducing the number of unserved online users.

Recently, federated learning (FL) has been threat-ened by the gradient inversion attack that infers user-private data from shared gradients. To cope with this problem, the differential privacy (DP) technique is widely employed in FL. However, when FL faces the non-independent identically distributed (non-IID) data scenarios, applying DP to protect user data privacy remains inefficient in terms of model accuracy and communication costs. In this paper, inspired by the Mixup data augmentation method, we propose a privacy-preserving FL approach called Fed2VAEs to address this problem. Specifically, we introduce a Mixup Module consisting of two variational autoencoders to remove the private information of user data. To balance the trade-off between data privacy and data utility, from the perspective of mutual information, a learning objective is proposed. We conduct extensive experiments under different non-IID data settings, and the experimental results show that Fed2VAEs can significantly reduce the communication cost and improve model accuracy (up to 8.57%) on the premise of successfully protecting user data privacy.

In the edge computing system, federated learning (FL) is a distributed machine learning approach designed to allow multiple participants (e.g., mobile devices, edge nodes, or organizations) to collaborate on training machine learning models without sharing raw data. However, when training FL over wireless networks, mobile users (MU) need to transmit local model parameters over the wireless channel, which introduces training and transmission overheads and results in not enough MUs willing to participate in FL. In order to improve the performance of FL, it is necessary to introduce an appropriate incentive mechanism to encourage MUs to participate in the FL training task. In this paper, we adopt contract theory to design an effective incentive mechanism to motivate MUs to join FL. Edge computing base station (BS), maximize their own utility by signing contracts with MUs regarding contributions and rewards of the MU. We model the utility maximization of BS as a problem of solving the maximum value of a concave function and successfully find the optimal solution with the help of Lagrangian dyadic method. The simulation results show the proposed optimal contract satisfies individual rationality (IR) and incentive compatibility (IC), and it can also effectively improve the accuracy of FL, and obtain a high-quality global model with a fast speed.

Routing optimization within a low Earth orbit (LEO) satellite broadband network (LSBN) has seen advancements through deep reinforcement learning (DRL) approaches in academia. Nonetheless, a crucial aspect often overlooked in these approaches pertains to the inference time of deep neural network (DNN) models during the routing of packets. Our investigation reveals that this oversight can significantly impair routing throughput in LSBN. In response, this paper innovatively proposes a decentralized flow-centric DRL approach, shifting the focus from routing individual packets to entire traffic flows. To align with the large-scale feature of LSBN, we embrace a fully-distributed architecture for flow-centric routing, which is modeled as a partially observable Markov decision process. In this construct, each satellite operates as an independent agent, locally classifying flows following a tailor-designed definition, and is responsible for forwarding a flow to an adjacent satellite based on its internal policy. Notably, the DNN inference is conducted only once on each agent to determine the route for the initial packet of a specific flow; subsequent packets are directed along the same route. Recognizing the potential impact of dynamic LSBN topologies on routing performance, we also introduce an adaptive flow routing update scheme. This scheme is completely free from LSBN environment modelling and aims to bolster the efficacy of the flow-centric approach. Comparative experiments showcase the superiority of the proposed approach over baseline algorithms across various metrics. Consequently, the flow-centric DRL approach can enable high-throughput traffic transmission for LSBN.

Integrating multiaccess edge computing (MEC) with the Internet of Things (IoT) is able to provide IoT sufficient computational resources in addition to its capabilities of sensing and communication. In this article, given the limited computational and energy resources, IoT devices (IDs) are allowed to offload computational tasks to MEC servers for execution. However, as the number of IDs increases dramatically, jointly optimizing the usage of sensing, communication, and computational resources becomes challenging due to the exponential growth in interactions among the IDs. In this article, we address the energy-efficient joint optimization problem for sensing and computation in the MEC-assisted IoT system, aiming to ensure the freshness of the status update and minimize the energy consumption of IDs. To reduce the computation complexity, we introduce the concept of the general mean-field N-player Markov game (GMFG), and reformulate it as a mean-field game (MFG) with teams, leveraging the network structure of states. Considering the advantages of reinforcement learning (RL) for solving dynamic problems, we propose an MFG-based actor-critic algorithm (MFGAC) to minimize the long-term average system cost. Through extensive simulations, we demonstrate that the proposed method is effective and can outperform other schemes under different scenarios.

Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) systems have emerged as a promising technology with the capability to expand terrestrial networks. UAVs, working as edge computing nodes and mobile base stations, can be deployed closer to user equipment (UEs). However, with the rapid increase of UEs, the scarcity of spectrum resources and computing resources has become a critical challenge for future mobile communication systems. Additionally, the inherent characteristics of wireless transmission and untrusted broadcasting pose significant security and privacy concerns for multi-UAV networks. To address these issues, this paper presents a blockchain-based resource trading mechanism (BRTM) and a double auction-based resource trading algorithm (DARA) for multi-UAV edge computing systems. It combines blockchain technology with double auction theory to ensure the security and fairness of resource trading. The relations between UEs and UAVs as a two-stage Stackelberg game is formulated and a pricing-based incentive strategy is proposed. The proposed scheme encourages active participation from both UEs and UAVs while maximizing the sum of their utilities. The security assessment and numerical outcomes show that the proposed method is effective and outperforms other benchmark schemes.

Nowadays, vehicular networks have emerged as a boosting technology to enhance traffic efficiency and safety within transportation systems. As the amount of onboard data increases and data privacy concerns grow, federated learning (FL) has gained popularity for harnessing the data for intelligent transportation operations. To satisfy the strict latency criteria in vehicular networks, a quantization scheme is employed within FL to reduce the size of local models before uplink transmission. In this paper, considering the nature of vehicles’ high mobility, we aim to optimize both the learning performance and latency simultaneously by jointly considering the communication resource budget and quantization strategies. Specifically, we first analyze the convergence performance of the quantized FL, which demonstrates the effects of both quantization error and the number of clients on the convergence rate. Then, we formulate a multi-objective optimization problem (MOP) to maximize the number of participating clients and minimize the overall latency, by jointly optimizing the quantization level, wireless resource allocation and client selection. To deal with the MOP, we decompose the MOP into a set of scalar optimization subproblems, each formulated as a Markov Decision Process (MDP). To solve the MDP in high-mobile vehicular networks, we propose a novel deep reinforcement learning-based vehicle heterogeneous quantization FL (DRL-VQFL) method, which leverages a DRL framework built upon the proximal policy optimization algorithm. Then, a parameter transfer strategy is employed to solve the neighboring subproblems efficiently. Our extensive simulations demonstrate the effectiveness and efficiency of the DRL-VQFL approach, showcasing its superiority over other benchmark methods.

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.

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance.

In this paper, we propose a novel age-of-information (AoI)-aware waveform design scheme for the cooperative joint radar-communications (JRC) system, called AoI-aware Online Prediction (A-OnP) scheme. To be specific, we optimize the power allocation of the orthogonal frequency division multiplexing (OFDM) signal. We aim to maximize the radar mutual information (RMI) with considering the communication data rate (CDR) and AoI performance. Specifically, we design a cognitive operating framework for the JRC system, with a particular emphasis on the closed-loop signal processing for online prediction of the radar target scattering coefficient (TSC). Then, considering the obtained TSC prediction result and corresponding communication performance requirement, we optimize the power allocation of the transmit waveform and the signal-to-interference-plus-noise ratio (SINR) threshold of the communication users. Accordingly, we propose a constraints-splitting coordinate descent (CS-CD) method to solve the formulated non-convex problem by strategically splitting the sum-constraints and assign a quota to each channel, where the allocation criteria is automatically decided during iteration. Simulation results demonstrate that, the cooperative radar-centric communication-constrained (RC-CC) waveform outperforms the separately optimized radar-optimal plus communication-optimal (RO-CO) waveform. Additionally, the A-OnP scheme can increase RMI while meeting the communication CDR and AoI requirements.

Unmanned aerial vehicle (UAV)-assisted wireless communication systems are able to provide high-quality services and ubiquitous connectivity for massive Internet of Things (IoT) devices. In this paper, we study the Age of Information (AoI) and energy tradeoff in a system where an employed UAV performs data collection for multiple IoT nodes (INs). Bearing in mind the importance of AoI and energy consumption during the data collection process, we present a multi-objective optimization problem to minimize the AoI and UAV energy consumption. To explore the tradeoff between AoI and energy consumption, we jointly optimize the collection time, the UAV trajectory, and the duration of time slots. Due to the non-convexity of the formulated problem, we divide the main problem into three sub-problems and address them by leveraging successive convex approximation (SCA) and Lagrangian dual methods. Finally, we design a multi-variable fixed algorithm to iteratively solve the three sub-problems. Simulations are carried out to investigate the tradeoff between AoI and UAV energy consumption, revealing that reducing AoI and energy consumption simultaneously is unattainable. Furthermore, the convergence and validity of the proposed algorithm are presented and analyzed.

Unmanned aerial vehicles (UAVs) have emerged as pivotal roles within internet of vehicles (IoV), serving as mobile base stations. However, while expanding coverage and improving mobility, the deployment of UAVs also poses a threat to the integrity and privacy of sensitive data due to open wireless communication channels in IoV. Therefore, preventing unauthorized access and data tampering is critically important between UAVs and vehicles. For the authenticity and legitimacy of the UAV certificate, existing authentication approaches may lead to significant challenges in key management overhead or dependence on a trusted third party. In this paper, a blockchain-based authentication scheme for UAV-assisted IoV system (BASUV) is proposed. This solution enables dependable UAV registration and authentication services, and permits the dynamic addition and removal. Specifically, blockchain is introduced to achieve the decentralized management and distributed trust of the UAV certificate ledger. Furthermore, to prevent information tampering and identity deception, we design CMPES, a novel combined scheme based on multiple public key generators (PKGs) for encryption and signature. Identical key pair in encryption and signature can reduce key generation and management overhead. The security and experimental analysis demonstrates the effectiveness and efficiency of the proposed scheme.

Future vehicle localization will be committed to improving the positioning accuracy and energy efficiency of localization systems in the intelligent transportation. Recently, reconfigurable intelligent surface (RIS) as an emerging technology has gained widespread attention and is favorable to enhance the performance of vehicle localization systems because of its capacity of customizing the wireless channel. In this paper, in order to minimize the transmit power, we consider the joint active and passive beamforming problem of RIS-assisted vehicle localization system under the constraints of the localization accuracy and the phase shift parameters of the RIS. Specifically, we establish the model of RIS-assisted vehicle localization system and derive the Cramér-Rao bound (CRB) as the localization performance metric. Next, for the scenario of single vehicle localization, we derive the optimal RISs’ phases, and obtain the optimal solution for joint active and passive beamforming based on semidefinite programming relaxation of the non-convex beamforming problem and the corresponding equivalent analysis. Lastly, aimming to the scenario of multiple vehicles localization, we transform the nonconvex joint active and passive beamforming problem into semidefinite programming (SDP) and geometric programming (GP) form subproblems through alternating optimization. Simulation results verify the feasibility of the proposed methods.

This paper considers a reconfigurable intelligent surface (RIS)-aided multi-user simultaneous localization and communication (SLAC) system with statistical position uncertainty, where an RIS is deployed to simultaneously enhance the quality of service. To this end, we first derive the closed-form Cramér-Rao lower bound concerning position parameters as the localization metric and also provide the achievable rate metric for communication services. Then, the joint robust design of subcarrier groups, beamforming vectors, and the phase-shift matrix of the RIS is formulated as a stochastic bi-objective optimization problem to maximize expected localization and communication metrics. Due to the nonlinearity of the multi-objective function and the coupling between optimizing variables, the resulting problem is highly non-convex. Accordingly, we transform the expected achievable rate into an analytical form and further develop a novel unified successive convex approximation (U-SCA)-based iterative algorithm to obtain a robust resource allocation strategy. In particular, we derive closed-form solutions of beamforming vectors and the phase-shift matrix of RIS to decrease the computational complexity. In addition, we also analyse the convergence of the proposed U-SCA-based algorithm. Simulation results demonstrate the effectiveness of the presented method.

Multi-target tracking (MTT) plays a significant role in intelligent transportation systems, serving as an enabling technology for applications such as self-driving, surveillance, and navigation. To enhance the MTT performance, the unmanned aerial vehicles (UAVs) have emerged as effective assistants to MIMO radar system, due to their advantages of high flexibility, controllable deployment and cost-effectiveness. Towards this end, this work investigates a multi-UAV-enabled MIMO radar system, in which each UAV is equipped with a MIMO radar unit and dispatched to track multiple targets simultaneously. We are interested in the joint trajectory planning and transmit resource optimization (i.e. radar waveform optimization and transmit power allocation) to minimize the system power consumption, subject to constraints related to UAVs motion, system resources, and tracking accuracy. Specifically, the posterior Cramér-Rao Lower Bound (PCRLB) is derived and employed as a guideline for the joint optimization. Given the non-convex and inter-variable coupling nature of the formulated problem, we decompose it into three sub-problems and design an alternating optimization method. Firstly, for the UAVs trajectory planning, we obtain sub-optimal results leveraging the successive convex approximation (SCA)-based algorithm. Next, we present a feasible solution set for radar waveform optimization. For transmit power allocation, we perform a convex transformation and find the numerical solution. In addition, through introducing the Lagrange dual method, we further obtain the optimal analytical solution. Finally, simulation results demonstrate the effectiveness and advantages of the developed strategy.

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

To exploit the massive amounts of onboard data in vehicular networks while protecting data privacy and security, federated learning (FL) is regarded as a promising technology to support enormous vehicular applications. Despite that FL has great potential to improve the architecture of intelligent vehicular networks, the mobility of the vehicles and the dynamic nature of wireless channels make the integration of FL and vehicular networks more challenging. In this paper, we propose a vehicle mobility- and channel dynamic-aware FL (MADCA-FL) scheme to fit vehicular networks and enhance learning performances. This novel scheme enables the RSU to select appropriate vehicles and weightedly average the local models. Afterward, MADCA-FL formulates a problem to maximize the model accuracy while assuring the latency and energy restrictions, by jointly optimizing the computation and communication resources. With a mixed- integer non-linear programming structure, the problem is NP-hard. Firstly, we utilize the successive convex approximation algorithm to handle the non-convexity, and then apply the Lagrange multiplier method and the block coordinate descent method to obtain the optimal solution. Extensive experiments are conducted to confirm the effectiveness of our proposed scheme.

Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced the development of smart healthcare systems. Mobile edge computing (MEC)-assisted federated learning (FL) has emerged as a promising technology for providing fast, efficient, and reliable healthcare services while ensuring patient privacy. However, concerns about the privacy and security of sensitive information often make patients hesitant to share their data. Moreover, MEC servers face challenges accessing the necessary radio resources for data transmission. To address these issues, designing an effective incentive mechanism that encourages healthcare user participation in FL and facilitates resource provision from the base station (BS) is vital. This work proposes an efficient and privacy-preserving incentive scheme that considers the interaction among the BS, MEC servers, and MEC users in the MEC-assisted FL healthcare system. Utilizing the Stackelberg game model, we investigate the allocation of transmit power, determination of differential privacy (DP) budgets for MEC users, reward strategies, radio resource demands for MEC servers, and pricing for radio resources at the BS. Furthermore, we analyze the Stackelberg equilibrium and empirically validate the effectiveness of our proposed scheme using a real-world medical dataset.

Digital twin (DT) technology enables the replica of physical objects and environmental statuses of a physical system, which can be used for further simulation, analysis, and prediction. By combining DT with mobile edge computing (MEC), a new paradigm called digital twin edge networks (DITEN) is able to fill the gap between physical edge networks and digital systems and provide novel services to physical devices. Due to possible failure in data sensing, balancing sensing time and successful sensing rate needs to be considered to ensure the freshness of collected data in DITEN. Additionally, an optimal data scheduling policy is necessary to ensure efficient communication between physical devices and the edge server while maintaining the accuracy of DT. Therefore, this paper proposes a joint optimization problem for the sensing and communication for DITEN. Due to the nonconvex nature of the formulated problem, we decompose the original problem into three subproblems, and an iterative optimization algorithm is proposed to minimize the system overhead of DNT realization. The effectiveness of the proposed method is evaluated through extensive simulations.

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform power allocation approach in the spectrum-sharing dual-functional radar-communication (DFRC) systems, with a particular emphasis on improving radar recognition performance while considering the specific communication requirements of individual users. Specifically, the radar mutual information (RMI) is maximized in terms of allocating the radar power to the OFDM subcarrier within the limits of the power constraints and meeting the age of information (AoI) expectation requirements. By considering the impact of radar interference, we measure the AoI performance of individual users using the transmission outage probability. Then, an iterative constraints-splitting (ICS) method is developed to find the optimal radar power allocation results from the formulated non-convex problem by transforming it into an equivalent convex problem using an iteratively determined factor. Simulation results demonstrate that the proposed individual user AoI preference-based approach can improve RMI performance while meeting the AoI communication requirements of individual users. Additionally, higher RMI and more stable AoI performance can be achieved while maintaining total communication performance by allocating more sub-carriers to fewer users.

In this paper, a novel reconfigurable intelligent surfaces (RIS)-assisted joint localization and communication (JLAC) scheme is presented to supply both position-sensing and data transmission functions for a multi-user system by a frequency division strategy. In particular, considering the parameter uncertainty, we formulate the robust resource design problem as a statistical mixed-integer form, aiming to maximize localization and communication performance by joint subcarrier group, beamforming, and phase-shift optimization. To tackle the formulated non-convex problem efficiently, we develop an iterative method based on the stochastic successive convex approximation technology to handle the original problem. Simulation studies are presented to demonstrate the effectiveness of the proposed JLAC scheme and method.

Vehicular edge computing (VEC) has been introduced to bring powerful in-proximity computing solutions to vehicles. VEC is able to boost the development of vehicular networks by handling computing tasks and accommodating artificial intelligence (AI). To fulfill the requirements of low latency and security in realizing AI for vehicular networks, and fully utilize the vehicles' capabilities on sensing and computing, federated learning (FL) in VEC emerges as a potential solution. However, privacy protection, data security and information asymmetry issues pose challenges on efficiently and securely motivating more vehicles to participate in FL. Thus, this paper proposes a contract-based incentive mechanism for blockchain-enabled FL, which employs contract theory to establish an optimal contract design between VEC servers and vehicles. We present the necessary and sufficient conditions to obtain an optimal contract and analyze the simplification of the constraints. The simulation results show that our proposed method is effective in providing incentives and outperforms other benchmark schemes.

In this paper, we propose an energy efficiency (EE) optimization scheme for data collection in high altitude platform (HAP)-assisted unmanned aerial vehicle (UAV) network. The HAP acts as the aerial base station (ABS) to assist UAV and sensing devices (SDs) establishing direct uplink data transmission, i.e., device-to-UAV (D2U) communication. Our goal is to operate the UAV in an energy-efficient manner for cyclic data collection by dynamically clustering the target area and optimizing sink SD selection and transmit power in D2U communications. An optimization problem is formulated that maximizes the throughput-energy utility. Aiming at addressing the formulated problem, we propose a dynamic clustering algorithm based on Affinity Propagation (AP) to determine sink SDs for D2U communications, and a centralized reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to obtain the optimal UAV trajectory and sink SDs' transmit power. Simulation results demonstrate that the proposed scheme has advantages in EE compared with other schemes.

In mobile edge computing (MEC)-assisted federated learning (FL), the MEC users can train data locally and send the results to the MEC server to update the global model. However, the implementation of FL may be prevented by the selfish nature of MEC users, as they need to contribute considerable data and computing resources while scarifying certain data privacy for the FL process. Therefore, it is of great importance to design an efficient incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission and data privacy, we design an energy-efficient and privacy-preserved incentive scheme to facilitate the FL process by investigating interactions between the MEC server and MEC users in a MEC-assisted FL system. Using a Stackelberg game model, we explore the transmit power allocation and privacy budget determination of MEC users and reward strategy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results demonstrate the effectiveness of our proposed scheme.

In this paper, we consider an Internet of Things (IoT) system where the employed unmanned aerial vehicle (UAV) carries edge computing server to perform data collection and execution for multiple IoT nodes (INs). For such a network, UAV trajectory and uplink transmission power optimization are integrated to minimize the Age of Information (AoI) of the results returned to all ground INs subject to energy consumption limitations. Due to the non-convex nature of the formulated problem, it is divided into two subproblems, which are respectively solved by Lagrangian dual and convex optimization methods, and block coordinate descent method is applied to solve the overall problem. Simulation results show that the proposed algorithm achieves the lowest average AoI of all INs compared with other schemes. The results also reveal the relationship between the average AoI and the number of INs and advantages of the proposed scheme.

Wireless power transfer (WPT) via resonant laser beam from a spatially distributed laser resonator not only benefits the instinct safety but also has important potential of adaptive operation without positioning and aiming when retroreflectors are used to enable the alignment-free operation of the laser. Despite the intense theoretical modeling and experimental efforts with different laser schemes, the adaptive resonant beam WPT across large dynamic range, including both working distance and Field of View (FoV), has never been demonstrated, due to the absence of true alignment-free laser. In this work, we summarized the requirements on the dynamic range and discussed the optimization criteria of dynamic range and laser safety, including the telescope in the laser resonator, the resonator stability, coupled-resonator scheme, and influences of the optical aberrations. The analysis indicated that loss induced by optical aberrations is the main issue limits the dynamic range. In the experiment, alignment-free laser with large dynamic range for WPT application was demonstrated with improved resonator design and optical design to compensate the aberrations. Efficient multiwatt optical output was obtained across a working distance of 1–5 m, a receiver FoV of ±30°, and a transmitter FoV of 4.6°; and over 1.3-W electrical output was obtained via an InGaAs photovoltaic cell, with dc–dc WPT efficiency being 4%. Our work proved the feasibility of large-dynamic-range alignment-free laser with distributed resonator, which paves the way for practical resonant beam charging and communication applications based on it for the Internet of Things devices.

Benefiting from the deployment flexibility and the line-of-sight (LoS) channel conditions, unmanned aerial vehicle (UAV) has gained tremendous attention in data collection for wireless sensor networks. However, the high-quality air-ground channels also pose significant threats to the security of UAV-aided wireless networks. In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the remote ground base station (BS). First, during the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective of maximizing the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate of transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

During the past decade, Industry 4.0 has greatly promoted the improvement of industrial productivity by introducing advanced communication and network technologies in the manufacturing process. With the continuous emergence of new communication technologies and networking facilities, especially the rapid evolution of cellular networks for 5G and beyond, the requirements for smarter, more reliable, and more efficient cellular network services have been raised from the Industry 5.0 blueprint. To meet these increasingly challenging requirements, proactive and effective allocation of cellular network resources becomes essential. As an integral part of the cellular network resource management system, cellular traffic prediction faces severe challenges with stringent requirements for accuracy and reliability. One of the most critical problems is how to improve the prediction performance by jointly exploring the spatial and temporal information within the cellular traffic data. A promising solution to this problem is provided by graph neural networks (GNNs), which can jointly leverage the cellular traffic in the temporal domain and the physical or logical topology of cellular networks in the spatial domain to make accurate predictions. In this article, we present the spatial-temporal analysis of a real-world cellular network traffic dataset and review the state-of-the-art research works in this field. Based on this, we further propose a time-series similarity-based graph attention network, TSGAN, for the spatial-temporal cellular traffic prediction. The simulation results show that our proposed TSGAN outperforms three classic prediction models based on GNNs or GRU on a real-world cellular network dataset in short-term, mid-term, and long-term prediction scenarios.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution. Since status updates generated by insufficient sensing operations may be invalid and lead to additional processing time, a joint data sensing and processing optimization problem needs to be considered. Therefore, this work formulates an NP-hard problem that considers the freshness of the status updates and energy consumption of the IoT devices. Subsequently, the problem is decomposed into sampling, sensing, and computation offloading optimization problems. To optimize the system overhead, a multi-variable iterative system cost minimization algorithm is proposed. Simulation results illustrate the efficacy of our method in decreasing the system cost, and indicate the influence of sensing and processing under different scenarios.

In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the base station (BS). First, for the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective to maximize the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As the most widely-used FL framework,Jederated averaging (FedAvg) suffers an expensive communication cost especially when there are large amounts of devices involving the FL process. Moreover, when considering asynchronous FL, the slowest device becomes the bottleneck for the cask effect and determines the overall latency. In this work, we propose a communication-efficient federated learning framework with partial model aggregation (CE-FedPA) algorithm to utilize compression strategy and weighted device selection, which can significantly reduce the size of uploaded data and decrease the communication time. We perform a series of experiments on the MNIST/CIFAR-10 datasets, in both lID and non-lID data settings. We compare the communication time of different aggregation schemes, in terms of iteration rounds and target accuracy. Simulation results demonstrate that the uploading time of the proposed scheme is up to 4.3 times shorter than other existing ones. Experiments on an end - to-end FL framework also verify the communication efficiency of CE-FedPA in a real-world setting.

Federated learning (FL) is a type of distributed machine learning in which mobile users can train data locally and send the results to the FL server to update the global model. However, the implementation of FL may be prevented by the self-fish nature of mobile users, as they need to contribute considerable data and computing resources for participating in the FL process. Therefore, it is of importance to design the incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission, we design an incentive scheme to facilitate the FL process by investigating interactions between the multi-access edge computing (MEC) server and mobile users in a MEC-based FL system. By using a two-stage Stackelberg game model, we explore the transmission power allocation of the users and reward policy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results show that our model is effective for different parameter settings and the utility of the MEC server can be increased significantly compared to the baseline.

For unmanned aerial vehicle (UAV)-assisted wireless networks, the continuous trajectory designs generally suffer from infinite number of variables of the continuous UAV trajectory. In this paper, to avoid unexpected trajectory approximation and overcome the difficulty in obtaining an accurate continuous trajectory, we aim at characterizing an analytical optimal solution for jointly designing the resource allocation and continuous UAV trajectory. We focus on a scenario with UAV at a fixed altitude being deployed to assist the wireless communication with a ground user. With limited energy accessible for the wireless transmissions, we construct a throughput maximization problem for jointly optimizing the continuous transmit power and the UAV's continuous trajectory. Via duality analysis, we obtain the features of the optimal power control and successfully convert the dual problem to a series of pure trajectory design problems, which can be optimally addressed based on a mechanical equivalence approach. Afterwards, we accordingly propose an algorithm for optimally solving the dual problem, from which the optimal joint solution can be analytically constructed in a closed form. Finally, we also verify our proposed algorithm and confirm the optimality of the obtained solution via simulations.

Joint localization and communication systems have drawn significant attention due to their high resource utilization. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided simultaneously localization and communication system. We first determine the sum squared position error bound (SPEB) as the localization accuracy metric for the presented localization-communication system. Then, a joint RIS discrete phase shifts design and subcarrier assignment problem is formulated to minimize the SPEB while guaranteeing each user’s achievable data rate requirement. For the presented non-convex mixed-integer problem, we propose an iterative algorithm to obtain a suboptimal solution by utilizing the Lagrange duality as well as penalty-based optimization methods. Simulation results are provided to validate the performance of the proposed algorithm.

The unmanned aerial vehicles (UAVs) have been widely used in civilian environments, due to its high flexibility, low cost and ease of deployment. In this paper, an UAV-enabled mobile edge computing (MEC) system is studied, in which the UAV serves as an aerial mobile base station to provide services for a group of ground user equipments (UEs) with computation task requests. We jointly optimize the time allocation, resource allocation and the UAV flying trajectory to minimize the time required for the UAV to complete the task, subject to the constraints of different kinds of resources, energy and velocity. Due to the non-convexity of the formulated problem, we first transform it to a feasibility check problem and then divide it onto three convex optimization subproblems. By using the block coordinate descent method and the successive convex approximate (SCA) method, we propose an efficient iterative algorithm to solve the three subproblems alternately with ensured convergence. Extensive simulation results show that the proposed joint optimization algorithm reduces the task completion time compared with other schemes.

Owing to the unique advantages of low cost and controllability, reconfigurable intelligent surface (RIS) is a promising candidate to address the blockage issue in millimeter wave (mmWave) communication systems, consequently has captured widespread attention in recent years. However, the joint active beamforming and passive beamforming design is an arduous task due to the high computational complexity and the dynamic changes of wireless environment. In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm to jointly design active hybrid beamformer at the base station (BS) side and passive beamformer at the RIS side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a maximum entropy based DRL algorithm, which can explore more stochastic policies than deterministic policy, to design active analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method. The experimental results demonstrate that our proposed SAC algorithm can achieve better performance compared with conventional optimization algorithm and DRL algorithm.

Reconfigurable intelligent surface (RIS) is considered as an extraordinarily promising technology to solve the blockage problem of millimeter wave (mmWave) communications owing to its capable of establishing a reconfigurable wireless propagation. In this paper, we focus on a RIS-assisted mmWave communication network consisting of multiple base stations (BSs) serving a set of user equipments (UEs). Considering the BS-RIS-UE association problem which determines that the RIS should assist which BS and UEs, we joint optimize BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate of the system. To solve this intractable non-convex problem, we propose a soft actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming and BS-RIS-UE association design algorithm, which can learn the best policy by interacting with the environment using less prior information and avoid falling into the local optimal solution by incorporating with the maximization of policy information entropy. The simulation results demonstrate that the proposed SAC-DRL algorithm can achieve significant performance gains compared with benchmark schemes.

This article considers an unmanned aerial vehicles (UAVs) communication network, where UAV operate as an aerial mobile relay between a source and a destination of the network. To capture the “timeliness” of the received information at the destination node, a new performance metric named Age of Information (AoI) is considered. In addition, a short packet communication scheme maintains low latency in the proposed UAV wireless communication system. The finite block-length theory investigates the performances of the short packet communications scheme in the UAV-assisted wireless communications system. In this article, the Average AoI (AAoI) is estimated by applying the stochastic hybrid systems (SHSs) model. The SHS model comprises discrete states represents events that reset the AoI process and continuously dynamics that represent linearly growing age processes. Finally, a closed-form expression for the AAoI of the proposed UAV wireless communication system is derived and it can be used to estimate the optimal altitude, block length, and other parameters.

With the increasing popularity of the smart grid, huge volumes of data are gathered from numerous sensors. How to classify, store, and analyze massive data sets to facilitate the development of the smart grid has recently attracted much attention. In particular, with the popularity of household smart meters and electricity monitoring sensors, a large amount of data can be obtained to analyze household electricity usage so as to better diagnose the leakage and theft behaviors, identify man-made tampering and data fraud, and detect powerline loss. In this article, the time window method is first proposed to obtain the features and potential periodicity of household electricity data. Combining the denoising ability of the autoencoder and the induction ability of the feedforward neural network, a multilayer hierarchical network (MLHN) is then established to detect anomalies in single sensor data and classify multiple groups of sensor data, respectively. The experimental results show that the accuracy of detecting abnormal data and data classification is significantly improved compared with the presented scheme.

Dueto its distributed characteristics, the development and deployment of the blockchain framework are able to provide feasible solutions for a wide range of Internet of Things (IoT) applications. While the IoT devices are usually resource-limited, how to make sure the acquisition of computational resources and participation of the devices will be the driving force to realize blockchain at the network edge. In this article, an edge computing-based blockchain framework is considered, where multiple edge service providers (ESPs) can provide computational resources to the devices for mining. We mainly focus on investigating the trading between the devices and ESPs in the computational resource market, where ESPs act as the sellers and devices act as the buyers. Accordingly, a sequential game model is formulated and by exploring the sequential Nash equilibrium (SE), the existence of the optimal solutions of selling and buying strategies can be proved. Then, a deep Q-network-based algorithm with modified experience replay update method is applied to find the optimal strategies. Through theoretical analysis and simulations, we demonstrate the effectiveness of the proposed incentive mechanism on forming the blockchain via the assistance of edge computing.

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.

In this paper, a novel system model for the non orthogonal multiple access (NOMA) based wireless network is proposed using physical layer network coding (PLNC) scheme. The outage performance of a two pairs hybrid NOMA-PLNC relay network is analyzed. Proposed network is designed with three time slots and half-duplex decode-and-forward (DF) relay. A closed form expression for the end-to-end outage probability of the proposed NOMA-PLNC relay network is derived. It is noted that the performance of the proposed hybrid NOMA-PLNC relay network is significantly improved when compare to the conventional orthogonal multiple access (OMA)-PLNC relay network. Finally, numerical results of the proposed relay network is validated using the simulated experimental results.

In this paper, we propose a UAV-aided covert communication scheme assisted by a multi-antenna jammer to maximize the transmission rate between a ground transmitter and a UAV receiver against several randomly distributed wardens. The transmitter adopts the maximum ratio transmission, while the jammer zero-forces its transmitted signal at the UAV to disturb the monitoring at wardens without interfering the legitimate transmission. First, we analyze the detection performance and derive the optimal threshold for each warden to minimize its detection outage probability (DOP). Then, with the worst situation in which all wardens set their respective optimal thresholds to achieve the minimum global DOP, the location and the transmit power of the jammer are optimized to maximize the DOP. The location of UAV and the transmit power of the ground transmitter are also optimized to maximize the transmission rate with the minimum DOP requirement satisfied. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-aided covert communication scheme.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the warden can move. In this paper, we propose a covert communication scheme against a mobile warden, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the covert outage probability (COP) limit. First, we analyze the monotonicity of the COP to obtain the optimal location the warden can move. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. Under this worst situation, we first maximize the connection probability over the transmit-to-jamming power ratio within the maximum allowed COP for a fixed transmission rate. Then, the Newton's method is applied to maximize the connection throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-s sliding windows. Then, the 2-s interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14% sensitivity, 99.62% specificity and 99.54% accuracy in the segment-based level. From the perspective of the event-based level, 99.31% sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-s latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09% sensitivity, 99.81% specificity and 99.73% accuracy were obtained. In the event-based level, 97.52% sensitivity, 0.07/h FDR and mean 13.2-s latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection.

Abstract Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using $$l_1$$ l 1 -norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP with the proximal algorithm. The subproblems in the new model are strongly convex in the block coordinate descent (BCD) framework. Therefore, the new sparse NCP provides a full column rank condition and guarantees to converge to a stationary point. In addition, we proposed an inexact BCD scheme for sparse NCP, where each subproblem is updated multiple times to speed up the computation. In order to prove the effectiveness and efficiency of the sparse NCP with the proximal algorithm, we employed two optimization algorithms to solve the model, including inexact alternating nonnegative quadratic programming and inexact hierarchical alternating least squares. We evaluated the proposed sparse NCP methods by experiments on synthetic, real-world, small-scale, and large-scale tensor data. The experimental results demonstrate that our proposed algorithms can efficiently impose sparsity on factor matrices, extract meaningful sparse components, and outperform state-of-the-art methods.

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results.

Fog computing system is able to facilitate computation-intensive applications and emerges as one of the promising technology for realizing the Internet of Things (IoT). By offloading the computational tasks to the fog node (FN) at the network edge, both the service latency and energy consumption can be improved, which is significant for industrial IoT applications. However, the dynamics of computational resource usages in the FN, the radio environment and the energy in the battery of IoT devices make the offloading mechanism design become challenging. Therefore, in this article, we propose a dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands. Specifically, with the objective to minimize the system cost related to latency, energy consumption, and weights of MDs, we propose a joint computation offloading and radio resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, we divide the main problem into several subproblems at each time slot and address them accordingly. Through performance evaluation, the effectiveness of the proposed scheme can be verified.

The papers in this special section focus on the topic of green industrial Internet of Things (IIoT). These papers aim to consolidate the current state of the art in terms of fundamental research ideas and network engineering, geared toward exploiting greenness of IIoT. The IIoT is a new ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine–human collaboration to improve productivity, efficiency, and reliability. IIoT connects billions of mobile digital devices, manufacturing machines, industrial equipment, etc., and generates an unprecedented volume of industrial data. The gap between the rapidly growing demands of data rate and existing bandwidth-limited network infrastructures has become ever prominent. Moreover, the interaction and connection of things in IIoT will consume substantial energy in contrast with limited energy storage of the things. Therefore, the greenness of IIoT is crucial for the success of IIoT. In particular, with the prevalence of mobile devices, electronic devices, cameras, social networks, social media, etc., ourworld is generating big data and multimedia big data, which further aggregate the energy demand in terms of the data transmission and processing of IIoT.

The last few decades have witnessed an explosive growth of the Internet-of-Things (IoT) systems, which provide ubiquitous sensing and computing services. When adopted in industrial and manufacturing environments, IoT is referred to as the industrial IoT (IIoT), which has attracted increasing research attention. Energy efficiency is one of the most important research topics in green IIoT, as 1) the limited resource can significantly affect the lifetime of IIoT systems and 2) massive sensors, devices, machines keep consuming a considerable amount of energy, and increasing the carbon footprint. In this article, we present a comprehensive survey on energy-efficient communications and computation mechanisms in IIoT systems (such as smart grids). We categorize the existing works, review, discuss, and compare the works to explore their pros and cons. We also discuss the open issues and research challenges, considering the recent 5G communications and edge computing trends.

Low latency is a critical requirement of beyond 5G services. Previously, the aspect of latency has been extensively analyzed in conventional and modern wireless networks. With the rapidly growing research interest in wireless-powered ambient backscatter communications, it has become ever more important to meet the delay constraints, while maximizing the achievable data rate. Therefore, to address the issue of latency in backscatter networks, this paper provides a deep Q-learning based framework for delay constrained ambient backscatter networks. To do so, a Q-learning model for ambient backscatter scenario has been developed. In addition, an algorithm has been proposed that employ deep neural networks to solve the complex Q-network. The simulation results show that the proposed approach not only improves the network performance but also meets the delay constraints for a dense backscatter network.

Blockchain has been gradually applied to different Internet-of-Things platforms. As the efficiency of the blockchain mainly depends on the network computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this article, we focus on investigating incentive mechanism for rational miners to purchase the computational resources. An edge-computing-based blockchain network is considered, where the edge service provider (ESP) can provide computational resources for the miners. Accordingly, we formulate a two-stage Stackelberg game between the miners and ESP. The aim is to investigate SE of the optimal mining strategy under the two different mining schemes, in order to find the optimal incentive for the ESP and miners to choose autofit strategies. Through theoretical analysis and numerical simulations, we can demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the blockchain.

To accommodate the explosively growing demands for mobile traffic service, wireless network virtualization is proposed as the main evolution towards 5G. In this work, a novel contract theoretic incentive mechanism is proposed to study how to manage the resources and provide services to the users in the wireless virtualized networks. We consider that the infrastructure providers (InPs) own the physical networks and the mobile virtual network operator (MVNO) has the service information of the users and needs to lease the physical radio resources for providing services. In particular, we utilize the contract theoretic approach to model the resource trading process between the MVNO and multiple InPs. Two scenarios are considered according to whether the information (such as the radio resource they can provide) of the InPs are globally known. Subsequently, the corresponding optimal contracts regarding the user association and transmit power allocation are derived to maximize the payoff of the MVNOs while maintaining the requirements of the InPs in the trading process. To evaluate the proposed scheme, extensive simulation studies are conducted. It can be observed that the proposed contract theoretic approach can effectively stimulate InPs' participation, improve the payoff of the MVNO, and outperform other schemes.

With the drastic increase of space information network (SIN) traffic and the diversity of network traffic types, the optimal allocation of the scarce network resources is of great significance for optimizing the SIN system capability. In this paper, we propose a multi-resource management method for multi-tier SIN using the cooperative Nash bargaining solution. Since the original problem is a non-convex problem, we firstly make logarithmic transition, and then find a tightest lower bound function to convert the initial problem into a convex one. In order to carry out the optimal bandwidth and power allocation in SIN, we construct a joint bandwidth and power allocation (JBPA) algorithm. Simulation results show the performance improvement of the JBPA scheme and the convergence of JBPA algorithm.

We consider a low-latency communication network operating with finite blocklength (FBL) codes. During the transmission, the minimum mean squared error (MMSE) channel estimation is assumed to be applied to obtain the instantaneous but imperfect Channel State Information (CSI) for the rate selection. We aim at optimizing the FBL throughput of the system under given reliability constraints. First, we provide an optimal frame structure design by optimally allocating the total frame length for MMSE training of channel estimation and data transmission. In addition, we further improve the FBL throughput considering channel dynamics which optimally selects the coding rate per frame. Combining the frame structure and the coding rate selection, a joint optimization problem is studied and solved by a sub-optimal algorithm. In the simulation study, we validate the proposed analytical model and evaluate the FBL throughput of the proposed solution in comparison to benchmark schemes.

Mobile edge computing (MEC) is an important and effective platform to offload the computational services of modern mobile applications, and has gained tremendous attention from various research communities. For delay and resource constrained mobile devices, the important issues include: 1) minimization of the service latency; 2) optimal revenue maximization; and 3) high quality-of-service requirement to offload the computational service offloading. To address the above issues, an adaptive service offloading scheme is designed to provide the maximum revenue and service utilization to MEC. Unlike most of the existing works, we consider both the delay-tolerant and delay-constraint services in order to achieve the optimized service latency and revenue. Furthermore, we consider the different priorities to prioritize the edge services for optimal service offloading. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive service offloading scheme over other existing state-of-the-art solutions, in terms of service latency, utility value, revenue, and utilization.

In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large number of antennas are responsible to provide WPT in the downlink, and the users can recycle and utilize the received energy for uplink data transmission. The role of BS is to execute WPT; thus, there are no data transmissions in the downlink. A time-division protocol is considered to divide the time of downlink WPT and uplink wireless information transfer into separate time slots. With the objective to improve the energy efficiency, we propose the time, subcarrier, and power allocation schemes and antenna selection algorithms. As the perfect channel state information (CSI) is hard to obtain in the practical systems, we also take the case where only estimated CSI is available into consideration when executing resources allocation decisions and analyze the corresponding performance. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem. Then, an efficient alternating direction method of multipliers-based distributed resource allocation algorithm is applied to address the transformed problem. Performance evaluations are conducted to demonstrate the advantages of the proposed schemes.

This paper addresses the energy efficiency issue for content sharing with collaborative mobile clouds (CMC). We start by maximizing the data rate of cellular transmissions under the maximum transmit power constraint of the cellular users, to obtain the optimal beamforming vectors. Using these vectors, we propose a water filling based data segmentation approach for content distribution. Furthermore, within the CMC, we design cost-effective resource allocation and power control mechanisms for device-to-device communications. Through performance comparisons, we disclose that our proposed scheme outperforms some previous study in terms of delay and energy consumption per mobile terminal, which further validates the effectiveness of our design.

Mobile Edge Computing (MEC) is emerging as one of the effective platforms for offloading the resource- and latency-constrained computational services of modern mobile applications. For latency- and resource-constrained mobile devices, the important issues include: 1) minimize end-to-end service latency; 2) minimize service completion time; 3) high quality-of-service (QoS) requirement to offload the complex computational services. To address the above issues, a latency-oblivious distributed task scheduling scheme is designed in this work to maximize the QoS performance and goodput for the MEC services. Unlike most of the existing works, we consider the latency-oblivious property of different services in order to achieve the optimized goodput and service latency. Furthermore, we design an optimal decision engine for efficiently offloading the computational services. Simulation results are presented to demonstrate the effectiveness of the proposed offloading scheme over other existing state-of-the-art solutions, in terms of service latency, goodput, service completion time and fairness.

Energy harvesting relays are predicted to play a pivotal role in large scale networks. This paper evaluates the secrecy performance of a system that employs a two-way decode-and-forward relay assisting transmission between two nodes. The relay has RF energy harvesting capability and it can receive energy from the RF signal and the transmission of the system can be overheard by an eavesdropper. More specifically, we derive an exact expression for the interception probability when the main and wiretap links experience generalized κ-μ fading. The impacts of the power- splitting factor at the relay and the fading parameters on the secrecy performance of the considered system are also assessed. Numerical and simulation results are presented to verify the derived results.

The advancement in vehicles has enabled crowdsensing in vehicular fog computing (VFC), where vehicles are recruited to be assigned different subtasks and participate sensing activities that may disclose their sensitive information. To stimulate more participants, VFC systems should be able to provide reliable and privacy-preserving data transmission and processing mechanisms for the sensing report. To ensure the report process, we present a reliable and privacy- preserving task recomposition (REPTAR) for multiple subtasks sensing in VFC. Modified homomorphic Paillier encryption and superincreasing sequence are employed for aggregating hybrid subtasks into one ciphertext. Reliability is verified by means and variances of each aggregated subtasks from different vehicular fog nodes. Detailed security analysis and performance evaluation are provided to demonstrate the security, privacy-enhancement, efficiency and low complexity of the proposed REPTAR.

In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

In this work, we propose a distributed user association and resource allocation scheme for the wireless virtualized information-centric networks with hybrid energy supply, where the base station (BS) equipped with caching and energy harvesting capabilities to reduce the COPEX and OPEX cost. In particular, with the objective to obtain the utility maximization for the network operators, a joint user association, caching, spectrum and power problem is presented. To tackle the formulated mixed combinatorial and non-convex optimization problem with low complexity, the original problem is divided into two subproblems and we propose an alternating direction method of multipliers (ADMM)-based distributed algorithm to address them efficiently and effectively. Extensive simulation studies demonstrate the advantages of our presented system architecture and proposed schemes.

In this study, an energy‐efficient optimisation scheme for a large‐scale multiple‐antenna system with wireless power transfer (WPT) is presented. In the considered system, the user is charged by a base station with a large number of antennas via downlink WPT and then utilises the received power to carry out uplink data transmission. Novel antenna selection, time allocation and power allocation schemes are presented to optimise the energy efficiency of the overall system. In addition, the authors also consider channel state information cannot be perfectly obtained when designing the resource allocation schemes. The non‐linear fractional programming‐based algorithm is utilised to address the formulated problem. Their proposed schemes are validated by extensive simulations and it shows superior performance over the existing schemes.

The ubiquitous sensor-rich mobile devices (e.g., smartphones, wearable devices, and smart vehicles) have been playing a vital role in the evolution of the Internet of Things (IoT), which bridges the gap between digital and physical spaces. The powerful computing/communication capacities, huge population, and inherent mobility make mobile device networks a much more flexible and cost-effective IoT solution than traditional wireless sensor networks. However, the energy issue of mobile terminals poses significant challenges to the widespread use of IoT: not only the mobile terminals have short lifetime with the proliferation of mobile applications but also the current networking and communication technologies are not adequately taking the energy efficiency into account. Therefore, the sustainable issue of IoT has attracted considerable attention from both academia and industry. Wireless energy harvesting (EH), and transfer technology was recently proposed as an effective mean to address this issue. It enables the mobile terminals to harvest energy from the ambient environment to prolong its battery. Although some forms of EH have been applied to WSNs, networking and communication solutions must be redesigned for wireless powered IoT with massive number of mobile terminals.

Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting (EH) techniques, the system performance could be further improved. In this paper, we take the social relationships of the EH mobile devices (MDs) into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost, we advocate game theoretic approach and propose a dynamic computation offloading scheme designing the offloading process in fog computing system with EH MDs. Different queue models are applied to model the energy cost and delay performance. It can be seen that the proposed problem can be formulated as a generalized Nash equilibrium problem (GNEP) and we can use exponential penalty function method to transform the original GNEP into a classical Nash equilibrium problem and address it with semi-smooth Newton method with Armijo line search. The simulation results demonstrate the effectiveness of the proposed scheme.

Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.

nonPeerReviewed

Wireless network visualization is envisioned as a promising framework to provide efficient and customized services for next-generation wireless networks. In wireless virtualized networks (WVNs), limited radio resources are shared among different service providers for providing services to different users with heterogeneous demands. In this work, we propose a distributed resource allocation scheme for a wireless virtualized small cell networks. The SBSs in the considered system are equipped with self-backhaul and energy harvesting capabilities in order to reduce the operation cost. In particular, with the objective to obtain the utility maximization, a joint user association, time, spectrum and power allocation problem is presented. To tackle the formulated mixed combinatorial and non-convex optimization problem, the original problem is divided into three low-complexity subproblems and we propose an alternating direction method of multipliers (ADMM)-based distributed algorithm to address them efficiently and effectively. Simulation studies are conducted to demonstrate the advantages of our presented system architecture and proposed schemes.

System fairness has been regarded as an important performance index related to qualities of services in mobile networks. Most of researches evaluate the fairness of a cellular system in terms of the cumulative distribution function (CDF) of user throughputs. However, it's difficult to treat the CDF as a parameter to set, adjust and compare. This paper proposes Gini coefficient, which is a primary measure of the inequality of income in economics, can be developed to represent the system fairness in mobile networks. Furthermore, we present a scheme with modified genetic algorithm (GA) to achieve certain level of the system fairness by adjusting the almost blank subframe (ABS) arrangement in LTE-Advanced heterogeneous networks (HetNets). Validations and numerical analysis on the relationship between the system throughput and fairness are performed by computer simulations.

In this paper, we investigate the energy efficient computation offloading scheme in a multi-user fog computing system. We consider the users need to make the decision on whether to offload the tasks to the fog node nearby, based on the energy consumption and delay constraint. In particular, we utilize queuing theory to bring a thorough study on the energy consumption and execution delay of the offloading process. Two queuing models are applied respectively to model the execution processes at the mobile device (MD) and fog node. Based on the theoretical analysis, an energy efficient optimization problem is formulated with the objective to minimize the energy consumption subjects to execution delay constraints. In order to address the formulated problem, an alternating direction method of multipliers (ADMM)-based distributed algorithm is proposed. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over the other existed schemes can be observed.

Device-to-device (D2D) cooperative relay can assist users with inferior channel conditions to implement multi-hop transmissions, improving network coverage and throughput. However, energy efficiency is an important issue to be optimized because of the limited battery capacity of handheld equipments. Considering a two-hop D2D relay communication scenario, this paper proposes a resource management approach that jointly optimizes relay selection, spectrum allocation, and power control, so that the total energy efficiency of D2D links is maximized while guaranteeing the quality of service (QoS) requirements of D2D and cellular links at the same time. Since the formulated joint optimization problem involves a four-dimensional matching that is NP-hard, we propose a pricing-based two-stage matching algorithm to reduce dimensionality and provide a tractable solution. In the first stage, the spectrum resources reused by relay-to-receiver links are determined by a two-dimensional matching. Then, a three- dimensional matching is conducted to match users, relays, and the spectrum resources reused by transmitter-to-relay links. The optimal transmit power is solved during the preference establishment process in the second stage. As shown in simulation results, the proposed algorithm not only performs good on energy efficiency, but also enhances the average number of served users in comparison to the case without any relay.

Mobile-edge cloud computing is a new cloud platform to provide pervasive and agile computation augmenting services for mobile devices (MDs) at anytime and anywhere by endowing ubiquitous radio access networks with computing capabilities. Although offloading computations to the cloud can reduce energy consumption at the MDs, it may also incur a larger execution delay. Usually the MDs have to pay cloud resource they used. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay and price cost of offloading process in a mobile-edge cloud system. Specifically, both wireless transmission and computing capabilities are explicitly and jointly considered when modelling the energy consumption and delay performance. Based on the theoretical analysis, the multi-objective optimization problem is formulated with the joint objectives to minimize the energy consumption, execution delay and price cost by finding the optimal offloading probability and optimal transmission power for each MD. The scalarization scheme and interior point method are applied to address the formulated problem. Through extensive simulations, the effectiveness of the proposed scheme can be demonstrated.

The proof-of-concept software-defined radio access network (RAN) is not flexible enough due to the inherent delay and the necessity of high-capacity fronthaul links. We are hence motivated to propose a hierarchical software-defined RAN architecture, over which the base stations (BSs) are abstracted into multiple virtual local controllers while these local controllers are administered by a high-level controller. Under such a hierarchical network architecture, we particularly investigate in this paper how to adapt the BS transmit power over a long term according to the network dynamics under the constraints of mobile user queue stability and limited fronthaul capacity. We first formulate an off-line stochastic power adaptation problem. Through developing the Lyapunov method, we transform the problem into an approximate on-line optimization task. However, the challenge arises from the introduced per-cluster fronthaul capacity constraint. To solve the task efficiently and avoid extensive information exchange between the high-level controller and the local controllers, we put forward a novel low-complexity algorithm by designing a non- cooperative power adaptation game among the local controllers. Simulations are provided to evaluate the efficacy of the proposed studies.

In this paper, we investigate the energy efficient resource allocation problem for the wireless powered OFDMA multi-cell networks. In the considered system, the users who have data to transmit in the uplink are empowered by the wireless power obtained from multiple base stations (BSs) with a large scale of multiple antennas in the downlink. A time division protocol is considered to divide the time of wireless power transfer (WPT) in the downlink and wireless information transfer (WIT) in the uplink into separate time slot. With the objective to improve the energy efficiency (EE) of the system, we propose the antenna selection, time allocation, subcarrier and power allocation schemes. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem and then address it through an efficient alternating direction method of multipliers based distributed resource allocation algorithm. Extensive simulations are conducted to show the effectiveness of proposed schemes.

To detect the presence of primary users, spectrum sensing is considered as one of the most important and basic functions of cognitive radio. To improve the detection probability in spectrum sensing, this study mainly investigates the effect of channel gain and signal‐to‐noise ratio to secondary users (SUs) cooperation in weighted combining energy detection. In particular, with the objective to maximise the detection probability, optimal linear combining weights are derived as well as optimal transmit power of each SUs for sensing data transmission from the SUs to the fusion centre. Simulation results indicate that the detection probability can be improved by the presented scheme and the performance of the proposed method outperforms some recent works detection.

Wireless network virtualization is envisioned as a promising framework to provide efficient and customized services for next-generation wireless networks. In wireless virtualized networks (WVNs), limited radio resources are shared among different services providers for providing services to different users with heterogeneous demands. In this paper, we propose a resource allocation scheme for an orthogonal frequency division multiplexing-based WVN, where one small cell base station equipped with a large number of antennas serves the users with different service requirements. In particular, with the objective to obtain the energy efficiency in the uplink, a joint power, subcarrier, and antenna allocation problem is presented considering availability of both perfect and imperfect channel state information. Subsequently, relaxation and variable transformation are applied to develop the efficient algorithm to solve the formulated non-convex and combinational optimization problem. Extensive simulation studies demonstrate the advantages of our presented system architecture and proposed schemes.

In addition to comforting passengers' journey, the modern railway system is responsible to support a variety of on-board Internet services to meet the passenger's demands on seamless service provisioning. In order to provide wireless access to the train, one idea attracting increasing attention is to deploy a series of track-side access points (TAPs) with high-speed data rates along the rail lines dedicated to the broadband mobile service provisioning on board. Due to the heavy data traffic flushing into the base stations (BSs) of the cellular networks, TAPs act as a complement to the BSs in data delivery. In this paper, we focus on the TAP association problem for service provisioning in a heterogeneous wireless railway network, where the TAP and BS coexist by applying a queueing game theoretic approach. Specifically, we present comprehensive theoretical analysis of the delay performance on the circumstances of partially observed, totally unobserved, and totally observed state of the system. Moreover, based on the considered payoff model and the derived association delay time, the passenger's equilibrium strategies on association behaviors, i.e., whether to associate with a TAP or not, are studied. Finally, performance evaluations and discussions are provided to illustrate our proposed passenger-TAP association scheme for the heterogeneous wireless railway communication system.

Wireless network virtualization has been advocated as one of the most promising technologies to provide multifarious services and applications for the future Internet by enabling multiple isolated virtual wireless networks to coexist and share the same physical wireless resources. Based on the multiple concurrent virtual wireless networks running on the shared physical substrate, service providers can independently manage and deploy different end-users services. This paper proposes a new formulation for bandwidth allocation and routing problem for multiple virtual wireless networks that operate on top of a single substrate network to minimize the operation cost of the substrate network. We also propose a preventive traffic disruption model for virtual wireless networks to minimize the amount of traffic that service providers have to reduce when substrate links fail by incorporating $\ell _{1}$ -norm into the objective function. Due to the large number of constraints in both normal state and link failure states, the formulated problem becomes a large-scale optimization problem and is very challenging to solve using the centralized computational method. Therefore, we propose the decomposition algorithms using the alternating direction method of multipliers that can be implemented in a parallel and distributed fashion. The simulation results demonstrate the computational efficiency of our proposed algorithms as well as the advantage of the formulated model in ensuring the minimal amount of traffic disruption when substrate links fail.

Vehicular ad hoc network (VANET) is able to facilitate data exchange among vehicles and provides diverse data services. Intuitively, end-to-end backlog and delay bounds are considered significant metrics to evaluate the quality of service in VANETs. In order to analyze how the multi-hop transmission impacts the delay performance, we model the multi-hop service process into a virtualized single service in a min-plus convolution form. To obtain multi-hop end-to-end backlog and delay bound, we consider the stochastic network traffic characteristics and the highly dynamic channel environment under the static priority, first in first out, and earliest deadline first scheduling policies by applying the martingale theory. The IEEE 802.11p enhanced distributed channel access mechanism is also adopted to analyze the access performance in the MAC sub-layer. With three kinds of real wireless data traces, i.e., VoIP, gaming, and UDP, we verify our algorithm by considering the double Nakagami-m fading channel model among vehicles. From the simulation results, we can see that the supermartingale end-to-end backlog and delay bound are remarkably tight to the real simulation results when compared with the existing standard bounds. The effect of the number of vehicles on the highway on the end-to-end backlog and delay performance is also investigated.

The explosively growing demands for mobile traffic services bring both challenges and opportunities to wireless networks. Wireless network virtualization is proposed as the main evolution path toward the forthcoming fifth generation (5G) cellular networks. In this paper, we propose a software defined and virtualized (SDV) wireless network architecture for enabling multi-flow transmission with multiple infrastructure providers (InPs) and multiple mobile virtual network operators (MVNOs). In order to ensure the heterogeneity, we formulate the virtual resource allocation problem with diverse QoS requirements as a social welfare maximization problem with distance-related transaction cost. Due to hidden information of InPs and MVNOs for the auctioneer, we introduce a shadow price for ensuring desirable economic properties and total welfare for the system. Simulations are conducted with different system configurations to show the effectiveness and the energy efficiency performance of the proposed SDV wireless network framework and iterative double auction mechanism.

This paper proposes an energy-efficient resource allocation scheme for a wireless power transfer (WPT) enabled multi-user massive MIMO system with imperfect channel estimation. In the considered system, the users who have data to be transmitted only can be empowered by the WPT in the downlink from a base station (BS) with a large scale of multiple antennas. The problem of optimizing the energy efficiency objective is formulated with consideration of beamforming design, antenna selection, power allocation and time division protocol based on the practical consideration, i.e., imperfect channel state information (CSI) at the BS. In particular, the proposed antenna selection scheme is to find the optimal number of antennas and then employ the energy beamforming. The nonlinear fractional programming based scheme is utilized to address optimization of energy efficiency and to find the optimal power and time allocation. Performance evaluation is presented to demonstrate the effectiveness of the proposed schemes.

In this work, we investigate the problem of context-aware data caching in the heterogeneous small cell networks (HSCNs) to provide satisfactory to the end-users in reducing the service latency. In particular, we explore the storage capability of base stations (BSs) in HSCNs and propose a data caching model consists of edge caching elements (CAEs), small cell base stations (SBSs), and macro cell BS (MBS). Then, we concentrate on how to efficiently match the data contents to the different cache entities in order to minimize the overall system service latency. We model it as a distributed college admission (CA) stable matching problem and tackle this issue by utilizing contextual information to generate the preference lists of cache entities and contents, respectively. In the CA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to find a stable matching between the contents and the cache entities. Through numerical results, we illustrate the advantages of of our proposed methods.

In this paper, we address the energy efficient resource allocation problem for a secure orthogonal frequency division multiple access (OFDMA) relay system. In particular, we consider there is a eavesdropper near the base station (BS) which tries to overtake the information sent by BS. We formulate the joint optimization problem with the objective to optimize the energy efficiency of the considered system by considering subcarrier pairing, secret data rate and power allocation. In addition, the system can assign different priority to different users such that the security of information transmission can be guaranteed. The proposed iterative algorithm not only maximizes the system energy efficiency but can guarantee quality-of-service (QoS) properties. Simulation results show that the proposed scheme can obtain the energy efficiency maximization and also illustrate that the strategy of giving different priority to different users obtain a higher energy efficiency.

Energy-efficiency (EE) is critical for cognitive device-to-device (D2D) communications due to limited battery capacity of user equipments (UEs) and hash quality of service (QoS) requirements. In this paper, we address the EE optimization problem by proposing a game theory and matching based resource allocation algorithm. Noncooperative game is adopted to analyze the interactions among UEs and establish mutual preferences, both of which vary dynamically with channel states and interference levels. We then employs the Gale-Shapley (GS) algorithm to match D2D pairs with cellular UEs (CUs), which is proved to be stable and weak Pareto optimal. We also extend the algorithm to address scalability issues in large-scale networks by introducing some tie-breaking and preference deletion rules. Simulation results demonstrate that the proposed algorithm achieves significant EE performance and UE satisfaction gains compared to heuristic algorithms.

The explosively growing demands for mobile traffic service bring both challenges and opportunities to wireless networks, among which, wireless network virtualization is proposed as the main evolution towards 5G. In this paper, we first propose a Software Defined Network (SDN) based wireless virtualization architecture for enabling multi-flow transmission in order to save capital expenses (CapEx) and operation expenses (OpEx) significantly with multiple Infrastructures Providers (InPs) and multiple Mobile Virtual Network Operators (MVNOs). We formulate the virtual resource allocation problem with diverse QoS requirements as a social welfare maximization problem with transaction cost. Due to the high computational complexity of formulated problem and hidden information of InPs and MVNOs for SDN controller, we introduce the shadow price for ensuring the desirable economic properties as well as the total welfare of system. Simulations are conducted with different system configurations to show the effectiveness of the proposed SDN based wireless virtualization framework and double auction mechanism.

In this paper, resource allocation (RA) problem in heterogeneous Software Defined Network (SDN) with infrastructure sharing platform among multiple network service providers (NSPs) is studied. The considered problem is modeled as a reverse combinatorial auction (R-CA) game, which takes competitiveness and fairness of different NSPs into account. The heterogeneous RA associated with personal QoS requirement problem is optimized by maximizing the social welfare, which is demonstrated to be total system throughput. By exploiting the properties of iterative programming, the resulting non-convex Winner Determination Problem (WDP) is transformed into an equivalent convex optimization problem. The proposed R-CA game is strategy- proof and proved to be with low computational complexity. Simulation results illustrate that with SDN controller sharing environment, the proposed iterative ascending price Vickrey (IA-PV) algorithm converges fast and can obtain nearly optimal system throughput. It is also demonstrated to be robust with density changing, enable higher fairness and ensure individual profit among different NSPs. With the fairness guaranteed, this infrastructure sharing SDN platform can attract more NSPs to participate, in order to achieve more profit and cost reduction.

In this paper, we address the energy-efficient power allocation problem for energy-harvesting device-to- device (EH-D2D) communications, which enable user equipments (UEs) to harvest energy from ambient environments. The challenge is how to optimize energy efficiency (EE) with the intermittent and dynamic characteristics of energy arrivals. We model the offline power allocation problem as a non- cooperative game over a finite horizon. Various practical constraints such as circuit power consumption, energy causality, battery capacity, quality of service (QoS), and maximum transmission power have been taken into consideration. A low- complexity iterative power allocation algorithm is developed by exploiting properties of non-linear fractional programming and Lagrange dual decomposition. Simulation results demonstrate that the proposed algorithm outperforms the power-greedy algorithm by 55% and 84% for D2D and cellular UEs, respectively.

In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base station to other WeCMC members. When considering multi-input multi-output wireless channel and wireless power transfer, an efficient algorithm is presented to optimally schedule the data offloading and radio resources in order to maximize energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy minimization and quality of service requirement into consideration. Performance evaluations demonstrate that a significant energy saving gain can be achieved by the proposed schemes.

In this work, a game theoretical approach for addressing the virtual network service providers (NSPs), small cell provider (SCP) and user interaction in heterogenous small cell networks is presented. In particular, we consider the users can select the services of different NSPs based on their prices. The NSPs have no dedicated hardware and need to rent from the SCP in term of radio resources, e.g., small cell base stations (SBSs) in order to provide satisfied services to the users. Due to the fact that the selfish parties involved aim at maximizing their own profits, a hierarchical dynamic game framework is presented to address interactive decision problem. In the lower-level, a Stackelberg game is formulated to model and analyze the adaptive service selection of non-atomic users. In the upper-level, the NSPs and SCP sequentially determine the leasing and pricing strategies, respectively, by taking into account the service selection in the lower-level game. Performance evaluation shows the effectiveness and advantages of the proposed game theoretic approaches.

In order to fully exploit the high speed broadband multimedia services, prolonging the battery life of user equipment is critical, especially for the current smartphones. In this work, we investigate the problem of designing a content sharing collaborative mobile cloud (CMC) via user cooperation to reduce the energy consumption at terminal side. Given a group of users interested in downloading the same content from an operator, a grouping and scheduling based algorithm is proposed in order to select the proper data receiver in each scheduling time. The objective of the presented algorithm is to obtain energy efficiency as well as user fairness among the members of CMC. The proposed scheme can take both base station and terminal aspects into consideration and it is shown that the significant energy saving performance can be achieved without scarifying and drowning the battery of any terminal.

This paper addresses the resource allocation problem in collaborative relay-assisted OFDMA networks. Recent works on the subject usually ignored either the selection of relays, asymmetry of the source-to-relay and relay-to-destination links or the imperfections of channel state information. In this article we take into account all these together and our focus is two-fold. Firstly, we consider the problem of asymmetric radio resource allocation, where the objective is to maximize the system throughput of the source-to-destination link under various constraints. In particular, we consider optimization of the set of collaborative relays and link asymmetries together with subcarrier and power allocation. Using a dual approach, we solve each sub-problem in an asymptotically optimal and alternating manner. Secondly, we pay attention to the effects of imperfections in the channel-state information needed in resource allocation decisions. We derive theoretical expressions for the solutions and illustrate them through simulations. The results validate clearly the additional performance gains through an asymmetric cooperative scheme compared to the other recently proposed resource allocation schemes.

Future wireless communication systems are expected to offer several gigabits data rate. It can be anticipated that the advanced communication techniques can enhance the capability of mobile terminals (MTs) to support high data traffic. However, aggressive technique induces high energy consumption for the circuits of MTs, which drain the batteries fast and consequently limit mobility. In order to solve such a problem, a scheme called distributed mobile cloud (DMC) is foreseen as one of the potential solutions to reduce energy consumption per node in a network by exploiting collaboration within a cluster of nearby mobile terminals. In this paper we provide a detailed analysis of the energy consumption of a terminal joining the DMC and also analyze the conditions for energy savings opportunities. Numerical results are also provided to illustrate the analysis and show the potential of significant reduction of the per-node energy consumption in the mobile cloud concept.

This work addresses the radio resource allocation problem for cooperative relay assisted OFDMA wireless network. The relays adopt the decode-and-forward protocol and can cooperatively assist the transmission from source to destination. Recent works on the subject have mainly considered symmetric source-to-relay and relay-to-destination resource allocations, which limits the achievable gains through relaying. In this paper we consider the problem of asymmetric radio resource allocation, where the objective is to maximize the system throughput of the source-to-destination link under various constraints. In particular, we consider optimization of the set of cooperative relays and link asymmetries together with subcarrier and power allocations. We derive theoretical expressions for the solutions and illustrate them through simulations. The results validate clearly the additional performance gains through asymmetric cooperative scheme compared to the other recently proposed resource allocation schemes.

Future wireless services induces higher demands for the circuits of mobile terminals, which will subsequently increase energy usage and hence limit users' abilities to experience high quality of multimedia services offered by the high data rate wireless systems. In order to address this problem, we advocate a model called collaborative mobile cluster (CMC), that is foreseen as one of the potential solutions to reduce energy consumption per terminal in a network by enabling collaboration within a cluster of mobile terminals. We first compare the energy efficiency performance of unicast and multicast transmission strategies within the CMC. In addition, we propose an algorithm that can dynamically use unicast as an additional support for multicast, ultimately overcoming the inherent drawbacks of sole multicast. Analytical results are derived and illustrated by simulations. The analysis demonstrates that: i) CMC enables a great potential to reduce the per-terminal energy consumption; ii) unicast and multicast transmissions are two optional candidates, but a proper combination of them allows better energy saving gain while still fulfilling the minimum data rate requirement.

Recently, the IEEE 802.11 standard based Wireless Local Area Networks (WLAN) have become more popular and are widely deployed. It is anticipated that WLAN will play an important rule in the future wireless communication systems in order to provide several gigabits data rate. IEEE 802.11ac is one of the ongoing WLAN standard aiming to support very high throughput (VHT) with data rate of up to 6 Gbps below the 6 GHz band. In the development of IEEE 802.11ac standard, several new physical layer (PHY) and medium access control layer (MAC) features are taken into consideration, such as employing wider bandwidth in PHY and incrementing the limits of frame aggregation in MAC. However, due to the newly introduced features, some traditional techniques used in previous standards could face some problems. This paper presents a performance analysis of 802.11ac Distributed Coordination Function (DCF) in presence of hidden nodes in overlapping BSS (OBSS) environment. The effectiveness of DCF in IEEE 802.11ac WLAN when using different primary channels and different frequency bandwidth has also been discussed. Our results indicate that the traditional RTS/CTS handshake mechanism faces shortcomings and needs to be modified in order to support the newly defined 802.11ac amendment.

The IEEE 802.11ac is an emerging very high throughput (VHT) WLAN standard that could achieve PHY data rates of close to 7 Gbps for the 5 GHz band. In this paper, we introduce the key mandatory and optional PHY features, as well as the MAC enhancements of 802.11ac over the existing 802.11n standard in the evolution towards higher data rates. Through numerical analysis and simulations, we compare the MAC performance between 802.11ac and 802.11n over three different frame aggregation mechanisms, viz., aggregate MAC service data unit (A-MSDU), aggregate MAC protocol data unit (A-MPDU), and hybrid A-MSDU/A-MPDU aggregation. Our results indicate that 802.11ac with a configuration of 80MHz and single (two) spatial stream(s) outperforms 802.11n with a configuration of 40 MHz and two spatial streams in terms of maximum throughput by 28% (84%). In addition, we demonstrate that hybrid A-MSDU/A-MPDU aggregation yields the best performance for both 802.11n and 802.11ac devices, and its improvement is a function of the maximum A-MSDU size.