Satellite-terrestrial integrated network (STIN) has been recognized as a promising paradigm to provide ubiquitous and reliable coverage for billions of devices over the world. Integrated sensing and communication (ISAC) can achieve higher spectrum resource utilization efficiency, reduce the hardware size and lighten the payload of satellites for STIN. Multi-access mobile edge computing (MEC) leverages distributed edge servers to alleviate the computational burden for sensing data processing on the satellites. In this paper, we propose multi-access MEC empowered ISAC for STIN. Specifically, a group of low earth orbit (LEO) satellites perform radar sensing operations with optimized scheduling. While a portion of the acquired sensing data undergoes onboard processing at the satellites, the remaining part is processed remotely on multiple terrestrial edge servers. We formulate an optimization problem which concurrently optimizes the following strategy variables: the sensing scheduling, the beamforming for offloading transmission, the beamforming for radar sensing, the duration for sensing and data offloading, the offloaded workload and the computing capacity allocation of each edge server. Notwithstanding the non-convex nature of the formulated optimization problem, we develop a hierarchical decomposition algorithm for achieving the solution efficiently. Extensive numerical simulations confirm the superior performance of our proposed multi-access MEC-enabled ISAC framework in STIN scenarios while simultaneously verifying the efficiency of our optimization algorithm.

Cardiovascular disease (CVD) is one of the foremost causes of mortality globally, and cardiac arrhythmias constitute a major contributing factor. Continuous monitoring of cardiac signals plays a vital role for early detection and prevention. However, traditional electrocardiogram (ECG) devices require skin contact, which can be uncomfortable and inconvenient for long-term usage. In contrast, contactless cardiac health monitoring technologies, such as Wi-Fi and millimeter-wave (mmWave) radar, present a promising alternative. Millimete-rwave radar provides high range resolution, strong immunity to ambient light, and high sensitivity to small vibrations, making it ideal for contactless monitoring. However, mmWave radar primarily captures cardiac mechanical vibrations, known as radar cardiography (RCG) signals, which differ from the electrical activity recorded by clinical ECGs. To bridge this gap, we propose a contactless framework that uses mmWave radar and a conditional diffusion model to reconstruct ECG signals and then utilizes a deep learning model to classify arrhythmias. Specifically, mmWave radar captures RCG signals associated with cardiac activities. Leveraging the nonlinear relationship between cardiac mechanics and electrical activities, we design a Residual Network (ResNet)-based conditional diffusion model to convert these RCG signals into ECG signals. Finally, we develop a CNN-BiLSTM-SE network for arrhythmia classification. Experimental findings demonstrate the efficacy of the proposed approach for signal conversion as well as arrhythmia classification, offering a promising pathway toward contactless cardiac health monitoring.

We investigate wideband mmWave massive multiple-input multiple-output (MIMO) beamforming for near-field/far-field integrated sensing, communication and computation over-the-air (ISCCO) systems with multi-antenna receivers, a scenario that has not been addressed in existing works focusing on single-antenna receivers for near-field beamforming. The data from integrated sensing and communication devices is transmitted to a multi-antenna access point for data fusion by utilizing over-the-air computation, which improves spectral efficiency and reduces overhead through the addition of analog waves. We formulate the near-field/far-field wideband mmWave massive MIMO beamforming problem by maximizing the computational mean square error performance over subcarriers while guaranteeing the sensing performance measured by Cram´er-Rao bound subject to the power constraint. We propose two approaches for solving this problem. The first approach provides a fully-digital scheme serving as a performance benchmark by using the alternating direction method of multipliers algorithm. The second approach aims to further reduce computational complexity by multibeam beamforming with respect to the carefully designed analog beamformer based on the approximated channel. Simulation results demonstrate the effectiveness and low complexity of our proposed multibeam beamformer, applicable to near-field/far-field wideband mmWave ISCCO systems.

With the popularization of mobile devices, mobile crowdsensing (MCS) has become a paradigm with broad application prospects. However, traditional MCS face numerous challenges, such as surges in network traffic and infrastructure failures. To address these issues, we leverage flexible and low-cost Unmanned Aerial Vehicles (UAVs) in the MCS framework. UAV-assisted crowdsensing (UCS) provides an innovative approach to data collection that effectively addresses problems such as insufficient network coverage and congestion. In the UCS framework, UAVs can serve not only as temporary base stations (BSs) but also participate in collecting data and processing tasks. Nevertheless, the lack of adequate incentive mechanisms may lead both UAVs and mobile users to be reluctant to participate in sensing tasks. Therefore, this paper aims to investigate hierarchical incentive mechanisms for UCS. Considering the freshness of the collected data and the benefits of the platform, we adopt the Age of Information (AoI) metric to measure the quality of data. To ensure AoI of data, we model the incentive mechanisms from both the UAV and user perspectives, and we formulate them as single-dimensional and multi-dimensional contract-based incentives under scenarios of information asymmetry. Furthermore, we derive the optimal contract scheme under the constraints of individual rationality and incentive compatibility. Finally, experimental results confirm the effectiveness of the proposed contract design and maximize the utility of the model owner.

Dispersed computing has emerged as a promising paradigm that leverages underutilized resources from massive Internet of Things devices (IoTDs) to enhance the computing capacity at the network edge. However, existing works about the dispersed computing overlook the heterogeneous computing environment with parallel and serial computations and task reliability requirements for the hardware-constrained IoTDs, and they lack multi-objective optimization approaches to optimize the task offloading. To address the challenges, we propose a comprehensive scheme to achieve a delay-aware and economic-aware dispersed computing paradigm by using a multi-objective optimization approach. Particularly, we consider parallel processing at an edge server and serial processing at the lightweight IoTDs, and leverage the task redundancy to satisfy the task reliability requirements on the IoTD side. We further formulate a constrained multi-objective optimization problem (CMOP) aiming at jointly optimizing the task assignment, bandwidth allocation, and CPU frequency allocation to simultaneously minimize the total delay cost and the total charge cost of the tasks. To address the CMOP, we propose an improved constrained multi-objective evolutionary algorithm that employs a dual-population cooperative mechanism between two populations and a repairing constraint-handling technique. The dual-population cooperative mechanism can balance convergence toward Pareto optimality and solution diversity maintenance. The repairing constraint-handling technique is designed to guide solutions toward feasible regions, achieving efficient exploration of complex constrained search spaces. Simulation results demonstrate the superiority of our algorithm in seeking the better-converged and better-distributed Pareto optimal solutions to well address the tradeoffs between the two objectives.

The rapid proliferation of mobile devices within Internet of Things (IoT) has substantially heightened the demand for mobile edge computing (MEC). Fog computing (FC) is a more advanced form of edge computing that allows computing nodes to cooperate with each other. Reconfigurable intelligent surfaces (RIS) have emerged as a critical technology for optimizing wireless communication environments, attracting considerable attention. In this paper, we develop an online optimization problem for RIS-aided mobile FC deployed across wireless networks with computing nodes at the base stations (BS). We propose a Lyapunov-drift-plus-penalty-based, space aggregation-assisted proximal policy optimization (LSAPPO) algorithm to tackle the challenges in online optimization problem in RIS-aided mobile FC system. Our technique integrates a reinforcement learning (RL) algorithm employing the proximal policy optimization (PPO) agent, further enhanced by Lyapunov drift-plus-penalty optimization. The space aggregation technique effectively consolidates excessive decision variables and channel state information (CSI) into a manageable set of parameters to streamline the computing framework. Numerical simulation result shows that our proposed algorithm surpasses the benchmarks, underscoring the effectiveness in complicated wireless networks. Furthermore, we introduce the multi-agent LSAPPO algorithm to address the distributed demands of practical scenarios. The multi-agent LSAPPO algorithm enhances convergence speed and performs better in large-scale problems.

Semantic communication (SemComm) has emerged as new paradigm shifts. Most existing SemComm systems transmit continuously distributed signals in analog fashion. However, the analog paradigm is not compatible with current digital communication frameworks. In this paper, we propose an alternating multi-phase training strategy (AMP) to enable the joint training of the networks in the encoder and decoder through non-differentiable digital processes. AMP contains three training phases, aiming at feature extraction (FE), robustness enhancement (RE), and training-testing alignment (TTA), respectively. In particular, in the FE stage, we learn the representation ability of semantic information by jointly training the encoder and decoder in an analog manner. When we take digital communication into consideration, the domain shift between digital and analog demands the fine-tuning for encoder and decoder. To cope with joint training process within the non-differentiable digital processes, we propose the alternation between updating the decoder individually and jointly training the codec in RE phase. To boost robustness further, we investigate a mask-attack (MATK) in RE to simulate an evident and severe bit-flipping effect in a differentiable manner. To address the training-testing inconsistency introduced by MATK, we employ an additional TTA phase, fine-tuning the decoder without MATK. Combining with AMP and an information restoration network, we propose a digital joint source-channel coding system for image transmission, named AMP-SC1. Comparing with the representative benchmark, AMP-SC achieves 0.82 ~ 1.65dB higher average reconstruction performance among several representative datasets at different scales and a wide range of signal-to-noise ratios.

Cooperative perception constitutes a critical technology to enhance situational awareness of vehicular users (VUs) by fusing multi-source observation data. Existing approaches employ either vehicles or road infrastructure as fusion platforms. However, vehicle-based approaches suffer from severe occlusions that compromise perception reliability, while infrastructure-based approaches are constrained by fixed coverage ranges that restrict spatial perception, thereby failing to achieve both reliable and comprehensive perception simultaneously. To overcome these limitations, we propose an uncrewed aerial vehicle (UAV)-enabled cooperative perception system where a UAV operates in a cyclic process: it adjusts its position to respond to VU requests, collects observation data, and returns the compressed fusion results to the VUs. In each cycle, we jointly optimize decisions regarding UAV trajectory, request response, data collection, compression degree of the fusion results, and resource allocation to balance fusion reliability and service latency, subject to UAV kinematics, task assignment, resource allocation, and latency constraints. We formulate this optimization problem as a dynamic constrained multi-objective optimization problem featuring cascaded dependencies where the request response, data collection, and resource allocation should be determined sequentially due to the inherent logic of cooperative perception. To solve this problem, we design an evolutionary algorithm based on a cascaded dependency generation strategy in which decision variables are generated according to their dependency order. Experimental results demonstrate the superior solution performance of our algorithm over four baseline algorithms. This study advances cooperative perception for vehicular networks by providing a UAV-enabled solution ensuring reliable fusion and timely service under dynamic traffic conditions.

Ultra-reliable low-latency communication (uRLLC) has emerged as a promising technology to enable safety-critical message transmission for intelligent transportation systems. However, dynamic channel fading and complex network topologies raise the challenges of finding idle channels with limited band-width resources. Moreover, the stringent delay and reliability requirements intensify the demand for efficient and privacy-protection algorithms. In this paper, a joint optimization problem of vehicle association, bandwidth allocation and power control is formulated to maximize average energy efficiency. Considering the dynamical environments, a multi-agent deep reinforcement learning algorithm is developed to reduce computational complexity and improve privacy preservation. A partially cooperative reward function is designed to balance energy efficiency and performance constraints. Simulation results illustrate that our design can achieve the highest average energy efficiency while effectively meeting the requirements on delay and reliability.

The increasing adoption of mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) in vehicular networks is to reduce the execution delay of computation-intensive tasks and improve the spectrum efficiency. In this paper, we introduce a NOMA-assisted vehicular edge computing network, where vehicular users (VUs) form NOMA groups to share the radio resource with cellular users (CUs) for offloading their computing tasks to the MEC server in a highway scenario. Under the VU's execution delay constraints, we jointly optimize the computation resource allocation of the MEC server, the data transmission time, and the offloading decision to minimize the long-term energy consumption of the system. However, the long-term stochastic optimization problem is intricate due to the VU's mobility and the time-varying of the wireless channel. We thus propose a Lyapunov optimization based algorithm to transform the original problem into a single time slot optimization problem. Specifically, we decouple this problem into the computation resource allocation sub-problem solved at the MEC server and the offloading decision sub-problem solved at each VU. The optimal computation resource allocation is obtained by solving the knapsack problem, while the cross-entropy based algorithm is used to determine the optimal offloading decision for VUs. After that, our numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithms.

With the development of intelligent transportation systems (ITSs), digital twin (DT) technology is becoming increasingly widespread in the application of connected automated vehicles (CAVs) to enhance driving safety. However, when DT systems are used for driving safety decisions through virtual control of reality and virtual reflection of reality, decision errors may occur, which can be fatal for the driving safety of CAVs. The main reasons are attributed to three aspects: 1) the accuracy; 2) the communication delay; and 3) the safety control of the DT system. In this article, we study to improve the accuracy and safety of the DT system decisions with communication delay. First, we considered powertrain factors to construct a high-precision and high-fidelity DT system. We use the Goodness-of-Fit Functions (GoFs) and Measure-of-Performances (MoPs) to fit the vehicle’s model and carry out error measurements in the DT system. Second, we analyze the stability of the DT system using plant stability and string stability under time delay. The effective range of time delay ensures the accuracy and stability of the DT system, and provides a safety constraint for the design of the CAV’s controller. Finally, we propose a DT-assisted robust safety-critical traffic control (RSTC) strategy based on the control barrier functions (CBFs). This strategy ensures the driving safety of CAVs with preceding and following vehicles while maintaining traffic stability. The theoretical analysis and experimental results present that the proposed scheme can effectively avoid conflicts and crash risks to ensure driving safety.

Federated learning (FL) encounters slow convergence due to data heterogeneity issues. Recently, generative artificial intelligence (AI) has showcased remarkable capabilities in synthesizing realistic data. To effectively address the challenges of nonindependent and identically distributed (non-IID) data, this article introduces a collaborative AI training framework that leverages generative AI to enhance the learning performance of FL. In this framework, heterogeneous edge devices (HEDs) identify specific data categories lacking in their local data sets and acquire these data from generative AI providers (GAPs). This strategy aims to improve the convergence rate of FL. However, HEDs and GAPs may be reluctant to contribute their resources to FL training due to self-interest. Therefore, an incentive mechanism is necessary to encourage their participation. We propose a reverse auction model to facilitate data transactions among FL training buyers, GAPs, and HEDs within the FL training buyer’s budget. It focuses on determining winners and devising payment rules to maximize the FL training buyer’s utility. This involves solving a 0-1 programming problem with two sellers (GAPs and HEDs). To tackle this, we use joint bidding and virtual seller pairs for analysis. We demonstrate that our method ensures truthfulness, individual rationality, and computational efficiency. Furthermore, we employ a one-side matching mechanism to approximate the optimal solution. We further investigate a strategy to analyze and allocate data based on variance, aiming to minimize non-IID issues in local data. Simulation results demonstrate that our proposed matching mechanism can effectively improve the computational efficiency, with the test accuracy differing from the theoretical optimum by only about 0.7%, and our mechanism can outperform the other greedy algorithms. Additionally, our data allocation strategy enhances the test accuracy by approximately 7% compared to existing methods.

Incorporating mobile edge computing (MEC) and integrated sensing and communication (ISAC) has emerged as a promising technology to enable integrated sensing, communication, and computing (ISCC) in the sixth generation (6G) networks. ISCC is particularly attractive for vehicle-to-everything (V2X) applications, where vehicles perform ISAC to sense the environment and simultaneously offload the sensing data to roadside base stations (BSs) for remote processing. In this paper, we investigate a particular ISCC-enabled V2X system consisting of multiple multi-antenna BSs serving a set of single-antenna vehicles, in which the vehicles perform their respective ISAC operations (for simultaneous sensing and offloading to the associated BS) over orthogonal sub-bands. With the focus on fairly minimizing the sensing completion latency for vehicles while ensuring the detection probability constraints, we jointly optimize the allocations of radio resources (i.e., the sub-band allocation, transmit power control at vehicles, and receive beamforming at BSs) as well as computation resources at BS MEC servers. To solve the formulated complex mixed-integer nonlinear programming (MINLP) problem, we propose an alternating optimization algorithm. In this algorithm, we determine the sub-band allocation via the branch-and-bound method, optimize the transmit power control via successive convex approximation (SCA), and derive the receive beamforming and computation resource allocation at BSs in closed form based on generalized Rayleigh entropy and fairness criteria, respectively. Simulation results demonstrate that the proposed joint resource allocation design significantly reduces the maximum task completion latency among all vehicles. Furthermore, we also demonstrate several interesting trade-offs between the system performance and resource utilizations.

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services.

In this paper, we exploit an emergency mobile crowdsensing (MCS) framework that utilizes unmanned aerial vehicles (UAVs) in collaboration with uncrashed base stations (BSs) to enhance sensing and communication efficiency. In the proposed framework, mobile users (MUs) equipped with sensors collect data, while UAVs, deployed as aerial relays, collaborate with uncrushed BSs to facilitate the transmission and aggregation of the sensed data from all MUs. However, the limited resources significantly affect the deployment of UAVs and the design of the UAV-relay-assisted MCS system. Moreover, selecting MUs for sensing tasks and allocating bandwidth among them are crucial factors that determine MUs’ sensing capabilities and the data transmitting policies. Incorporating with foregoing essential factors, we formulate a comprehensive problem that jointly optimizes the MU selection, bandwidth allocation, UAV deployment, as well as strategies for sensing and transmitting data, aiming to improve the total reward of agent. The formulated problem poses high challenges due to the coupling between the sensing, transmission, as well as the UAVs deployment policies. To deal with this problem, we first derive the optimal transmission power and sensing data size under given MU selection, bandwidth allocation, and UAVs deployment strategy. The original optimization problem is subsequently decomposed into three folds, corresponding to finding the optimal MU selection, bandwidth allocation solution, as well as the deployment of UAVs. Meanwhile, a joint dynamic programming and a swap-then-compare enabled algorithm is proposed to obtain the optimal MU selection and bandwidth allocation policies. Next, the successive convex approximation (SCA) techniques are used to find the optimal locations for the UAVs. Extensive numerical results verify that the proposed joint algorithm can significantly outperform several benchmark approaches.

The development of intelligent transportation has generated many ultra reliable low latency communication (URLLC) tasks, which require sufficient communication and computation resources for task offloading and processing. Although mobile edge computing (MEC) provides a promising solution, its efficiency is subject to the limited knowledge and analysis capability on the physical networks. Therefore, in this paper, we propose a digital twin (DT) empowered MEC framework to strengthen the MEC task offloading efficiency in cellular vehicle-to-everything (C-V2X) networks. Our proposed DT is constructed through a hybrid data-driven and model-driven approach to capture the realistic transportation network features. Then, DT leverages the metric of time to collision to predict vehicular safety levels and estimates the corresponding URLLC task requirements of future time slots. The prediction results are further utilized to make decisions on the URLLC resource reservation. Different from conventional studies, we consider the influence of DT’s inaccurate predictions (i.e., the prediction with error) on the resource allocations. Specifically, the inaccurate DT prediction results are considered as uncertain constraints of the resource reservation problem. A robust parameter from the robust optimization is adopted to adjust the tradeoff between the problem uncertainty and solution optimality degree. Further, we leverage the optimized resource reservation results to construct the task offloading problem. The problem is decoupled into two sub-problems of channel resource allocation and computation resource allocation, respectively. And a two-stage matching algorithm is developed to solve each sub-problem based on the resource reservation constraints. Finally, realistic road information is mapped into DT for simulations. Simulation results validate the advantages of our proposed approach by comparing with existing schemes.

As an emerging technology, Digital Twin (DT) can provide a virtual representation of transportation infrastructures to achieve efficient and precise management of Intelligent Transportation Systems (ITS). However, a mixed traffic scenario of coexisting intelligent connected vehicles (ICVs) and non-intelligent connected vehicles (N-ICVs) increases challenges for digital ITS. N-ICVs are unable to generate and update their DT models independently due to constrained communication and computing capabilities. It is crucial to achieve real-time DT model update and migration of N-ICVs. In this paper, we propose a cooperative perception aided DT model update and migration approach, which dispatches ICVs to cooperatively sense and transmit information of nearby N-ICVs to assist in generating N-ICVs’ DT models. In particular, with the objective of minimizing the average maximum weighted age of information (AMWAoI), we jointly optimize the cooperative ICV selection as well as the bandwidth and computation allocations while guaranteeing the perception performance. We then propose a sensing data weighted size maximization matching algorithm to achieve an optimal ICV selection strategy, and the bandwidth and computation allocations are optimized by the gradient descent algorithm. Considering the dynamic nature of vehicular networks, a deep reinforcement learning-based access selection and DT model migration algorithm is further proposed to achieve continuous service provisioning. Simulation results demonstrate that the proposed algorithm achieves the lowest AMWAoI while meeting the perception performance requirement.

Due to the limitations of computing resources and battery capacity, the computation tasks of ground devices can be offloaded to edge servers for processing. Moreover, with the development of the low earth orbit (LEO) satellite technology, LEO satellite-terrestrial edge computing can realize a global coverage network to provide seamless computing services beyond the regional restrictions compared to the conventional terrestrial edge computing networks. In this paper, we study the computation offloading problem in the LEO satellite-terrestrial edge computing systems. Ground devices can offload their computation tasks to terrestrial base stations (BSs) or LEO satellites deployed on edge servers for remote processing. We formulate the computation offloading problem to minimize the cost of devices while satisfying resource and LEO satellite communication time constraints. Since each ground device competes for transmission and computing resources to reduce its own offloading cost, we reformulate this problem as the LEO satellite-terrestrial computation offloading game (LSTCO-Game). It is derived that there is an upper bound on transmission interference and computing resource competition among devices. Then, we theoretically prove that at least one Nash equilibrium (NE) offloading strategy exists in the LSTCO-Game. We propose the game-theoretical distributed computation offloading (GDCO) algorithm to find the NE offloading strategy. Next, we analyze the cost obtained by GDCO's NE offloading strategy in the worst case. Experiments are conducted by comparing the proposed GDCO algorithm with other computation offloading methods. The results show that the GDCO algorithm can effectively reduce the offloading cost.

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT's model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts.

Unmanned Aerial Vehicle (UAV)-assisted Low Earth Orbit (LEO) satellite edge computing (ULSE) networks can address the challenge communications issues in areas with harsh terrain and achieve global wireless coverage to provide services for mobile user devices (MUDs). This paper studies the LEO-UAV task offloading problem where MUDs compete for limited resources in the ULSE networks. We formulate the optimization problem with the goal of minimizing the cost of all MUDs while meeting resource constraint and satellite coverage time constraint. We first theoretically prove that this problem is NP-hard. We then reformulate the problem as a LEO-UAV task offloading game (LUTO-Game), and show that there is at least one Nash equilibrium solution for the LUTO-Game. We propose a joint UAV and LEO satellite task offloading (JULTO) algorithm to obtain the Nash equilibrium offloading strategy, and analyze the performance of the worst-case offloading strategy obtained by the JULTO algorithm. Finally, extensive experiments, including convergence analysis and comparison experiments, are carried out to validate the effectiveness of our JULTO algorithm.

Containers have become popular for deploying applications in Edge Computing (EC) for their seamless integration and easy deployment. Frequent container updates are essential to enhance performance and introduce new challenges for cutting-edge applications such as large language models and digital twins. However, traditional container update methods result in substantial download costs and task interruptions, which are unacceptable for latency-sensitive tasks in resource-constrained EC. Existing work has largely overlooked the layered structure of container images. By leveraging this layered structure, duplicate downloads can be reduced, and various layers can be transferred from other edges, reducing burden on the remote cloud. In this paper, we model the layer-aware container update problem with edge-cloud collaboration to minimize update and scheduling costs. We present the Layer-aware Edge-cloud collaborative Container Update (LECU) algorithm based on reinforcement learning to make container update decisions. Moreover, a task scheduling algorithm is devised to schedule tasks affected by container updates to other edges, minimizing the impact of task interruptions. We implement our LECU algorithm on an edge system with real-world data traces to demonstrate its effectiveness and conduct larger-scale simulations to evaluate its scalability. Results demonstrate that our algorithms reduce container update and task scheduling costs by 14% and 19%, respectively, compared to baselines.

With the development of new generation communication technologies, the future maritime information networks pave the way to promote the exploration of ocean resources. Moreover, the underwater data center (UDC) is considered to be a significant data storage and computing unit in future maritime networks for providing ocean services. However, the current deployment of UDC faces the critical issues, i.e., the long-distance underwater transmission is unreliable and the energy consumption and resources of underwater transmission are overloaded. To address the two critical issues of unreliable data transmission and high resource overheads, in this paper, we present a hybrid semantic data transmission architecture in air-ocean integrated networks, which can perceive the sea surface data accurately and transmit it to the UDC for processing. Specifically, in surface layer, uncrewed aerial vehicles (UAVs) perceive ocean environment and send data to the buoy via non-orthogonal multiple-access (NOMA) transmission to improve the channel utilization. In underwater layer, the buoy sends the collected data to UDC via semantic transmission, while the semantic fidelity metric is utilized to improve the transmission efficiency. A resource allocation problem for energy minimization is formulated to jointly optimize the semantic scaling factor, the NOMA decoding order, the communication and computing resource allocations. We exploit a decomposition approach to transform the problem into two sub-problems, where the optimal resource allocations are obtained by proposing efficient algorithms. Finally, we provide simulations to verify the effectiveness and efficiency of our proposed scheme. The results demonstrate that our proposal has the advantages of lower energy consumption compared to several baseline schemes.

Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences. 360$^{\circ }$VR video streaming has experienced significant growth and popularity as a pivotal VR application. However, the combination of limited network bandwidth and the demand for high-quality videos frequently hinders the achievement of a satisfactory quality of experience (QoE). Although prior methods have enhanced QoE, the effects of decoding latency have been poorly studied. It is technically challenging to design a quality adaptation algorithm that can balance the pursuit of high-quality videos and the limitation of limited bandwidth resources. To address this challenge, we propose an edge-end architecture for 360$^{\circ }$VR video streaming and aim to enhance overall QoE by solving a performance optimization problem. Specifically, our experiments on commercial mobile devices in real-world situations reveal that decoding latency significantly influences QoE. First, decoding latency plays a major role in contributing to end-to-end latency, which exceeds the transmission latency. Second, decoding latency can differ considerably between devices with varying computational capabilities. Building on this insight, we propose a novellatency-awarequalityadaptation (LAQA) algorithm. LAQA lies in developing a solution that can allocate video quality in real-time and enhance overall QoE. LAQA involves not only the quality of the received content, the transmission latency and the quality variance, but also the decoding latency and the fairness of the user quality. Subsequently, we formulate a combinatorial optimization problem to maximize overall QoE. Through extensive validation with experimental data from real-world situations, LAQA offers a promising approach to enhance QoE and ensure fairness performance in different devices. In particular, LAQA achieves 16.77% and 10.66% enhancement over the state-of-the-art combinatorial optimization and reinforcement learning algorithm, respectively, in terms of QoE at 4K resolution. Furthermore, LAQA ensures excellent scalability by simulating the number of users ranging from 15 to 60, making it a robust solution for diverse and growing user scales.

Non-orthogonal multiple access (NOMA) emerges as a superior technology for enhancing spectral efficiency, reducing latency, and improving connectivity compared to orthogonal multiple access. In NOMA networks, successive interference cancellation (SIC) plays a crucial role in decoding user signals sequentially. The challenge lies in the joint optimization of SIC ordering and power allocation, a task complicated by the factorial nature of ordering combinations. This study introduces an innovative solution, the Attention-based SIC Ordering and Power Allocation (ASOPA) framework, targeting an uplink NOMA network with dynamic SIC ordering. ASOPA aims to maximize weighted proportional fairness by employing deep reinforcement learning, strategically decomposing the problem into two manageable subproblems: SIC ordering optimization and optimal power allocation. We use an attention-based neural network to process real-time channel gains and user weights, determining the SIC decoding order for each user. A baseline network, serving as a mimic model, aids in the reinforcement learning process. Once the SIC ordering is established, the power allocation subproblem transforms into a convex optimization problem, enabling efficient calculation of optimal transmit power for all users. Extensive simulations validate ASOPA’s efficacy, demonstrating a performance closely paralleling the exhaustive method, with over 97% confidence in normalized network utility. Compared to the current state-of-the-art implementation, i.e., Tabu search, ASOPA achieves over 97.5% network utility of Tabu search. Furthermore, ASOPA has two orders of magnitude less execution latency than Tabu search when$N=10$and even three orders magnitude less execution latency less than Tabu search when$N=20$. Notably, ASOPA maintains a low execution latency of approximately 50 milliseconds in a ten-user NOMA network, aligning with static SIC ordering algorithms. Furthermore, ASOPA demonstrates superior performance over baseline algorithms besides Tabu search in various NOMA network configurations, including scenarios with imperfect channel state information, multiple base stations, and multiple-antenna setups. These results underscore the robustness and effectiveness of ASOPA, demonstrating its ability to ability to achieve good performance across various NOMA network environments.

Federated learning (FL) has been regarded as a promising paradigm for enabling distributed model training over resource-limited edge devices. Although FL maintains data locality and enhances model generalization, it faces challenges such as model leakage and pressure from frequent model updates. Some existing schemes, such as differential privacy and model encryption, can partially alleviate these issues while sacrificing the accuracy of the modeling training or increasing the computational overheads in training. To address this issue, we design a low-complexity linear collaborative FL (LCFL) framework to enhance the privacy and accuracy of FL. Specifically, we propose the collaborative secrecy transmission (CST) algorithm by integrating a variant of Shamir's secret-sharing with the model segmentation, which can compresses and encrypts the local models for FL. The decoding complexity of the CST algorithm is only$O(N^{3})$under the compression ratio of$N$, which reduces the communication overhead and computational complexity. We conduct a quantitative analysis of the model error induced by the CST algorithm and derive its closed-form upper bound. Within LCFL, we formulate an optimization problem to maximize the global model accuracy in wireless FL by optimizing compression ratios, bandwidth allocation, and transmit-powers. Subsequently, we propose a low-complexity algorithm to solve this problem effectively. Numerical simulations demonstrate the efficacy of LCFL in improving FL's accuracy and security, and the results validate the efficiency of the proposed optimization scheme for wireless FL. The source code can be downloaded from the Github:https://github.com/MinITerence/LCFL.

The data compression based transmission has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources in the future sixth-generation (6G) wireless networks. In this paper, we propose an integrated communication and computation resource allocation system for image transmission based on compressive sensing (CS), which consists of several camera devices and a base station (BS). The device side first compresses the images, after which the compressed images are transmitted using non-orthogonal multiple access (NOMA) transmission, and finally the BS restores the received compressed images. Due to the limited energy supply, the total system energy consumption is minimized by jointly optimizing the image sampling rate, the image data transmission power, the number of floating point operations per second (FLOPS), the time of image compression and the time of data transmission under the constraints of latency and the peak signal-to-noise ratio (PSNR). Due to the non-convexity of the proposed problem, after a series of equal substitutions we convexify the problem. Then, the Karush-Kuhn-Tucker (KKT) condition and the gradient descent method are used to obtain the optimal solution of the target problem. After simulation experiments, it is concluded that the proposed CS-based image transmission scheme effectively reduces the total energy consumption by a factor of 2.7 compared with frequency division multiple access (FDMA), and the total latency by 180% compared with the original image transmission.

Recently, the promising unmanned aerial vehicle (UAV)-assisted wireless networks (UAWNs) have emerged by advocating the UAVs to provide wireless transmission services. However, owing to the ever-growing volume of data traffic and the untrusted network operation environment, efficiently and securely assigning limited bandwidth for high-quality wireless communication between UAVs and mobile users poses a significant challenge. To address this challenge, we propose a novel secure UAV-bandwidth allocation scheme to provision reliable wireless transmission services for mobile users in UAWNs. Specifically, we first introduce a novel blockchain-empowered framework for secure bandwidth allocation, designed to automate payment processes and deter malicious activities through the immutable logging of transactional and behavioral data. Wherein, a smart contract is designed to regulate the honest behaviors of both mobile users and UAVs during bandwidth allocation with a distributed manner. Besides, a delegated proof-of-stake (DPoS) with reputation consensus protocol is presented to ensure the authenticity and efficiency of the decision-making process. Further, we apply the Stackelberg game theory to model the dynamic of the bandwidth allocation between mobile users and UAVs. In this game, the UAVs act as game leaders to determine the bandwidth price, while each mobile user acts as a game follower, making decision on the bandwidth request. We utilize the backward induction method to derive the optimal strategies of both parties, culminating in the identification of the Stackelberg equilibrium of the formulated game. Finally, extensive simulations are carried out to show the superiority of the proposed scheme over conventional schemes in terms of security, efficiency, and fairness in bandwidth allocation.

Mobile Edge Computing (MEC), as a highly promising technology, effectively processes computation-intensive tasks by offloading them to edge servers. Utilizing the advantages of Unmanned Aerial Vehicles (UAVs) in deployment flexibility and broad coverage, UAV-assisted edge computing can significantly enhance system efficiency. This paper studies a scenario where a UAV-MEC system serves multiple Mobile Users (MUs) with random task arrivals and movements. We minimize the energy consumption of MUs by jointly optimizing UAV trajectory and resource allocation for MUs subjected to the UAV energy limit. The problem is formulated as a multi-stage Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we propose an algorithm called JTORA integrated Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques. Specifically, we initially transform the multi-stage MINLP problem into a deterministic optimization problem utilizing Lyapunov techniques and decompose the original problem into two sub-problems in parallel. Through DRL, we solve the first sub-problem of trajectory and communication resources optimization. For the second sub-problem involving computing resource allocation, convex optimization is employed to get the optimal solution. Theoretical analysis and experimental results demonstrate that the JTORA algorithm can effectively reduce the energy consumption of MUs while ensuring UAV endurance.

Integrated sensing and communication (ISAC) networks constitute a crucial paradigm for facilitating numerous advanced services in future wireless networks. This paper investigates the joint bandwidth and power allocations for device-to-device (D2D) communications aided ISAC in which D2D pairs complete their data transmissions by using the bandwidth allocated by the base station (BS) while providing sensing services for the BS. To this end, we formulate a joint optimization of bandwidth allocation and power allocations for both the target sensing and data transmission of each D2D pair, with the objective of maximizing a system-wise gain that accounts for both performances of target sensing and D2D data transmission. Despite the formulated optimization problem is strictly non-convex, we develop an efficient algorithm based on Lagrangian duality and sequential convex programming for solving it. Simulation results demonstrate that our proposed D2D communications aided ISAC is both accurate and efficient over several benchmark schemes.

With the growing demand for image transmission, there is a need for solutions that offer low energy consumption and low latency. In this paper, we present a novel relay-assisted system based on non-orthogonal multiple access (NOMA) and mobile edge computing (MEC). Our proposed system compresses images at the device end, decompresses them at either a relay or a cloud server (CS). The primary objective is to minimize system energy consumption under given task delay constraints. Considering that this is a non-convex optimization problem, we solve it by decomposing it into a continuous subproblem and a discrete subproblem. To solve the continuous subproblem, we convexify it by introducing new parameters and change variables to get the optimal the computing power of devices, relay and CS, sampling rate of devices, transmission power of devices and relay. To solve the discrete subproblem, we propose a cross-entropy (CE) algorithm to obtain the optimal decompression decision and subcarrier allocation decision. Simulation results demonstrate the accuracy and effectiveness of our algorithm in optimizing total energy consumption compared to the Linear Interactive and General Optimizer (LINGO) and frequency division multiple access (FDMA) methods.

Virtual Reality (VR) paves the way to link Meta-verse and the real world, allowing users to enjoy immersive experiences. However, delivering high-quality full spherical VR service within limited rendering energy is a challenge. Mobile edge computing (MEC) is a promising paradigm to provide rendering computation services to users. It is widely held that the rendering of panoramic video presents a significant impediment in the VR system, with disregard for the importance of the data correlation leading to excessive energy consumption caused by repeated rendering. In this paper, we propose a cooperative rendering scheme in mm Wave-enabled wireless networks with MEC via a coalition formation game, among which we focus on the data correlation of the background environment of VR streams. Specifically, we first devise a multiple MEC servers rendering framework, and we formulate an optimization problem to maximize the system utility, which contains energy savings for MEC servers and users' quality of experience (QoE). Then, considering the overlap of the VR streams requested by users in Metaverse, a coalition formation game is employed to model the cooperations among MEC servers, such that the user's QoE is significantly improved. The simulation experiments show that our proposed algorithm is superior to benchmark algorithms in improving the users' QoE and reducing the total energy consumption of MEC servers.

Lightweight containers provide an efficient approach for deploying computation-intensive applications in net-work edge. The layered storage structure of container images can further reduce the deployment cost and container startup time. Existing researches discuss layer sharing scheduling theoretically but with little attention paid to the practical implementation. To fill in this gap, we propose and implement a Layer-aware and Resource-adaptive container Scheduler (LRScheduler) in edge computing. Specifically, we first utilize container image layer information to design and implement a node scoring and container scheduling mechanism. This mechanism can effectively reduce the download cost when deploying containers, which is very important in edge computing with limited bandwidth. Then, we design a dynamically weighted and resource-adaptive mechanism to enhance load balancing in edge clusters, increasing layer sharing scores when resource load is low to use idle resources effectively. Our scheduler is built on the scheduling framework of Kubernetes, enabling full process automation from task information acquisition to container deployment. Testing on a real system has shown that our design can effectively reduce the container deployment cost as compared with the default scheduler.

As an emerging technology, Digital Twin (DT) can provide a virtual presentation of the physical Intelligent Trans-portation Systems (ITS) to enhance the applications of ITS such as cooperation perception. In cooperative perception, accurate location is crucial for selecting proper cooperative vehicles (CoVs) to improve the perception performance. However, due to the high mobility of vehicles, the deviation between DT and physical world may lead to non-negligible location errors, which raises the challenges for achieving efficient CoV selection in cooperative perception. In this paper, we propose a DT-empowered multi-vehicle cooperative perception system, in which the CoV selection and bandwidth allocation are jointly optimized to improve the performance of cooperative perception. Specifically, an asyn-chronous federated learning scheme is deployed in DT for location prediction to mitigate the effect of the deviation. Based on the prediction results, the problem of joint predictive scheduling and bandwidth allocation is then formulated as the average delay minimization problem while reaching the required performances. The adaptive CoV selection and bandwidth allocation algorithm based on deep reinforcement learning is proposed to find the optimal scheduling strategy. Simulation results demonstrate that the proposed algorithm achieves the lowest average delay while effectively guaranteeing the performance requirement.

This article considers deploying a dual-functional unmanned aerial vehicle (UAV) as both an aerial data collector and aerial anchor node (AN) to assist the ground base stations in providing integrated localization and communication (ILAC) service in urban areas. A major challenge to the urban ILAC service quality lies in the severe blockage of ground-to-air links by densely located buildings. To improve the service quality, we leverage the recent advance in urban physical environment map (PEM), also known as the three-dimensional (3-D) city map, to aid in optimizing the 3-D deployment of UAV. This allows a UAV to avoid blockages and establish strong acrlong LoS links to all target ground users. We propose a PEM-aided ILAC service model and formulate a UAV 3-D deployment optimization problem. The aim is to maximize the sum communication rate of ground users while satisfying individual localization accuracy and communication rate constraints. The problem is very challenging to solve mainly because the localization accuracy and blockage-avoiding constraints are both nonconvex with respect to UAV position. To tackle the problem, we first adopt a new localization accuracy metric and subsequently derive a convex expression of the localization constraint. Then, we convert the blockage-avoiding constraints into an equivalent and analytically tractable form and propose an efficient iterative algorithm to solve the UAV deployment optimization problem. Simulation results show that the proposed method achieves close-to-optimal performance under dense urban blockage setups, while significantly reducing the computational complexity.

Integrated sensing and communication (ISAC) provides a spectrum-efficient approach for simultaneously enabling reliable data transmission and high-quality sensing. This paper investigates an ISAC-enabled multi-device cooperative sensing system in which the devices perform cooperative sensing towards multiple targets in a time-division manner. Within the allocated time, each device senses the targets and transmits data to the base station simultaneously via ISAC. To investigate this problem, we formulate a joint optimization of the beamforming for both sensing and transmission as well as the time allocation for different devices, aiming at maximizing the total throughput of the devices while guaranteeing the multi-target sensing quality, the cooperative sensing requirement and the fairness in data transmission. To tackle the non-convexity of the formulated problem, we first decompose the problem into a beamforming subproblem and a time allocation subproblem. Subsequently, we transform the beamforming subproblem into a tractable form. We then analyze the feature of the optimal time allocation in the time allocation subproblem while providing its semi-analytical expression, based on which we further propose an efficient algorithm to solve the original problem. Simulation results validate the effectiveness of our algorithm and the performance advantages of our fairness-aware ISAC-enabled cooperative sensing in improving both throughput and cooperative sensing accuracy.

Federated learning (FL) has gained widespread adoption in Internet of Things (IoT) applications, promoting the evolution of IoT toward Artificial Intelligence of Things (AIoT). However, IoT devices are still vulnerable to various privacy inference attacks in FL. While current solutions aim to protect the privacy of devices during model training, the published model is still at risk from external privacy attacks during model deployment. To address the privacy concerns throughout the entire FL lifecycle, this article proposes a privacy-enhanced and efficient federated knowledge transfer framework for IoT, named PEFKT, which integrates the knowledge transfer method and local differential privacy (LDP) mechanism. In PEFKT, we devise a data diversity-driven grouping strategy to tackle the non-independent and identically distributed (non-IID) issue in IoT. Additionally, we design a quality-aware soft-label aggregation algorithm to facilitate effective knowledge transfer, thereby improving the performance of the student model. Finally, we provide rigorous privacy analysis and validate the feasibility and effectiveness of PEFKT through extensive experiments on real data sets.

Digital twin has been emerging as a promising paradigm that connects physical entities and digital space, and continuously evolves to optimize the physical systems. In this article, we focus on studying efficient communication and computation scheme when constructing the Marine Internet of Things (M-IoT)’s digital twin with secrecy provisioning. Specifically, the digital twin model is trained based on federated learning, in which all the unmanned surface vehicles deliver the trained models with nonorthogonal multiple access (NOMA) to the high-altitude platform (HAP) for global model aggregation. Considering the potential eavesdropping on the radio signals of HAP, we utilize the chaotic sequences to spread the model information before the global model broadcasting. In this framework, we aim to minimize the total energy consumption for constructing the digital twin of M-IoT by jointly optimizing the global accuracy, the local accuracy, the HAP’s transmission power and NOMA transmission duration, subject to the secrecy provisioning and latency constraint. An effective low-complexity algorithm is proposed to tackle this joint optimization problem with the use of a layered feature. Finally, numerical results are given to validate the performance gain of the proposed scheme, in comparison with the fixed accuracy scheme, the nonspread spectrum scheme and the time division multiple access transmission scheme.

Dual-function radar-communication (DFRC) can alleviate spectrum congestion and competition with the spectrum-sharing architecture for next-generation wireless networks. In this article, we consider the problem of robust beamforming in millimeter wave (mmWave) DFRC systems. Unlike most existing works which assume that the angle-of-arrival (AoA)/angle-of-departure (AoD) or channel state information (CSI) is perfectly known, we consider the case of imperfect CSI resulting from the movement of users/targets or the beam misalignment errors. Our object is to maximize the achievable ergodic rate for communication under the constrained worst-case sensing signal-clutter-noise ratio (SCNR) for radar. By integrating the two-phase-shifter structure into our proposed robust hybrid beamforming architecture, it substantially improves the system performance and increases the design flexibility at the cost of doubling the number of phase shifters. With the two-phase-shifter structure, our proposed robust multibeam technology coherently combines sensing subbeams and communication subbeams with a widebeam radiation pattern for alleviating the effect of AoA/AoD uncertainty. Our proposed robust multibeam beamformer can shape the transmit waveform flexibly with very low complexity, which is amiable for practical implementation. Theoretical and numerical results validate the effectiveness and robustness of our proposed method in mmWave DFRC systems.

Due to the massive computing demands of the Internet of Things, mobile edge computing (MEC) has been extensively investigated as a means of providing computation-intensive and latency-sensitive services at the network edge. With increasing density of base stations (BSs), users are simultaneously served by multiple BSs, leading to the multicell MEC environment. Intelligent reflecting surface (IRS) provides a promising solution for constructing the virtual Line-of-Sight (LoS) links between cell-edge users (CEUs) and BSs. In this article, we investigate the joint channel estimation and resource allocation in the IRS-aided multicell MEC system. Instead of assuming the perfect channel state information (CSI), we propose a three-phase channel estimation method to obtain the CSI. Our purpose is to minimize the total joint energy and latency cost (JELC) in terms of both task-execution latency and energy consumption in the IRS-aided multicell MEC problem by jointly optimizing the task offloading volume, precoding matrix, and IRS phase shifts. We propose a quadratically constrained program (QCP)-assisted proximal policy optimization (PPO) reinforcement learning algorithm with two modules (i.e., QCP optimizer and PPO agent) execute iteratively. The QCP optimizer is utilized to compute the offloading decision variables, and the PPO agent is adapted to determine the optimal channel precoding matrix and the phase shifts of IRS. Numerical results validate that our QCP-assisted PPO algorithm executes more rapidly than benchmarks. Moreover, the proposed QCP-assisted PPO algorithm delivers the best performance compared to benchmarks. Furthermore, the multicell IRS-aided MEC framework yields additional performance gains compared to those without IRS.

The integration of unmanned aerial vehicles (UAVs) and marine communication networks has been emerging as a promising paradigm to cater for the growing maritime activities, e.g., marine environment monitoring and ocean resource exploration. The increasing growth of marine applications and services poses challenges for processing marine data, while the resources-limited UAVs cannot satisfy the requirements of computing-intensive and energy consumption. In this paper, we consider a marine edge computing scenario with a group of UAVs and ocean beacon stations (OBSs) and propose a multi-UAV aided multi-access edge computing for marine networks from the perspective of system-welfare and energy-efficient design. Specifically, we propose a multi-task multi-access offloading scheme in marine edge computing networks, in which multiple UAVs can process their workloads locally or offload their partial workloads to multiple OBSs for processing. We consider the total utilities for completing all tasks as the system welfare, and measure the difference between the system welfare and energy consumption as the system revenue. A joint optimization problem is formulated by optimizing the OBS selection, the offloading ratio and the transmission duration, with the objective of increasing the system revenue in marine edge computing networks. We exploit a vertical decomposition architecture to solve the formulated non-convex problem via decomposing it into three sub-problems. Regarding each sub-problem, we propose efficient algorithms to derive the optimal solutions. We finally conduct simulations to verify the performance of the proposed algorithms. The results demonstrate that our proposed algorithms can achieve the best performance for improving the system revenue in comparison with several benchmark algorithms.

To provide a better support for various vehicular applications, digital twin (DT), as an emerging technology, can enable a virtual presentation of physical vehicular networks to reflect the current network state through real-time data updating. However, the constrained resources and high data updating cost may degrade the performance of DT. In this paper, we trade off the data updating cost and the performance of DT to adaptively determine the resource management and computation offloading in vehicular networks. Specifically, we propose a novel vehicle to vehicle pairing prediction algorithm assisted by DT to improve the offloading decision efficiency and investigate the effect of data updating frequency on prediction accuracy. Based on the prediction results, we formulate a joint data updating frequency selection, offloading decision and channel allocation problem with the objective of minimizing the computation and communication costs. To solve the formulated problem, we propose a prediction-based stability maximum pairing algorithm to obtain the proper task offloading strategy. Moreover, a deep Q-learning network algorithm is proposed to select the optimal DT data updating frequency according to the real-time vehicular network state. Based on the obtained optimal solution, we further propose an alternating direction method of multipliers-based iteration algorithm to optimize the computation and channel resource allocation and minimize the total costs. Numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms.

An unmanned aerial vehicle (UAV)-enabled intelligent transportation system utilizes a set of UAVs to collect and process surveillance data for transportation management. Subsequently, the processing results of the UAVs are transmitted to a control center that makes a centralized transportation management decision based on the fusion of all processing results. When performing the monitoring tasks, the UAVs can access to an edge server for offloading. To reduce the energy consumption and improve the fusion performance, the control center schedules the UAVs to perform the tasks in an energy-efficient manner while synchronizing the completion time of the UAVs. As a result, the control center studies a constrained multi-objective optimization problem (CMOP), in which two objectives, i.e., the total energy consumption of the UAVs and total completion time difference among the UAVs, are simultaneously considered. To tackle the CMOP, we develop an improved constrained multi-objective evolutionary algorithm. Particularly, we design an improved genetic operator and repairing constraint-handling technique to improve the overall performance of the proposed algorithm in seeking Pareto optimal solutions for the CMOP. Numerical results demonstrate that compared with the baseline algorithms, the proposed algorithm has great advantages in finding better solutions with the enhanced diversity and convergence for the CMOP.

Mobile Edge Computing (MEC) has envisioned to be a promising technology to provide more efficient services for computation-intensive but delay-sensitive onboard mobile services. In this paper, the Non-Orthogonal Multiple Access (NOMA) technology is applied in a vehicular edge computing network, in which vehicular users (VUs) can offload partial computation tasks to MEC servers over wireless channels for remote execution. In this network, an optimization problem for the long-term energy consumption of the system is presented and aims to minimize it by jointly optimizing the Successive Interference Cancellation (SIC) ordering of NOMA, the VUs’ transmit power for computation offloading, and computation resource allocation of the MEC server. To deal with the intractable long-term optimization problem, we first transform it into an equivalent instantaneous form based on the Lyapunov optimization theory. Since the transformed problem is still highly non-convex, we further decompose it into the interactive resource allocation and SIC ordering sub-problems. For the resource allocation sub-problem, we exploit its convexity through the transformation and reparameterization, and derive the optimal solution in accordance with the Karush-Kuhn-Tucker (KKT) conditions and the gradient descent algorithm. After that, we propose a low-complexity algorithm by leveraging the Tabu search to obtain the sub-optimal SIC ordering. Simulation results validate the effectiveness of the proposed algorithm and the superiority of NOMA compared to Frequency Division Multiple Access (FDMA).

The emerging multi-access edge computing (MEC) technology effectively enhances the wireless streaming performance of 360-degree videos. By connecting a user's head-mounted device (HMD) to a smart MEC platform, the edge server (ES) can efficiently perform adaptive tile-based video streaming to improve the user's viewing experience. Under constrained wireless channel capacity, the ES can predict the user's field of view (FoV) and transmit to the HMD high-resolution video tiles only within the predicted FoV. In practice, the video streaming performance is challenged by the random FoV prediction error and wireless channel fading effects. For this, we propose in this paper a novel two-stage adaptive 360-degree video streaming scheme that maximizes the user's quality of experience (QoE) to attain stable and high-resolution video playback. Specifically, we divide the video file into groups of pictures (GOPs) of fixed playback interval, where each GOP consists of a number of video frames. At the beginning of each GOP (i.e., the inter-GOP stage), the ES predicts the FoV of the next GOP and allocates an encoding bitrate for transmitting (precaching) the video tiles within the predicted FoV. Then, during the real-time video playback of the current GOP (i.e., the intra-GOP stage), the ES observes the user's true FoV of each frame and transmits the missing tiles to compensate for the FoV prediction errors. To maximize the user's QoE under random variations of FoV and wireless channel, we propose a double-agent deep reinforcement learning framework, where the two agents operate in different time scales to decide the bitrates of inter- and intra-GOP stages, respectively. Experiments based on real-world measurements show that the proposed scheme can effectively mitigate FoV prediction errors and maintain stable QoE performance under different scenarios, achieving over 22.1% higher QoE than some representative benchmark methods.

In many remote areas lacking ground communication infrastructure support, such as wilderness, desert, ocean, etc., an integrated edge computing network in the air with edge computing nodes is an effective solution. It can provide over-the-air computing services for ground devices (GDs) with limited computing resources and battery life. In this paper, we study task offloading and resource allocation in the aerial-based mobile edge computing (MEC) system supported by a high altitude platform (HAP) and unmanned aerial vehicles (UAVs), with the goal of minimizing the GD's energy consumption. Considering that the task arrival of GDs and wireless communication quality are both stochastic and dynamic, we apply stochastic optimization techniques to transform this task offloading and resource allocation problem into two subproblems, i.e., 1) a subproblem for local computation resource allocation, and 2) a subproblem for offloading resource allocation. For the first subproblem, we use convex optimization methods to address it. For the second subproblem, we use game theory to formulate the competition of offloading resources among GDs and propose the Distributed Game-theoretical Multi-server Selection (DGMS) algorithm and the Transmission Power Allocation (TPA) algorithm. Finally, we propose a Distributed Online Task Offloading and Resource Allocation (DOTORA) algorithm and give the theoretical performance analysis of the algorithm. We perform extensive experiments, including the comparison experiments with the UAV-Only and HAP-Only framework, and the comparison experiments with other algorithms under our HAP-UAV framework. The experimental results validate our proposed framework and the DOTORA algorithm.

Due to the limited computing resource and battery capability at the mobile devices, the computation-intensive tasks generated by mobile devices can be offloaded to edge servers or cloud for processing. In this paper, we study the multi-user task offloading problem in an end-edge-cloud system, in which all user devices compete for the limited communication and computing resources. Particularly, we first formulate the offloading problem with the goal of maximizing the Quality of Experience (QoE) of the users subject to resource constraints. Since each user focuses on maximizing its own QoE, we reformulate the problem as a Multi-User Task Offloading Game (MUTO-Game). We then identify an important property that for any device, both the communication interference and the degree of computing resource competition can be upper bounded. Based on the property, we further theoretically prove that there exists at least one Nash Equilibrium offloading strategy in the MUTO-Game. We propose the Game-based Decentralized Task Offloading (GDTO) approach to obtain the Nash Equilibrium offloading strategy. Finally, we analyze the upper bound for the convergence time and characterize the performance guarantee of the obtained offloading strategy for the worst case. A series of experimental results are presented, in comparison with both the centralized optimal approach and the approximate approaches.

With the explosive development of mobile computing, federated learning (FL) has been considered as a promising distributed training framework for addressing the shortage of conventional cloud based centralized training. In FL, local model owners (LMOs) individually train their respective local models and then upload the trained local models to the task publisher (TP) for aggregation to obtain the global model. When the data provided by LMOs do not meet the requirements for model training, they can recruit workers to collect data. In this paper, by considering the interactions among the TP, LMOs and workers, we propose a three-layer hierarchical game framework. However, there are two challenges. First, information asymmetry between workers and LMOs may result in that the workers hide their types. Second, incentive mismatch between TP and LMOs may result in a lack of LMOs’ willingness to participate in FL. Therefore, we decompose the hierarchical-based framework into two layers to address these challenges. For the lower-layer, we leverage the contract theory to ensure truthful reporting of the workers’ types, based on which we simplify the feasible conditions of the contract and design the optimal contract. For the upper-layer, the Stackelberg game is adopted to model the interactions between the TP and LMOs, and we derive the Nash equilibrium and Stackelberg equilibrium solutions. Moreover, we develop an iterativeHierarchical-basedUtilityMaximizationAlgorithm (HUMA) to solve the coupling problem between upper-layer and lower-layer games. Extensive numerical experimental results verify the effectiveness of HUMA, and the comparison results illustrate the performance gain of HUMA.

Virtual reality (VR) provides users with an immersive and interactive experience through head-mounted devices, which has attracted increasing attention in recent years. Specifically, tile-based VR content transmission provides a promising approach to alleviate the conflict between limited bandwidth and high-performance requirements (e.g., high-resolution and low-delay). However, the tiling pattern affects the encoding efficiency and visual distortion of the VR content. Accounting for this issue, in this paper, a quality of experience (QoE)-aware cost minimization problem is investigated for a tile-based VR content transmission scenario. In particular, an edge server (ES) co-located at a cellular base station (BS) separates its generated VR content into several tiles according to the tiling pattern selection, and a weighted-to-spherically-uniform quality model is used to evaluate the effect of different tiling patterns on QoE. Moreover, to improve the transmission performance between the edge server and VR users (VRUs), unmanned aerial vehicles (UAVs) are leveraged as relay points to provide line of sight channels. Then, we formulate an optimization problem to minimize the sum of weighted total energy consumption and VR content distortion (i.e., QoE-aware cost) by jointly optimizing the tiling pattern selections, the VRUs-UAV grouping, partial computing decisions, and resource allocation. The formulated problem is a mixed integer non-linear programming problem, which is challenging to solve. To address this difficulty, we equivalently decompose the formulated problem into three subproblems and propose corresponding algorithms to solve them, respectively. Numerical results demonstrate that our proposed solution can effectively reduce the QoE-aware cost for VR content transmission in comparison with other baseline algorithms.

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthetic data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.

Containers have gained popularity in Edge Computing (EC) networks due to their lightweight and flexible deployment advantage. In resource-constrained EC environments, overbooking container resources can substantially improve resource utilization. However, existing work overlooks the complex interplay between resource provisioning and container scheduling, which may result in performance degradation or inefficient resource utilization due to highly dynamic resource heterogeneity in EC. To address this issue, this paper presents a novel joint Resource Overbooking and Container Scheduling (ROCS) algorithm. Our approach accounts for resource heterogeneity and the geographical distribution of edge nodes, and we formulate the ROCS problem to consolidate various costs and revenues into a single profit metric for service providers. To enhance resource utilization and maximize the profit of the service providers, we develop an efficient algorithm that operates within a hybrid action space scheme by leveraging soft actor-critic reinforcement learning. Furthermore, we introduce a risk assessment mechanism to mitigate overbooking risks. Large-scale simulations with real-world data traces demonstrate the efficacy of our proposed ROCS algorithm, validating its advantage of improving resource utilization within EC networks.

Network slicing has been envisioned to play a crucial role in supporting various vehicular applications with diverse performance requirements in dynamic Vehicle-to-Everything (V2X) communications systems. However, time-varying Service Level Agreements (SLAs) of slices and fast-changing network topologies in V2X scenarios may introduce new challenges for enabling efficient inter-slice resource provisioning to guarantee the Quality of Service (QoS) while avoiding both resource over-provisioning and under-provisioning. Moreover, the conventional centralized resource allocation schemes requiring global slice information may degrade the data privacy provided by dedicated resource provisioning. To address these challenges, in this paper, we propose a two-timescale resource management mechanism for providing diverse V2X slices with customized resources. In the long timescale, we propose a Proximal Policy Optimization-based multi-agent deep reinforcement learning algorithm for dynamically allocating bandwidth resources to different slices for guaranteeing their SLAs. Under the coordination of agents, each agent only observes its partial state space rather than the global information to adjust the resource requests, which can enhance the privacy protection. Moreover, an expert demonstration mechanism is proposed to guide the action policy for reducing the invalid action exploration and accelerating the convergence of agents. In the short-term time slot, with our proposed Cross Entropy and Successive Convex Approximation algorithm, each slice allocates its available physical resource blocks and optimizes its transmit power to meet the QoS. Simulation results show our proposed two-timescale resource allocation scheme for network slicing can achieve maximum 8.4% performance gains in terms of spectral efficiency while guaranteeing the QoS requirements of users compared to the baseline approaches.

In edge-assisted vehicular networks, containers are very suitable for deploying applications and providing services due to their lightweight and rapid deployment. To provide high-quality services, many existing studies show that the containers need to be migrated to follow the vehicles’ trajectory. However, it has been conspicuously neglected by existing work that making full use of the complex layer-sharing information of containers among multiple users can significantly reduce migration latency. In this paper, we propose a novel online container migration algorithm to reduce the overall task latency. Specifically: 1) we model the multi-user layer-aware online container migration problem in edge-assisted vehicular networks, comprehensively considering the initialization latency, computation latency, and migration latency. 2) A feature extraction method based on attention and long short-term memory is proposed to fully extract the multi-user layer-sharing information. Then, a policy gradient-based reinforcement learning algorithm is proposed to make the online migration decisions. 3) The experiments are conducted with real-world data traces. Compared with the baselines, our algorithms effectively reduce the total latency by 8% to 30% on average.

In recent years, the Internet of Things (IoT) and mobile communication technologies have developed rapidly. Meanwhile, many delay-sensitive and computation-intensive IoT services have been widely applied. Because of the limited computing resources, storage, and battery capacity of IoT devices, mobile edge computing (MEC) is emerging as a promising paradigm to help process the tasks of IoT devices. Furthermore, non-orthogonal multiple access (NOMA) has evolved as a practical approach to meeting the requirement of massive connectivity. In this paper, we study the NOMA-aided dynamic task offloading problem for the IoT, which combines task scheduling and computing resource allocation decisions. We model and formulate the problem as a stochastic optimization problem, and our goal is to minimize the system energy consumption while satisfying performance requirements. We transform the original problem into a deterministic optimization problem through stochastic optimization technology. Then, we decompose it into four sub-problems and propose the energy efficient task offloading (EETO) algorithm to solve these four sub-problems. Our proposed EETO algorithm does not rely on prior statistical knowledge related to task arrival or wireless channel conditions. Through theoretical analysis and experiment results, we demonstrate that our EETO algorithm can make a flexible trade-off between system energy consumption and performance. Additionally, the EETO algorithm can effectively decrease the system energy consumption while ensuring system performance.

Integrated sensing, communications and computing (ISCC) system has been emerged as a crucial paradigm for addressing the growing demand of emerging wireless applications that require both ultra-reliable low-latency computing and high-precision sensing. In this paper, we investigate a non-orthogonal multiple access (NOMA)-assisted integrated sensing and two-tier task offloading (ISTTO) system in which the multi-functional access point (AP) provides task offloading services for a group of edge computing users via NOMA while performing sensing towards a target. To balance the utilization of the computing resources across different tiers, the AP can further offload part of the received workloads to a group of cloudlet servers. To investigate this problem, we formulate a joint optimization of the AP’s transmit beamforming, the two-tier dedicated sensing signals, the two-tier computation offloading strategies and the associated allocations of the communication and computing resources, with the objective of minimizing the total energy consumption, while guaranteeing the required sensing performance over the total duration. Although the formulated joint optimization problem is strictly non-convex, we identify the features of its solutions and exploit a decomposition-based framework for solving it. Numerical results validate the accuracy and effectiveness of our proposed algorithm and show the performance advantages of our NOMA-assisted ISTTO scheme. Compared with several benchmark schemes, our NOMA-assisted ISTTO scheme achieves better performances in both sensing and task offloading, while suppressing the interference from undesired directions.

Integrated sensing and communication (ISAC) is a promising paradigm for supporting emerging wireless services and applications that require both high-throughput data transmission and accurate environment sensing. In this paper, we investigate the energy-efficient channel sharing aided ISAC with sensing scheduling, in which the ISAC base station (BS) can simultaneously sense multiple targets by reusing the channel of conventional cellular users. To investigate this problem, we formulate a joint optimization of the multi-target sensing scheduling, the BS’s transmitting beamforming, and its receiving beamforming for each sensing target, with the objective of maximizing the energy efficiency for radar sensing while guaranteeing each cellular user’s throughput requirement. Despite that the formulated joint optimization problem is strictly non-convex, we exploit a framework of alternating optimization and propose the corresponding algorithms for solving the problem. Specifically, we address the fractional structure of the objective function by utilizing Dinkelbach’s method. Then, we identify the convexity of the problem after semidefinite relaxation and obtain the beamforming by utilizing the Lagrange duality. Furthermore, we formulate the sensing scheduling problem as a matching game and solve it by adopting the swap matching. Numerical results validate the effectiveness of our proposed algorithms compared to some benchmark algorithms and show the performance advantage of our channel sharing aided ISAC in comparison with different schemes.

To alleviate the spectrum scarcity problem, exploiting the vast available spectrum provided by the Millimeter-Wave (mmWave) frequency band and underlaying cellular network by Device-to-Device (D2D) communication are two promising solutions. In this paper, we focus on D2D-underlaid cellular network, where the D2D communication is performed on a hybrid manner (i.e., operating over either mmWave or microwave frequency band). To secure the hybrid D2D communication against vigilant adversary, we apply covert communication to hide its presence. In particular, the D2D transmitters perform power control and communication mode switch as well as leveraging the cellular signal to avoid the transmission detection by the adversaries. We model the conflict between the D2D transmitters and adversaries in the framework of a two-stage Stackelberg game. The D2D transmitters are the leaders to maximize their utility subject to the constraints on communication covertness at the upper stage. The adversaries are the followers to minimize their detection errors at the lower stage. We apply stochastic geometry to mathematically characterize the network spatial configuration and consider a large-scale D2D-underlaid network, enabling the study from system-level perspective. We analyze the game equilibrium and obtain it by adopting a bi-level algorithm. Numerical results are provided and insightful conclusions are drawn. Compared with the conventional D2D communication, hybrid D2D communication shows a significant advantage regarding throughput under the same security requirement while weak resistance to the more stringent security requirement.

Integrated sensing and communication (ISAC) provides an emerging paradigm for enabling a variety of next-generation wireless services and applications. Due to the limited computation resources on ISAC devices and the latency as well as the reliability requirements, we propose a paradigm of mobile edge computing (MEC) aided ISAC with short-packet transmissions, where multiple ISAC devices adopt short-packet transmissions to offload their sensed radar data to an edge-server for analysis. We adopt the mutual information to measure the performance of radar sensing and quantify the reliability and latency performances for analyzing the radar-data via edge computing. We formulate an energy minimization problem that jointly optimizes the size of each short packet, the duration of each short packet, the computing-capacity allocations of edge-server, the beamforming of the radar sensing and the offloading transmission, while providing guaranteed performances for the radar sensing, the latency for radar-data analysis, and the reliability of offloading transmission. We identify the hierarchical structure of the formulated problem and divide the problem into three subproblems. For both the bottom-layer problem optimizing the computing-capacity allocations of the edge-server and the middle-layer problem optimizing the size of each short packet and the duration of each short packet, we derive their solutions analytically. Finally, for the top-layer problem optimizing the beamforming of the radar sensing and the offloading transmission, we transform it into a difference of convex (DC) problem which can be efficiently solved. We show the performance advantages of our proposed scheme. The simulation results show that our proposed algorithm can outperform the benchmark algorithms.

Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hierarchical computing offloading with the assistance of high altitude platforms (HAPs), and a hybrid transmission scheme of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) is designed for achieving efficient computation offloading. Specifically, the offshore sensing devices (SDs) initially perform computation offloading to the HAPs by forming NOMA groups, and the HAPs further offload partial workload to the onshore base station (BS) via FDMA. For efficient calculation, we aim to minimize the overall delay in completing all the workload processing of these SDs by jointly optimizing the durations of NOMA and FDMA transmission as well as the hierarchical computation offloading workload. Though the problem is in the form of non-convexity, we design an efficient SCA-based algorithm to tackle it. Finally, numerical results demonstrate the optimality and convergence of the proposed algorithm, as well as the performance gains of the proposed scheme.

Internet of Things (IoT) is one of the key applications of 5G, and the data transmission is the basis of IoT networks. In this paper, we investigate the data transmission scheme in non-orthogonal multiple access (NOMA) for IoT networks to minimize the transmission latency. In order to improve the communication efficiency between devices and the base station (BS) without more energy consumption, we deploy an energy harvesting (EH) relay node between devices and the BS for data transmitting and forwarding. Based on this networking model, we first aim at minimizing the transmission latency by jointly optimizing the transmit power of devices and the relay, forwarding ratios among devices, and forwarding time fraction when transmitting a fixed data bits from devices to the BS via the relay under the constraints of energy buffer and data buffer. Noted that the formulated problem is discrete-continuous mixed and non-convex, we apply the deep deterministic policy gradient (DDPG) algorithm in the framework of bisection searching to obtain the optimal solution. Specifically, the bisection searching is used to seek the possible transmission latency, and the DDPG is to check the feasibility of the chosen transmission latency. Finally, the effectiveness of the proposed model-data-driven algorithm is verified by comparing it with other benchmark algorithms, such as LINGO.

In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flexible semantic model, which enables edge devices to customize the transmission fidelity with different widths of the semantic model. After that, we focus on the FAST optimization problem to minimize the system energy consumption with latency and fidelity constraints. Following that, the optimal transmission strategies including the scaling factor of the semantic model, computing frequency, and transmitting power are derived for the devices. Experiment results indicate that, when compared to the baseline transmission schemes, the proposed framework can reduce up to one order of magnitude of the system energy consumption and data size for maintaining reasonable data fidelity.

Digital twin has been emerging as a promising paradigm that connects the physical entities and digital space, and continuously evolves and optimizes the physical systems. In this paper, we focus on studying the efficient data communication and computation when constructing the marine digital twin network with secrecy provisioning. Specifically, we leverage the federated learning (FL) to train the digital twin model. In the process of FL, all unmanned surface vehicles (USVs) deliver the trained models with non-orthogonal multiple access (NOMA) to the high altitude platform (HAP) for the global model aggregation. Considering the possible eavesdropping on the HAP, we utilize the chaotic sequences to spread the model information during the global model broadcasting. In this framework, we further want to minimize the total energy consumption of completing the digital twin training by jointly optimizing the global accuracy, local accuracy, HAP's transmission power, and model uploading duration subject to the secrecy provisioning and latency constraint. Despite the non-convexity, we propose a low-complexity search algorithm (LCS-Algorithm) to solve this joint optimization problem. Finally, the numerical results validate the performance of the proposed algorithm in terms of optimality and time efficiency.

The increasingly severe energy consumption caused by exploding wireless demands attracts considerable research. Remarkably, base station (BS) sleeping is a promising technique to enable the green network. A disruptive and original fully-decoupled radio access network (FD-RAN) architecture aiming at the next-generation mobile communication networks is developed, which removes the obstacles to achieving BS sleeping, i.e. deficient cooperation between BSs, coupled data-control transmission and coupled uplink-downlink transmission. In this paper, we investigate the joint user association and uplink BS sleeping considering power control in the FD-RAN with the superiority of fully decoupled architectures. Specifically, we propose an energy consumption model for the uplink FD-RAN and tackle the mixed-integer second-order cone problem to minimize the whole network energy consumption by leveraging the many-to-many swap matching theory. Extensive simulation results validate a higher energy efficiency of the uplink FD-RAN compared to the traditional cellular network and cell-free networks and demonstrate the effectiveness of our proposed algorithm.

Non-orthogonal multiple access (NOMA)-aided mobile edge computing (MEC) system can enhance the spectral-efficiency with massive tasks offloading. However, with more dynamic devices and the uncontrollable stochastic channel environment, it is even desirable to deploy appealing technique, i.e., intelligent reflecting surfaces (IRS), in the MEC system to flexibly adjust the communication environment and improve the system energy-efficiency. In this paper, we investigate the joint offloading, communication and computation resource allocation for IRS-assisted NOMA-aided MEC system. We firstly formulate a mixed integer energy-efficiency maximization problem with the system queue stability constraint. We then propose a Het-erogeneous Multi-agent Lyapunov-function-based Mixed Integer Deep Deterministic Policy Gradient (HMA-LMIDDPG) algorithm which is based on the multi-agent reinforcement learning (MARL) framework with homogeneous edge devices (EDs) and heterogeneous base station (BS) as heterogeneous multi-agent. Numerical results show that our proposed algorithms can achieve superior energy-efficiency performance to the benchmark algorithms while maintaining the queue stability.

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.

Human pose estimation is a crucial problem in computer vision, and it has numerous applications in diverse fields such as virtual reality, surveillance, human-computer interaction, and action assistance. With the advent of edge computing, it is a promising paradigm to perform real-time artificial intelligence (AI)-assisted action based on pose estimation at the edge. However, task scheduling optimization for human pose estimation in edge computing is a challenging problem, due to the limited computing resources. In this paper, we propose a novel framework for task scheduling optimization in human pose estimation at the edge. Our framework takes computing resources scheduling and task scheduling decision into account, with the objective of maximizing the quality of service (QoS) of the system. We use multiple depth cameras at different locations to build three-dimensional (3D) poses to maintain the accuracy of estimation and to assist in guiding action. We evaluate our proposed framework on a real-world dataset. The results demonstrate its effectiveness in improving system delay and estimation accuracy in comparison with benchmark methods. We also verify the sensitivity of our proposed framework, which can provide insights into optimal parameter settings for different scenarios.

In this paper, the non-orthogonal multiple access (NOMA) technology is applied in a vehicular edge computing network, in which mobile vehicles can offload partial computation tasks to the MEC server for remote execution. In this network, a long-term energy consumption minimization problem is presented by jointly optimizing the successive interference cancellation (SIC) order, transmit power, and computation resource allocation. To deal with the formulated problem, we first transform it into an equivalent instantaneous form based on the Lyapunov optimization theory. Since the transformed problem is still highly non-convex, we further decompose it into the interactive resource allocation and SIC order subproblems. For the resource allocation subproblem, we exploit its convexity through the transformation and reparameterization and then derive the optimal solution by the Karush-Kuhn-Tucker (KKT) conditions. After that, we propose a low-complexity algorithm by leveraging tabu search to obtain the suboptimal SIC order. Simulation results validate the effectiveness of the proposed algorithm and the superiority of NOMA compared to frequency division multiple access (FDMA).

The previous robust adaptive beamformer for sparse array enjoys performance improvement due to enhanced degrees-of-freedom (DOFs), while the polarization diversity of signals is not considered. The polarization diversity is an important factor that could be exploited to design a more functional beamformer. Specifically, in this paper, a cascaded sparse array (CSA) composed of diversely polarized antennas, which is polarization sensitive and has reduced mutual coupling, is proposed with a closed-form expression for the array geometry. Then, a polarimetric sparse reconstruction beamformer operating in the joint spatial and polarization domain is proposed for the CSA, and it is capable of suppressing the interferences using the information in the additional polarization domain with enhanced DOFs. As a result, it not only offers reduced costs but also improved functionality, demonstrating its potential value in future wireless communications. The polynomial rooting based joint direction-of-arrival and polarization estimation procedures are then given to support the power distribution estimation in a closed-form manner. Subsequently, the estimated steering vector of the desired signal and the reconstructed interference-plus-noise covariance matrix are combined to calculate the proposed beamformer. Numerical simulations are included to verify the potential advantages of the proposed CSA as well as the superior performance and robustness of the proposed beamformer.

Multi-camera three-dimensional (3D) pose estimation (MCTPE) has already achieved very high estimation accuracy by utilizing deep neural network (DNN) based models. However, long inference latency of the utilized complex DNN models prevents the real-time deployment of MCTPE. Device-edge collaborative inference (co-inference) is a promising way to reduce the total inference latency of MCTPE, which performs one part of the inference operations on the devices and the other part of inference operations on the edge server to fully exploit computation resources of both the devices and the edge server. Besides, there is overlap between the detection ranges of different cameras in many cases. We propose the camera-selecting device-edge collaborative inference for MCTPE (CDC-MCTPE), which discards some of the raw data from parts of the cameras to reduce the inference task size without sacrificing estimation accuracy too much. In CDC-MCTPE, we formulate the joint optimization problem with regard to the model split points and camera-selecting decisions to minimize the total inference latency and the energy consumption of all devices under the constraints of the estimation accuracy. A Random-Ordered Greedy Algorithm (ROGA) is proposed to quickly solve the problem. The simulation results show that the proposed CDC-MCTPE achieves better performance compared with three benchmarks.

With the rapid growth of marine services and applications for achieving smart oceans, advanced marine communication networks have attracted increasing interests. However, the limited resources constrain the applications in marine communication networks. In this paper, we investigate a two-tier computation offloading scheme for unmanned aerial vehicle (UAV) aided marine communication networks via game theory to improve offloading efficiency. Specifically, these underwater wireless sensors (UWSs) are deployed at the seafloor, which partially offloads their sensed information to unmanned surface vessels (USVs) for assist computing. USV acts as a relay to offload part of its data to UAVs. We formulate three optimization problems to optimize the utility of UWSs, USVs, and UAVs, respectively. To address the formulated problems, we propose efficient algorithms to derive the solutions, which can maximize the utility of each participant. Finally, simulations are conducted to validate the performance of the proposed algorithms, and the results show the efficiency and effectiveness of the proposed algorithms in comparison with the benchmark schemes.

The rapid development of communication and computing techniques enables unmanned aerial vehicles (UAVs) to provide reliable and cost-effective wireless communication and computing services from the air. Compared to the conventional fixed infrastructure, UAVs have attractive attributes, such as high flexibility and operability, and, as a result, on-demand line-of-sight connection links. Therefore, UAV-assisted wireless networks have been envisioned as a promising paradigm to achieve enhanced coverage and connectivity for future wireless communications. Meanwhile, achieving high levels of energy efficiency, sensing, communication, and computing capacities, and security and privacy are critical to the success of UAV-assisted wireless networks. In order to improve the performance of UAV-assisted wireless networks, some frameworks and mechanisms have been developed in the past few years. In this article, we provide a comprehensive survey of these developments. Specifically, we conduct a brief overview for the architecture of UAV-assisted wireless networks from four domains (i.e., framework-related, technology-related, challenge-related, and solution-related) and four aspects (i.e., sensing-related, communication-related, computing-related, and application-related). Then, the integrated sensing, communication, and computing for UAV-assisted wireless networks is introduced, followed by the characteristics and requirements. We also provide the implementation and applications of UAV-assisted wireless networks. Next, we discuss the challenges and the state-of-the-art solutions for UAV-assisted wireless networks. Finally, the advanced technologies for UAV-assisted communication and computing networks are exploited, followed by the potential research directions.

Reinforcement learning has been adopted to improve the efficiency of vehicular lane changing (LC) decisions. However, since the local vehicular data needs to be uploaded to the edge node for accomplishing the learning and decision tasks, the data and traffic safety are under critical threat. Illegal data usage or attacks can generate misleading LC decisions such as collisions or rollover, which severely degrades the traffic safety. Therefore, this article investigates a blockchain-federated reinforcement learning (FRL) approach for the LC decisions, which jointly accounts for the traffic, data, and training safety of the LC decision making. A vehicular reputation model based on the Bayesian risk situation is constructed and combined with the FRL. The proposed model can help evaluate the traffic safety, select the vehicles for participating in the FRL training, and adjust the FRL aggregation weights. The FRL can protect the data safety by enabling the exchange and aggregation of the LC decision network parameters. The blockchain can ensure the FRL training safety by recording the FRL task information. In addition, a Proof-of-Work (PoW) consensus scheme is devised to increase the FRL robustness, where the vehicles can collaboratively join the blockchain consensus and accomplish the FRL aggregation in a distributed manner. Two typical scenarios are selected for the experimental evaluation, including the highway scenario and the merging scenario. The experimental results indicate that the proposed method shows better efficiency, convergence, and message safety delivery ratio by comparing with the existing studies.

Nowadays, wireless charging has become one of the most popular technologies in Internet of Things (IoT), which makes electric devices battery free and flexible. The electromagnetic coupling in antenna design is important to push the range limits beyond the near field. Previous studies did not consider the coupling among coils and they do not work for the far-field scenarios. In this article, we design an antenna for wireless charging that can expand the charging distance from 5 to 55 cm, a ten-fold improvement compared to the near-field commercial communication distance according to the simulation results. Specifically, it is practical to take into account the coupling of every two coils when we analyze the transmit power. Therefore, we first model the near-field signal propagation with higher-order factors, and establish the relationship between the geometry parameters and the transmit power. Then, since our problem is extremely nonlinear, we design the greedy search algorithm and narrow down the feasible region to obtain the optimal parameters of the coils that can maximize the charging distance. An Ansoft-High Frequency Structure Simulator is adopted to simulate the performance of the coil models, and the results show that square coils achieve the best performance. We also discuss the best arrangement of multiple coil antennas. The impact of the signal phase is also introduced and the best combination of signal phases from different transmit coils is analyzed.

Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. Recent development of the WiFi-based ICC technology relies on detecting the variation of wireless channel state information (CSI) caused by human motions and has gained increasing popularity due to its low hardware cost, reliability under all lighting conditions, and privacy preservation in sensing data processing. To attain high estimation accuracy, existing WiFi-based ICC methods often require a large amount of labeled CSI training data samples for each application domain, i.e., a particular WiFi transceiver or background deployment. This makes large-scale deployment of the WiFi-based ICC technology across dissimilar domains extremely difficult and costly. In this article, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of the few-shot learning (FSL) paradigm consisting of two major stages: 1) source domain meta training and 2) target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain data set to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few amounts of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68%, and on average 96.37% detection accuracy in a 0–8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.

Integrated sensing and communication (ISAC), which enables the joint radar sensing and data communications, shows its great potential in many intelligent applications. In this article, we investigate the unmanned aerial vehicle (UAV)-aided ISAC with mobile-edge computing (MEC), where the ISAC device deployed on the UAV senses multiple targets with the sensing scheduling and offloads the radar sensing data to the edge-server to train a machine learning model for target recognition. The radar estimation information rate is utilized to measure the radar sensing performance. We aim to minimize a systemwise cost that includes both the UAV’s energy consumption and the data collecting time, while satisfying the requirements on both the model training error and the radar sensing performance. We formulate a joint optimization problem of the sensing scheduling, the number of time-slots, the sensing power, the communication power, and the UAV trajectory. Despite the strict nonconvexity of the formulated problem, we propose an efficient algorithm for solving it. Our algorithm jointly leverages the vertical decomposition that exploits the layered structure of the formulated problem and the horizontal decomposition that utilizes the block coordinate descent (BCD) method. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed scheme.

To provide a dependency-aware application, multiple unmanned aerial vehicles (UAVs) are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV-enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we focus on the joint interdependent task scheduling and energy balancing for MU-AEC by using a multiobjective optimization approach, which enables a decision maker to identify the optimal solutions corresponding to the best feasible tradeoffs between the two objectives. A constrained multiobjective optimization problem involving two objectives: 1) the makespan minimization of all tasks and 2) energy balancing among different UAVs, is formulated. In the solution methodology, we propose a constrained decomposition-based multiobjective evolution algorithm. To quickly seek more superior solutions, a local search mechanism by utilizing the objective information, and an improved genetic operator are proposed for remarkable performance improvements. Finally, numerical results demonstrate that compared with the baseline algorithms, our algorithm achieves both advantages in increasing the convergence and diversity of the solutions.

With the emergence of numerous new applications and the explosive growth of Internet of Things (IoT) devices in beyond 5G (B5G) networks, the massive yet delay-sensitive personalized content delivery has imposed a crucial challenge to mobile network operators (MNOs). Cooperation among MNOs for sharing the communication, caching, and computing (3C) 3-D resources in an economic yet real-time manner has provided a promising solution to address this challenge. In this article, we investigate the 3C resource sharing among multiple MNOs to realize efficiently and economically personalized content delivery, where a third-party 3C resource provider (CRP) is introduced to manage the sharing 3C resource pool. By leveraging the multidimensional contract theory, we propose an optimal 3C resource contract scheme for the CRP in a realistic asymmetric information scenario, and the appointed 3C resources in one contract will be allocated to the MNO who signs it. For each MNO, we establish a partial transcoding model to achieve the optimal orchestration on the 3C resources, where the closed-form solution of caching placement and transcoding strategy is obtained. Then, MNOs can choose the most suitable contracts to sign based on the service requirements from end users. In particular, we analyze the global incentive compatibility and feasibility of the proposed multidimensional contract approach, which is theoretically proven to achieve the optimal solution. Extensive simulation results demonstrate the efficiency of the proposed 3C resource sharing mechanism compared with other benchmark schemes. Specifically, the proposed 3C resource sharing scheme can reduce 35% delivery delay compared with the nonsharing scheme.

The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. In this article, we consider the UAV-assisted M-IoT network where USVs offload computation-intensive maritime tasks via non-orthogonal multiple access (NOMA) to the UAV equipped with the mobile-edge computing (MEC) server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation, as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of deep reinforcement learning (DRL), and the underlying algorithm is proposed to optimize the underlying multidomain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.

Most existing multi-unmanned aerial vehicle (multi-UAV) systems focus on fly path or energy consumption for task assignment, while little attention has been paid to the dynamic feature of the task, resulting in poor task completion ratio. The machine learning (ML) paradigm provides new methodologies for task assignment. However, ML methods are usually of heavy resource-consumption that cannot be directly applied in the UAV. In this paper, a digital twin (DT) assisted task assignment approach is proposed to improve the resource-intensive utilization and the efficiency of deep reinforcement learning (DRL) in multi-UAV system. The approach has a three-layer network structure which can dynamically assign tasks based on the task time constraints. Moreover, the approach is divided into two stages of initial task-assignment and task-reassignment. In the first stage, airship divides a task into multiple subtasks according to the shortest distance based on genetic algorithm and assigns them to UAVs. In the second stage, the DT can be leveraged to enable the airships to learn from the features of tasks and to generate the Q-value of the estimated value network of DRL for UAVs via pre-train of DT. The Q-value can be directly applied for deep Q-learning network (DQN) in the UAVs to reduce the training episode. Furthermore, the DQN is adopted to train task-reassignment strategy. Simulation results indicate that the DQN with DT can significantly reduce the training episode, improving 30% of the task completion ratio and 19% of the system energy efficiency compared with that of the baseline methods.

As a promising framework for distributed machine learning (ML), wireless federated learning (FL) faces the threat of eavesdropping attacks when a trained ML model is sent over a radio channel. To address this threat, we propose channel-sharing-based artificial jamming to increase the secrecy throughput of FL clients (FCs). Specifically, when an FC performs local model training, a selected device such as a sensor node (SN) not involved in the FL opportunistically accesses the FC’s channel to transmit its sensing data. In return, when the FC sends its locally trained model to the FL server (FLS), the selected SN provides artificial jamming to increase the FC’s secrecy throughput. Considering multiple FCs and SNs, we first consider a given pairing of FCs and SNs and optimize the local training time, the model uploading time, and the transmit-power of the FCs to minimize the total latency of FL training. After proving the convexity of this optimization problem, we propose an efficient algorithm to derive the semi-analytical solution. Then, we further investigate the pairing of the FCs and the SNs to minimize a system-wise cost reflecting both energy consumption and latency. The resulting problem is a bicriteria pairing problem, and we propose an efficient algorithm to compute the optimal pairing solution. Numerical results demonstrate the efficiency and performance advantage of our proposed channel-sharing-based approach with artificial jamming in comparison with different benchmark schemes.

The explosively increasing development of marine communication networks will improve the quality of service (QoS) of marine applications (e.g., ocean farm and marine tourism), which has attracted much attention from both academia and industrial in recent years. However, real-time data processing for diverse marine tasks (especially those computing-intensive and latency-sensitive tasks) is still challenging due to the limited marine communication and computing resources. Mobile edge computing (MEC) driven by powerful computing capability is envisioned as a promising solution to address the issue for resource-constrained marine services. In this paper, we propose a hybrid offshore and aerial-based multi-access edge computing scheme in marine communication networks to improve the QoS of marine applications. Specifically, we consider a scenario that both offshore base-station and unmanned aerial vehicles (UAVs) are equipped with edge-servers, and the computation workloads of unmanned surface vehicle (USV) can be simultaneously offloaded to offshore base-station and UAVs via multi-access manner. To minimize the latency of completing USV’s workloads and reduce USV’s energy consumption, we formulate a joint optimization problem to optimize the offloading decision, transmission time, and computing-rate allocation, with the objective ofMinimizing theMaximumWorkloadsLatency (MMWL). Exploiting the features of the formulated problem, we present a layered structure approach and decompose it into three subproblems. We propose efficient algorithms to obtain the optimal solutions and validate the optimality of the proposed algorithms. Finally, we provide simulation results and analysis to demonstrate the effectiveness and efficiency of the proposed scheme and algorithms in comparison with benchmark algorithms.

This paper studies optimal day-ahead scheduling of a battery swapping and charging system (BSCS) for electric vehicles (EVs) from a new perspective of multiple decision makers. It is considered that the BSCS locally incorporates the battery swapping and charging processes, and the two processes are managed by two operators, called a battery swapping operator (BSO) and a battery charging operator (BCO), respectively. Our main contribution is to propose a bilevel model where the BSO acts as the leader to receive and serve the battery swapping requests from EV users, and the BCO acts as the follower to interact with the grid and control battery charging and discharging power. We reformulate the bilevel optimization problem into an equivalent single-level problem that is a nonconvex mixed-integer nonlinear program (MINLP), and its size can easily become very large. To solve the problem efficiently, we develop a new heuristic composed of two parts, i.e., an estimation of the integer solution and an algorithm based on the alternating direction method (ADM). The results show that the proposed heuristic performs well in solving large-scale problems, providing close-to-optimal solutions quickly. In addition, compared to a social welfare maximization model that follows most existing related works, the proposed bilevel model can increase the number of swapped-out batteries by 35% and the batteries’ average energy state by 6%, improving the quality of battery swapping services.

Federated learning (FL) has been considered as a promising paradigm for enabling distributed machine learning (ML) in wireless networks. To address the limited energy capacity of wireless devices, we propose a simultaneous wireless information and power transfer (SWIPT) aided FL, in which one FL server (FLS) co-located at a cellular base station (BS) uses SWIPT to simultaneously broadcast the global model to wireless user-devices (UDs) and provide wireless power transfer to them. The UDs then use the harvested energy to train their local models and further transmit the local models to the FLS for aggregation. To improve the spectrum efficiency, we consider that the UDs form a non-orthogonal multiple access (NOMA) group for simultaneously sending their local models over the same spectrum channel. Taking the UDs’ time-varying available energy and channel conditions into account, we propose a dynamic optimization of the UDs-scheduling, the BS's transmit-power allocation, and the UDs’ power-splitting factors for SWIPT, with the objective of minimizing the long-term energy consumption while ensuring the FL convergence. The optimization problem, however, is challenging to solve since it is a finite-horizon dynamic programming problem but with an unknown stopping time, and moreover, the action space covers both discrete and continuous variables. To address these difficulties, we first execute a series of equivalent transformations to reduce the number of decision variables and then formulate the problem as a stochastic shortest path problem, based on which we propose an actor-critic deep reinforcement learning algorithm with the proximal policy optimization to efficiently learn the policy that dynamically adjusts the UDs-scheduling for FL as well as the BS's transmit-power for SWIPT. Numerical results validate the effectiveness and performance of our proposed algorithm. The results demonstrate that our proposed algorithm can effectively reduce the long-term energy consumption in comparison with two baseline algorithms.

Thanks to the wide deployment of heterogeneous radio access networks (RANs) in the past decades, the emerging paradigm of multi-access mobile edge computing, which allows mobile terminals to simultaneously offload the computation-workloads to several different edge-computing servers via multi-RANs, has provided a promising scheme for enabling the computation-intensive mobile Internet services in future wireless systems. The broadcasting nature of radio transmission, however, may lead to a potential secrecy-outage during the offloading transmission. In this paper, we thus investigate the energy-efficient multi-access mobile edge computing with secrecy provisioning. Specifically, we first investigate the scenario of one wireless device's (WD's) multi-access offloading subject to a malicious node's eavesdropping. By characterizing the WD's secrecy based throughput in its offloading transmission, we formulate a joint optimization of the WD's multi-access computation offloading, secrecy provisioning, and offloading-transmission duration, with the objective of minimizing the WD's total energy consumption, while providing a guaranteed secrecy-outage during offloading and a guaranteed overall-latency in completing the WD's workload. Despite the non-convexity of this joint optimization problem, we exploit its layered structure and propose an efficient algorithm for solving it. Based on the study on the single-WD scenario, we further investigate the scenario of multiple WDs, in which a group of WDs sequentially execute the multi-access computation offloading, while subject to a malicious node's eavesdropping. Taking the coupling effect among different WDs into account, we propose a swapping-heuristic based algorithm (that uses our proposed single-WD algorithm as a subroutine) for finding the ordering of the WDs to execute the multi-access computation offloading, with the objective of minimizing all WDs’ total energy consumption. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms. The results demonstrate that our algorithms can outperform some conventional fixed offloading scheduling scheme and randomized offloading ordering scheme.

With the increasing exploration of marine resources, various marine wireless devices have been rapidly deployed for different marine applications such as marine navigation, ocean environment monitoring, and seabed resource exploitation. However, due to long transmission delay and low data rate between marine wireless devices and the cloud, it is challenging to satisfy the service requirements of computing-intensive and delay-sensitive tasks. By migrating computing resources from cloud to the near side of ocean, the paradigm of marine edge computing networks, which integrates communication and computation capacities in marine wireless devices, is expected to support a variety of marine tasks (e.g., data collection, monitoring and processing) with low delay and high data rate. However, considering the rationality and selfishness of marine wireless devices and their limited computing-capacity, how to motivate marine wireless devices to conduct task processing becomes an important problem for improving computing efficiency. To address this issue, in this paper, we propose an incentive oriented two-tier task offloading scheme for marine edge computing networks via hybrid Stackelberg-auction game approach, with the objective of improving the offloading efficiency and maximizing marine wireless devices’ utilities. Specifically, for underwater acoustic transmission tier, we exploit multi-access task offloading scheme, in which underwater wireless sensor (UWS) uploads its workloads to an unmanned underwater vehicle (UUV) and a sea surface sink node (SN) via non-orthogonal multiple access (NOMA) transmission. We formulate the utility of each party and model the task offloading process among UWS, UUV and SN as a Stackelberg game to optimize the UWS’s offloading strategy, UUV’s and SN’s price strategies. For radio frequency transmission tier, SN can offload its partial workloads to an unmanned aerial vehicle (UAV) via frequency division multiple access (FDMA) transmission. We provide their utilities and model the offloading process between a SN and a UAV as a double auction game to optimize their bidding strategies. Extensive simulation results are provided to validate the performance of the proposed scheme. Numerical results demonstrate that the proposed algorithms can obtain the optimal solutions and increase the utilities for marine wireless devices.

Model update compression is a widely used technique to alleviate the communication cost in federated learning (FL). However, there is evidence indicating that the compression-based FL system often suffers the following two issues, i) the implicit learning performance deterioration of the global model due to the inaccurate update, ii) the limitation of sharing the same compression rate over heterogeneous edge devices. In this paper, we propose an energy-efficient learning framework, named Snowball, that enables edge devices to incrementally upload their model updates in a coarse-to-fine compression manner. To this end, we first design a fine-grained compression scheme that enables a nearly continuous compression rate. After that, we investigate the Snowball optimization problem to minimize the energy consumption of parameter transmission with learning performance constraints. By leveraging the theoretical insights of the convergence analysis, the optimization problem is transformed into a tractable form. Following that, a water-filling algorithm is designed to solve the problem, where each device is assigned a personalized compression rate according to the status of the locally available resource. Experiments indicate that, compared to state-of-the-art FL algorithms, our learning framework can save five times the required energy of uplink communication to achieve a good global accuracy.

With the rapid advance of wireless communication technologies, the promising space-air-ground integrated networks (SAGINs) are advocated to provide ubiquitous multicast transmission services for the social community constituted by a group of mobile users that have strong social ties and similar content interests. However, due to the limited yet valuable spectrum resources, the network heterogeneity, and diverse service demands of mobile users, it is challenging to efficiently allocate bandwidth for social communities with the objective of achieving satisfactory quality of experience (QoE) in SAGINs. To address this problem, in this paper, we propose a hierarchical bandwidth allocation scheme to enable high-quality multicast services for social communities in SAGINs. Specifically, we first develop a hierarchical bandwidth allocation framework. Wherein, the low earth orbit (LEO) satellite is utilized to provide space-to-air (S2A) unicast bandwidth for unmanned aerial vehicles (UAVs) at a certain price. Each UAV is employed to provide air-to-ground (A2G) multicast bandwidth for ground social communities with a certain A2G multicast bandwidth charge. We then formulate the hierarchical bandwidth allocation problem as a four-stage Stackelberg game, where the target of each participant is to maximize its own utility. Afterward, through the game analysis by the backward induction method, the existence of the Stackelberg equilibrium is proved, where the closed-form solutions on the optimal policies of both the social communities and UAVs are derived by the convex optimization method, and the optimal pricing policies of the LEO satellite is achieved by a proposed gradient descent iteration algorithm. Finally, extensive experiments are conducted to demonstrate that the proposed scheme can greatly increase the utilities of social communities while consuming a less bandwidth compared to conventional schemes.

By employing powerful edge servers for data processing, mobile edge computing (MEC) has been recognized as a promising technology to support emerging computation-intensive applications. Besides, non-orthogonal multiple access (NOMA)-aided MEC system can further enhance the spectral efficiency with massive tasks offloading. However, with more dynamic devices brought online and the uncontrollable stochastic channel environment, it is even desirable to deploy appealing technique, i.e., intelligent reflecting surfaces (IRS), in the MEC system to flexibly tune the communication environment and improve the system energy efficiency. In this paper, we investigate the joint offloading, communication and computation resource allocation for the IRS-assisted NOMA MEC system. We first formulate a mixed integer energy efficiency maximization problem with system queue stability constraint. We then propose the Lyapunov-function-based Mixed Integer Deep Deterministic Policy Gradient (LMIDDPG) algorithm which is based on the centralized reinforcement learning (RL) framework. To be specific, we design the mixed integer action space mapping which contains both continuous mapping and integer mapping. Moreover, the award function is defined as the upper-bound of the Lyapunov drift-plus-penalty function. To enable end devices (EDs) to choose actions independently at the execution stage, we further propose the Heterogeneous Multi-agent LMIDDPG (HMA-LMIDDPG) algorithm based on distributed RL framework with homogeneous EDs and heterogeneous base station (BS) as heterogeneous multi-agent. Numerical results show that our proposed algorithms can achieve superior energy efficiency performance to the benchmark algorithms while maintaining the queue stability. Specially, the distributed structure HMA-LMIDDPG can acquire more energy efficiency gain than the centralized structure LMIDDPG.

Edge computing has been envisioned as a promising approach to enable the computation-intensive yet latencysensitive marine mobile services in the fifth generation and beyond wireless networks. In this paper, we investigate the edge computing in Marine Internet of Things (M-IoT) via the assistance of unmanned surface vehicles (USVs) subject to the eavesdropping attack. In particular, we consider a scenario in which USVs are exploited to provide cooperative jamming for the communication security at the physical layer when the high altitude platform (HAP) is performing task offloading transmission. We jointly optimize the workload offloaded by HAP, the HAP's transmission power as well as each USV's interfering signal power with the objective of minimizing the total energy consumption for completing the total workloads under the latency constraint. The bisection search method is first adopted to obtain the optimal solutions to the offloaded workload and each USV's interfering signal power. Further, by exploiting the monotonicity, the polyblock outer approximation based algorithm (POA-Algorithm) is designed to obtain the HAP's optimal transmission power. Finally, numerical results validate the optimality and effectiveness of our proposed algorithm by comparing it with the results of LINGO and different jamming schemes.

With the rapid development of Internet of Things (IoTs), various sensors are deployed to collect different physical information. Smart surveillance is one of applications by analyzing the real-time video generated by camera sensors. However, due to the limited computing capability of camera sensors, running video analysis models (e.g., AlexNet and YOLO3) on camera sensors directly consumes a lot of computing time. In addition, transferring video to the remote cloud suffers a long-distance transmission latency. Fortunately, edge computing has been considered as a promising solution for enabling computation-intensive yet latency-sensitive applications at resource-constrained devices. Thanks to edge computing, camera sensors can upload video to different edge servers employed at the edge of networks for processing. Moreover, the lightweight Kubernetes for edge computing, i.e., K3s, enable a fine-grained task division and parallel computing. In this paper, we consider a heterogeneous edge cooperative video analysis, i.e., face recognition, with the objective of minimizing the processing latency. Specifically, we use a Deep Q-Learning network (DQN) to dynamically adjust the size of pieces video allocated to different edge servers connected via wireless networks. In addition, to improve the resource utilization of edge servers and reduce the processing latency, each edge server further divides the received video into multiple segments that are processed by different containers in parallel. To validate the effectiveness of our scheme, we implement a small-scale prototype system and conduct numerous experiments. Experimental results show that our proposed algorithm outperforms the other four schedule schemes by testing on the tasks of face recognition and pose recognition.

With the development of vehicular networks, the vehicle-to-everything (V2X) communication aided emergency warning is envisioned to improve the safety of driving service in intelligent transportation systems (ITS). By considering the delay sensitivity of different vehicles receiving warning information, this paper investigates the V2X communication aided emergency message dissemination. Specifically, according to the distance between the vehicle and the emergency point, we divide the vehicles in the coverage of the roadside unit (RSU) into two groups, namely, the primary priority group and the secondary priority group. Then, we formulate a joint optimization problem for content partition, user grouping and channel allocation to improve the resource utilization and the efficiency of emergency message delivery. The objective is to ensure that all vehicles in the primary priority group can reliably receive the warning messages within a fixed deadline, and meanwhile, the RSU can send as many warning messages as possible to the vehicles in the secondary priority group. Despite the nature of mixed integer and non-linear programming problem, we propose a layered approach to solve the problem. Finally, we conduct simulations to validate the efficiency and effectiveness of the proposed algorithm, compared to some benchmark algorithms.

With the rapid development of wireless communications, it is challenging to guarantee secure wireless transmission and massive connectivity in the process of data collection. In this paper, we consider an unmanned aerial vehicle (UAV)-aided Non-orthogonal Multiple Access (NOMA) communication network. Specifically, the UAV is deployed to collect the data of transmission devices (TDs) in the NOMA manner subject to the eavesdropping attack, while a group of auxiliary devices (ADs) are deployed to provide the cooperative jamming to the eaves-dropper. Driven by this networking model, we aim to maximize the total secrecy capacity by jointly optimizing the TDs’ and ADs’ power allocations and the ADs’ scheduling decisions. Considering the problem’s non-convexity, we propose a deep reinforcement learning based online optimization algorithm to maximize the total secrecy capacity. Numerical results demonstrate that the proposed algorithm can achieve considerable performance gain over some existing algorithms.

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

Digital twin (DT), which integrates physical networks and digital space by using advanced technologies of sensing, communication and computation, has been envisioned as a promising paradigm for improving the quality of service in physical systems. In this paper, we propose a federated learning (FL)-enabled DT system consisting of the physical layer and DT layer. With FL, all wireless devices (WDs) can collaborate to update a universal DT model, after the DT server cluster (DSC) aggregates all the local models sent by the WDs with non-orthogonal multiple access (NOMA). Moreover, an action model based on the DT system is also updated to optimize the operations of WDs. To increase the energy efficiency, we formulate a problem to minimize the cost of the total energy consumption of the system by optimizing the time allocation of local training, uploading the local models, generating the action model as well as broadcasting the action model and DT model. The numerical results validate the effectiveness and efficiency of our proposed algorithm.

In this article, we consider a parking lot that manages the charging processes of its parked electric vehicles (EVs). Upon arrival, each EV requests a certain amount of energy. This request should be fulfilled before the EV’s departure. It is of critical importance to coordinate the EVs’ charging rates to smooth out the load profile of the parking lot because inappropriate charging rates can lead to sharp spikes and fluctuations on the load profile, imposing negative effects on the power grid. Meanwhile, empirical studies show that many parking lots exhibit statistical patterns on EV dynamics. For example, the bulk of EVs arrives during rush hours. Therefore, in this article, we incorporate such patterns into charging rate coordination. Although the statistical patterns can be summarized from historical data, they are difficult to be analytically modeled. As a result, we adopt a model-free deep reinforcement learning approach. We also take the latest continuous charging rate control technology into consideration. The decision variables are thus continuous and a policy gradient algorithm is needed to perform reinforcement learning. Technically, we first formulate the problem as a Markov decision process (MDP) with unknown state transition probabilities. To further derive a deep policy gradient algorithm, the challenge lies in the inconsistent and state-dependent action space of the MDP model, due to the constraint to satisfy EVs’ energy demands before their scheduled departure. To tackle the challenge, we design a customized model for neural network training by extending the action space to be consistent and state independent, and revise the reward function to penalize the neural network output if it is beyond the action space of the original MDP model. With this customized model, we then develop a deep policy gradient algorithm based on the proximal policy gradient framework. Numerical results show that our algorithm outperforms the benchmarks.

With the maturity of 5G cellular communication systems and mobile-edge computing (MEC), a large number of base stations (BSs) with edge-computing servers are densely deployed. There are extensive overlapping coverage areas among the BSs in which some heavy computational tasks from Internet of Things (IoT) devices can be divided and offloaded to multiple BSs via the coordinated multipoint (CoMP) technique for parallel processing. However, it is a challenging issue about how to make proper task offloading decisions among multiple connected BSs while satisfying delay requirements of multiple devices. To address this challenge, this article presents an efficient multidevice and multi-BSs task offloading scheme with the goal of minimizing the delay for completing the tasks of the devices. By conducting quantitative analysis of local delay and offloading delay, a nonlinear and nonconvex delay optimization offloading problem, which is based on the theory of noncooperative game, is formulated. We prove the existence of Nash equilibrium by analyzing the feature of the proposed offloading problem and further propose a distributed task offloading algorithm called DOLA. Finally, simulation experiments based on real-world data set from the Melbourne CBD area of Australia are conducted to validate the efficacy of our DOLA algorithm. Comparison experiments are also carried out to demonstrate the superiority of DOLA in comparison with some existing schemes.

To facilitate the implementation of deep learning-based vehicular applications, vehicular federated learning is introduced by integrating vehicular edge computing with the newly emerged federated learning technology. In vehicular federated learning, it is widely considered that the raw data collected by vehicles have complete ground-truth labels. This, however, is not realistic and inconsistent with the current applications. To deal with the above dilemma, a semisupervised vehicular federated learning (Semi-VFL) framework is proposed. In the framework, each vehicular client uses labeled data shared by an application provider, and its own unlabeled data to cooperatively update a global deep neural network model. Furthermore, the application provider combines the multidimensional contract theory with prospect theory (PT) to design an incentive mechanism to stimulate appropriate vehicular clients to participate in Semi-VFL. Multidimensional contract theory is used to deal with the information asymmetry scenario where the application provider is not aware of vehicular clients’ 3-D cost information, while PT is used to model the application provider’s risk-aware behavior and make the incentive mechanism more acceptable in practice. After that, a closed-form solution for the optimal contract items under PT is derived. We present the real-world experimental results to demonstrate that Semi-VFL achieves the advantages in both the test accuracy and convergence speed, in comparison with existing baseline schemes. Based on the experimental results, we further perform the simulations to verify that our incentive mechanism is efficient.

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.

Federated learning (FL) has been considered as a promising paradigm for enabling distributed training/learning in many machine-learning services without revealing users’ local data. Driven by the growing interests in exploiting FL in wireless networks, this paper studies the Non-orthogonal Multiple Access (NOMA) assisted FL in which a group of end-devices (EDs) form a NOMA cluster to send their locally trained models to the cellular base station (BS) for model aggregation. In particular, we consider that the BS adopts wireless power transfer (WPT) to power the EDs (for their data transmission and local training) in each round of FL iteration, and formulate a joint optimization of the BS’s WPT for different EDs, the EDs’ NOMA-transmission for sending the local models to the BS, the BS’s broadcasting of the aggregated model to all EDs, the processing-rates of the BS and EDs, as well as the training-accuracy of the FL, with the objective of minimizing the system-wise cost accounting for the total energy consumption as well as the FL convergence latency. In spite of the strict non-convexity of the joint optimization problem, we analytically characterize the BS’s and all EDs’ optimal processing-rates, based on which we propose a layered algorithm for finding the optimal solutions for the joint optimization problem via exploiting monotonic optimization. Numerical results validate that our algorithm can achieve the optimal solution as LINGO’s global-solver (i.e., a commercial optimization package) while significantly reducing the computation-time. Moreover, the results also demonstrate that our NOMA assisted FL can reduce the system cost compared to the benchmark FL scheme with the fixed local training-accuracy by more than 70% and the conventional frequency division multiple access (FDMA) based FL by 78%.

With the maneuverability and mobility control of unmanned aerial vehicle (UAV), carrying mobile edge computing (MEC) servers on UAVs is able to effectively alleviate the explosive growth of data traffic pressure. However, UAV adopts line-of-sight transmission which has broadcasting characteristics. Malicious eavesdroppers can easily take advantage of the characteristics to eavesdrop information during the UAV edge computing. Therefore, the security of the UAV-MEC systems is a challenging problem. This article proposes a secure communication scheme for the dual-UAV-MEC system. In the proposed scheme, UAV server assists ground users in calculating the offloading tasks. In order to reduce the eavesdropping of offloading information by UAV eavesdropper, jammer sends interference signals on the ground. We aim to maximize the user's minimum secure calculation capacity by optimizing resources and trajectory of the UAV server. We first transform the optimization problem into a tractable form through mathematical methods and use successive convex approximation and block coordinate descent algorithms to solve it in an iterative manner. The final numerical results show that, compared with the benchmark schemes, the method proposed in this article effectively increases the secure calculation capacity of the system.

Heart disease seriously threatens human life due to high morbidity and mortality. Accurate prediction and diagnosis become more critical for early prevention, detection, and treatment. The Internet of Medical Things and artificial intelligence support healthcare services in heart disease monitoring, prediction, and diagnosis. However, most prediction models only predict whether people are sick, and rarely further determine the severity of the disease. In this article, we propose a machine learning based prediction model to achieve binary and multiple classification heart disease prediction simultaneously. We first design a Fuzzy-GBDT algorithm combining fuzzy logic and gradient boosting decision tree (GBDT) to reduce data complexity and increase the generalization of binary classification prediction. Then, we integrate Fuzzy-GBDT with bagging to avoid overfitting. The Bagging-Fuzzy-GBDT for multiclassification prediction further classify the severity of heart disease. Evaluation results demonstrate the Bagging-Fuzzy-GBDT has excellent accuracy and stability in both binary and multiple classification predictions.

The air ground integrated networks can leverage unmanned aerial vehicle (UAV) communications to tackle the ever-increasing and unbalanced traffic load in future communication systems. This paper investigates the UAV enabled traffic offloading problem in air ground integrated networks with mixed user traffic. The problem jointly maximizes the system load balance and the total UAV reward, which can be formulated under a two-layer network graph model. In the cellular network graph, the association between the delay-sensitive users and the access points (APs) as well as the association between the UAVs and the APs are formulated. In the UAV network graph, the association between the delay-insensitive users and the UAVs is formulated. By observing the coupling relationship of the decision variables, we decouple the problem into three sub-problems and solve the first two sub-problems with reduced complexity. Then, we devise a Deep Neural Network (DNN) empowered genetic algorithm to solve the last sub-problem. The DNN can be leveraged to filter out the non-optimal solutions in the initialization operator of the genetic algorithm for improving the efficiency. Performance comparisons are provided between the proposed traffic offloading scheme and the existing ones, which validate the advantages of the DNN empowered genetic algorithm regarding its convergence, accuracy, and robustness.

Equipped with machine learning (ML) models, unmanned aerial vehicle (UAV) swarms can execute various applications like surveillance and target detection. However, the connections between UAVs and cloud servers cannot be guaranteed, especially when executing massive data. Thus, traditional cloud-centric approach will not be suitable, since it may cause high latency and significant bandwidth consumption. In this work, we propose a federated learning (FL) framework via non-orthogonal multiple access (NOMA) for a UAV swarm which is composed of a leader-UAV and a group of follower-UAVs. Specifically, each follower-UAV updates its local model by using its collected data, and then all follower-UAVs form a NOMA-group to send their respectively trained FL parameters (i.e., the local FL models) to the leader-UAV simultaneously. We formulate a joint optimization of the uplink NOMA-transmission durations, downlink broadcasting duration, as well as the computation-rates of the leader-UAV and all follower-UAVs, aiming at minimizing the latency in executing the FL iterations until reaching a specified accuracy. Numerical results are presented to verify the effectiveness of our proposed algorithm, and demonstrate that the proposed algorithm can outperform some baseline strategies.

In this article, we study the application of unmanned aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing seamless coverage and improving system performance in the next-generation wireless networks. To this end, we propose a reinforcement learning-based approach to plan the route of UAV to collect sensor data from sensor devices scattered in the physical environment. Specifically, the physical environment is divided into multiple grids, where one spot for UAV hovering as well as the wireless charging of UAV is located at the center of each grid. Each grid has a spot for the UAV to hover, and moreover, there is a wireless charger at the center of each grid, which can provide wireless charging to UAV when it is hovering in the grid. When the UAV lacks energy, it can be charged by the wireless charger at the spot. By taking into account the collected data amount as well as the energy consumption, we formulate the problem of data collection with UAV as a Markov decision problem, and exploit Q-learning to find the optimal policy. In particular, we design the reward function considering the energy efficiency of UAV flight and data collection, based on which Q-table is updated for guiding the route of UAV. Through extensive simulation results, we verify that our proposed reward function can achieve a better performance in terms of the average throughput, delay of data collection, as well as the energy efficiency of UAV, in comparison with the conventional capacity-based reward function.

The Internet-of-Things (IoT) platform is faced with critical challenges posed by the conflict between resource-hungry IoT applications and resource-constrained IoT devices. Mobile-edge computing provides a promising solution by allowing IoT devices to offload their computation to nearby edge servers to enable fast and energy-efficient data processing. In this article, we study a scenario, where two IoT users (IoT devices) offload their computation workloads to an edge server with hybrid nonorthogonal multiple access (NOMA)-orthogonal multiple access (OMA) transmission. The hybrid multiple access transmission incorporates three offloading methods, namely, hybrid NOMA, pure NOMA, and pure OMA. The offloading-method selection, together with user selection, which determines the roles played by different IoT users in data transmission, comprises our offloading strategy and is optimized to minimize the maximal offloading latency of the two IoT users. By exploiting the method of successive convex approximation, we design an efficient algorithm to solve the complicated nonconvex problem and rigorously prove the convergence of our algorithm. Extensive numerical tests show that our scheme can always help IoT users to flexibly choose the best offloading strategy. Inspired by experimental observations, we analytically establish the criteria for the three offloading methods. We show that pure OMA transmission is never the best offloading method, except in some extreme cases that rarely occur in practice, while pure NOMA transmission is the most desirable offloading method in terms of latency minimization. We then propose detection approaches for the best offloading strategy with both offloading-method selection and user selection under certain system settings. The user selection is applied to avoid the pure OMA transmission and encourage the pure NOMA transmission.

The deep penetration of electric vehicles (EVs) into the transportation section and the associated charging management has yielded a critical issue, namely, how to efficiently store the generated charging records. In this article, we investigate the cost-efficient charging-record storage scheme by exploiting blockchain (BC). Accounting for the operational cost due to the consensus process via the practical Byzantine fault tolerance (PBFT) protocol, we model the associated cost for storing the charging records via an ideal multiblockchain system and formulate a joint optimization of the storage selection (i.e., either storing the charging record locally or selecting one of the BCs for storing the charging record) and server-node allocation for each BC, with the objective of minimizing a systemwise cost. Despite the nature of the complicated mixed binary and integer programming problem, we exploit the decomposition structure and propose a layered algorithm (i.e., the bottom subproblem for determining the optimal storage selection and the top problem for finding the server-node allocation) to solve it. For the bottom subproblem, we exploit the nature of minimum weighted matching of the problem and propose a distributed auction-based algorithm for computing the optimal storage selection. With the optimal solution from the subproblem, we further propose an annealing-based algorithm to determine the server-node allocation for each BC. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our cost-efficient charging-record storage scheme via BC.

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED's offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED's offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED's latency-requirement and the selected WDs' required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs' delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD's optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA.

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting, and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this article, an architecture design of smart agriculture is first proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers' pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.

Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key approaches for enabling computation-intensive yet delay-sensitive services in future industrial Internet of Things (IoT). In this article, we exploit nonorthogonal multiple access (NOMA) for computation offloading in MA-MEC and propose a joint optimization of the multiaccess multitask computation offloading, NOMA transmission, and computation-resource allocation, with the objective of minimizing the total energy consumption of IoT device to complete its tasks subject to the required latency limit. We first focus on a static channel scenario and propose a distributed algorithm to solve the joint optimization problem by identifying the layered structure of the formulated nonconvex problem. Furthermore, we consider a dynamic channel scenario in which the channel power gains from the IoT device to the edge-computing servers are time varying. To tackle with the difficulty due to the huge number of different channel realizations in the dynamic scenario, we propose an online algorithm, which is based on deep reinforcement learning (DRL), to efficiently learn the near-optimal offloading solutions for the time-varying channel realizations. Numerical results are provided to validate our distributed algorithm for the static channel scenario and the DRL-based online algorithm for the dynamic channel scenario. We also demonstrate the advantage of the NOMA assisted multitask MA-MEC against conventional orthogonal multiple access scheme under both static and dynamic channels.

Powering cellular networks with hybrid energy supplies is not only environment-friendly but can also reduce the on-grid energy consumption, thus being emerging as a promising solution for green networking. Intelligent management of spectrum and power can increase the network utility in cellular networks with hybrid energy supplies, usually at the cost of higher energy consumption. Unlike prior studies on either the network utility maximization or on-grid energy cost minimization, this paper studies the joint spectrum and power allocation problem that maximizes the system revenue in a heterogeneous small-cell network with hybrid energy supplies. Specifically, the system revenue is considered as the difference between the network utility and on-grid energy cost. By developing the convexity of the optimization problem through transformation and reparameterization, we propose a joint spectrum and power allocation algorithm based on the primal-dual arguments to obtain the optimal solution by iteratively solving the primal and dual sub-problems of the convex optimization problem. To solve the primal sub-problem, we further propose the Lagrangian maximization based on the alternating direction method of multipliers (ADMM), and derive the optimal solution in the closed-form expression at each iteration. It is shown that the proposed joint spectrum and power allocation algorithm approaches the global optimality at the rate of 1=n with n being the number of iterations. Also, the proposed ADMM-based Lagrangian maximization algorithm approaches the primal optimal solution with the time complexity of O(1=εr) iterations with εrbeing the termination parameter. Simulation results show that in comparison with the power control with equal frequency allocation algorithm and frequency allocation with equal power allocation algorithms the proposed algorithm increases the system revenue by over 20 and 60 percent without consuming more on-grid energy when the proportional fairness utility and the weighted sum rate utility are considered with the approximate system parameter settings, respectively. Meanwhile, in comparison with the full frequency reuse case, the proposed algorithm increases the system revenue by 20 percent at least in terms of the weighted sum rate utility, although it achieves the similar system revenue when considering the proportional fairness utility. Simulation results also show that our proposed algorithm can perform well under the realistic fast fading channel conditions.

Mobile edge computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for terminal devices. In this paper, we investigate multi-user computation off loading in MEC and propose a joint optimization of off loading decisions, bandwidth and computation-resource allocations, with the objective of minimizing the total latency for completing all users' tasks. Due to the non-convexity of the formulated joint optimization problem, we identify its layer structure and decompose it into two problems, i.e·, a sub-problem and a top-problem. For the sub-problem, we propose a bisection-search based algorithm to efficiently find the optimal off loading solutions under a given feasible top-problem solution. Then, we use a linear-search based algorithm to obtain the optimal solution of the top-problem. Numerical results are provided to validate our proposed algorithm for minimizing the total latency in MEC-based multi-user computation off loading. We also demonstrate the advantage of our proposed algorithm in comparison with the conventional multi-user computation off loading schemes.

Mobile edge computing (MEC) has been considered as a promising approach for enabling computation-intensive Internet services in future wireless systems. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted MEC, in which edge-computing users (EUs) adopt NOMA to simultaneously offload part of their computation-workloads to the edge-server (ES). To improve the spectrum-efficiency, we consider a paradigm of underlaying device-to-device (D2D) communications, namely, the EUs reuse a cellular user's (CU's) licensed channel for offloading transmission. We firstly characterize the transmit-powers of EUs and CU in this D2D approach, and then formulate a joint optimization of the EUs' computation- workloads offloading and the ES's computation-resource allocation, with the objective of minimizing the latency in completing the EUs' tasks. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose an efficient algorithm for computing the optimal solution. Numerical results are provided to validate the effectiveness and efficiency of our proposed NOMA assisted MEC via the D2D sharing1.

With the rapid advancement of electric vehicle (EV) technology, EV has been emerging as a promising transportation due to the low carbon emission. However, the frequent and long time charging is indispensable to continue travelling. During peak hours, EVs further spend long time on the path routing because of the traffic congestion and queuing in the charging stations. Therefore, we study the EV charging scheduling problem that minimizes the total elapsed time which includes charging time for EVs through jointly optimizing the charging path routing and charging station selection in this paper. Considering the NP-hardness of this optimization problem, we propose an efficient EV charging scheduling method to obtain the optimal solution based on crowd sensing through considering the remaining energy in the battery, traffic condition, and the queue length of charging stations. Simulation results demonstrate that the proposed backtracking method based on crowd sensing can effectively reduce the total elapsed time, in comparison with the greedy algorithm.

Non-orthogonal multiple access (NOMA) has been considered as a promising scheme for enabling ultra-high throughput transmission and massive-connectivity in next generation wireless systems. In this paper, we investigate the secrecy-based NOMA transmission for encountering the eavesdropping attack. Exploiting the NOMA-users simultaneous transmission as an artificial jamming, we investigate the joint optimization of NOMA-users' power allocations and the secrecy-provisioning, with the objective of the effective secure throughput of NOMA-users while ensuring the fairness among them. Despite the non-convexity of the formulated joint optimization problem, we explore its hidden feature and design a search algorithm to compute the optimal solution. Numerical results are provided to validate the performance of our proposed algorithm.1

This study presents a mobile edge computing (MEC)‐enabled UAV communication system, where a number of UAVs are served by terrestrial base stations (TBSs) equipped with computation resource in the non‐orthogonal multiple access manner. Each UAV has to offload its computing tasks to the proper TBS due to the limited energy supply. For this, the authors aim at minimising the sum of transmission energy of UAVs and computation energy of TBSs through jointly optimising the UAV transmit power, computation resource allocation, and UAV grouping. Considering the non‐convexity of this optimisation problem, they obtain the optimal solution in the coupled steps: the convex resource allocation optimisation and the combinatorial UAV grouping optimisation. By exploiting the convex nature of the resource allocation optimisation problem, they obtain the optimal transmit power and computation allocation based on the KKT conditions and the idea of gradient descent method when considering a single TBS. Then, they adopt the simulated annealing to obtain the optimal UAV grouping and TBS selection based on the proposed resource allocation optimisation algorithm. Finally, simulation results show that the proposed joint optimisation of transmit power, computation resource allocation, and UAV grouping can effectively reduce the energy consumption of MEC‐aware UAV communication system.

Mobile edge computing (MEC) has recently become a promising paradigm to meet the increasing computing requirement of mobile devices, and hybrid energy supply has been considered as an effective approach for saving the energy consumption of the MEC system and making it environmentally friendly. In particular, the joint task scheduling and energy management (TSEM) scheme plays a crucial role in reaping the benefits of MEC with hybrid energy supply. In this article, we focus on jointly optimizing the TSEM decisions to maximize the utility of the MEC system which accounts for both the computation throughput and the fairness among different cells, by formulating a stochastic optimization problem subject to the constraints of queue stability and energy budget. We transform the formulated problem into a deterministic problem and then decouple it into four independent subproblems, which can be solved in a distributed manner without future system statistical information. An online TSEM algorithm is developed to derive the optimal solutions to these subproblems. Mathematical analysis shows that TSEM can achieve a close-to-optimal system utility and realize the utility-queue tradeoff. The experimental results validate the advantages of TSEM in improving the system utility and stabilizing the queue length.

The accurate prediction of vehicle state based on the data acquired by the vehicular networking system plays an important role in improving traffic safety in the transportation section. However, it is difficult to accurately predict the vehicle state due to the highly dynamic road environment and various drivers' behaviors. To this end, in this article, we propose a two-level quantized adaptive Kalman filter (KF) algorithm based on the autoregressive moving average (MA) model to predict the vehicle state (including the moving direction, driving lane, vehicle speed, and acceleration). First, we propose a vehicular networking system to acquire the vehicle data by exchanging traffic data between the onboard unit and the roadside unit (RSU). Then, we predict the vehicle state at the edge cloud server (ECS) equipped at the RSU. Specifically, we utilize the autoregressive MA model to predict vehicle acceleration at the next moment. Then, the predicted vehicle acceleration is used as an input variable of the adaptive KF model to predict the vehicle location and speed at the next moment, in which we quantify the predicted vehicle location to the moving direction and the driving lane. Finally, the ECS broadcasts the predicted state to other RSUs. Through the communication with the road unit, all vehicles moving at the intersection can share vehicles states each other. In this doing, we can efficiently improve traffic safety in the intersection. We provide numerical simulations to validate the effectiveness of the autoregressive MA model used for predicting acceleration. Then, we evaluate the efficiency of the proposed two-level quantized adaptive KF algorithm. Compared with five conventional prediction algorithms, our proposed algorithm can improve the speed prediction accuracy by 90.62%, 89.81%, 88.91%, 82.76%, and 70.77%, respectively, which implies that our algorithm is a promising scheme for predicting the vehicle state in vehicular networks.

The past decades have witnessed an explosive growth of the Internet of Things (IoT) services requiring intensive computation resources. The conventional IoT devices, however, are usually equipped with very limited computation resources, which results in degraded quality of experience when executing the resource-hungry applications. Mobile edge computing (MEC), which enables smart terminals (STs) to offload parts of their computation workloads to the edge servers located at cellular base stations (BSs), has provided a promising approach to address this issue. In this article, we investigate the nonorthogonal multiple access (NOMA)-enabled multiaccess MEC. Specifically, by exploiting the advanced NOMA, an ST can simultaneously offload its computation workloads to different edge servers (ESs), which thus reduces the overall delay in completing the ST's computation workloads. To study this problem, we formulate a joint optimization of the computation resource allocations at the ESs, the ST's offloaded workloads and its radio resource allocations for NOMA transmission, with the objective of minimizing a system wise cost that accounts for the overall delay in finishing the ST's total computation workload and the total computation resource usage cost at the ESs. Despite the nonconvexity of the joint optimization problem, we exploit its layered structure and propose an efficient layered algorithm to find the optimal solution. By exploiting the optimal offloading solution of a single ST, we further investigate the scenario of multiple STs and propose two algorithms to determine the optimal grouping among different ESs for serving the STs, with one algorithm aiming at minimizing the total cost of all STs and the other algorithm aiming at determining the Nash stable grouping for the ESs. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed NOMA-enabled multiaccess computation offloading.

In this paper, we study a general transactive energy (TE) retailing problem in smart grids: a TE retailer (e.g., a utility company) publishes the energy price, which may vary over time. TE customers arrive in an arbitrary manner and may choose to either purchase a certain amount of energy based on the posted price, or leave without buying. Typical examples of such a setup include a transactive electric vehicle charging platform, or a general market-based demand-side management program, etc. We consider the setting where the customer arrival information is unknown (i.e., without prediction), and focus on maximizing the social welfare of the TE system through a posted-price mechanism (PPM) that runs in an online fashion with causal information only. We quantify the performance of the proposed PPM in the competitive analysis framework, and show that our proposed PPM is optimal in the sense that no other online mechanisms can achieve a better competitive ratio. We evaluate our theoretic results for the case of transactive electric vehicle charging. Our extensive experimental results show that the proposed PPM is competitive and robust against system uncertainties, and outperforms several existing benchmarks.

We study a general online combinatorial auction problem in algorithmic mechanism design. A provider allocates multiple types of capacity-limited resources to customers that arrive in a sequential and arbitrary manner. Each customer has a private valuation function on bundles of resources that she can purchase (e.g., a combination of different resources such as CPU and RAM in cloud computing). The provider charges payment from customers who purchase a bundle of resources and incurs an increasing supply cost with respect to the totality of resources allocated. The goal is to maximize the social welfare, namely, the total valuation of customers for their purchased bundles, minus the total supply cost of the provider for all the resources that have been allocated. We adopt the competitive analysis framework and provide posted-price mechanisms with optimal competitive ratios. Our pricing mechanism is optimal in the sense that no other online algorithms can achieve a better competitive ratio. We validate the theoretic results via empirical studies of online resource allocation in cloud computing. Our numerical results demonstrate that the proposed pricing mechanism is competitive and robust against system uncertainties and outperforms existing benchmarks.

Driven by the explosive growth in computation-intensive applications in future 5G networks and industries, mobile edge computing (MEC), which enables smart terminals (STs) to offload their computation workloads to nearby edge servers (ESs) in radio access networks, has attracted increasing attention. In this article, we investigate the energy-efficient multitask multiaccess MEC via nonorthogonal multiple access (NOMA). Exploiting NOMA, an ST with multiple tasks can offload the respective computation workloads of different tasks to different ESs simultaneously. To study this problem, we adopt a two-step approach. Specifically, we first consider a given task-ES assignment and formulate a joint optimization of the tasks' computation offloading, local computation-resource allocation, and the NOMA-transmission duration, with the objective of minimizing the ST's total energy consumption for completing all tasks. Next, based on the optimal offloading solution for the given task-ES assignment, we further investigate how to properly assign different tasks to the ESs for further minimizing the ST's total energy consumption. For both the formulated problems, we propose efficient algorithms to compute the respective solutions. Numerical results are provided to validate the effectiveness of our proposed algorithms. The results also show that our proposed NOMA-enabled multitask multiaccess computation offloading can outperform conventional orthogonal multiple access based offloading scheme, especially when the tasks have heavy computation-workload requirements and stringent delay limits.

In recent years, the 3rd generation partnership project (3GPP) has approved the narrowband Internet of Things (NB-IoT) system to support the low-data-rate machine- type communications. With the rapid development of NB- IoT technology, the NB-IoT traffic volumes have been experiencing the unprecedented growth. To this end, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been proposed as promising technologies for the NB-IoT system. In this paper, our goal is to minimize the total energy consumption subject to the computation capacity and execution latency limits by jointly optimizing the transmit power, computation resource allocation, and successive interference cancellation (SIC) ordering. Considering the NP-hardness of the joint optimization problem, we obtain the optimal solution in the coupled steps: the resource allocation optimization and the combinatorial SIC ordering optimization. By exploiting the convex nature of the resource allocation optimization problem, we obtain the optimal transmit power and computation resource allocation based on the KKT conditions and the idea of gradient descent method when fixing the SIC ordering. Considering the combinatorial optimization of SIC ordering, we further propose a tabu search based SIC ordering algorithm on the basis of the proposed resource allocation optimization algorithm. Finally, simulation results demonstrate that the proposed joint optimization of transmit power, computation resource allocation, and SIC ordering in the context of NOMA can effectively reduce the total energy consumption of MEC- aware NB-IoT system, in comparison with the frequency- division multiple access technique.

Powering relays with harvested renewable ambient energy has been emerging as a promising solution to reduce the on-grid energy consumption and greenhouse gas emissions in green relaying communication networks. In this paper, we study the joint time scheduling and power allocation problem for the Decode-and-Forward energy-harvesting relay communication network. Particularly, our goal is to maximize the end-to-end throughput by a deadline subject to the finite data and energy storage. Due to the multi-slot optimization, the traditional deep reinforcement learning (RL) framework cannot be directly applied to obtain the optimal solution of maximizing the end-to-end throughput by a deadline in the online manner. To this end, we explore a novel deep reinforcement learning framework consisting of multiple computation units to obtain the online time scheduling and power allocation based on the current causal knowledge of energy arrivals and channel fading at each time slot. Simulation results show that the proposed deep reinforcement learning based algorithm can achieve more than 90% of maximum end-to-end throughput.

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have been emerging as promising techniques in narrowband Internet of Things (NB-IoT) systems to provide ubiquitously connected IoT devices with efficient transmission and computation. However, the successive interference cancellation (SIC) ordering of NOMA has become the bottleneck limiting the performance improvement for the uplink transmission, which is the dominant traffic flow of NB-IoT communications. Also, in order to guarantee the fairness of task execution latency across NB-IoT devices, the computation resource of MEC units has to be fairly allocated to tasks from IoT devices according to the task size. For these reasons, we investigate the joint optimization of SIC ordering and computation resource allocation in this paper. Specifically, we formulate a combinatorial optimization problem with the objective to minimize the maximum task execution latency required per task bit across NB-IoT devices under the limitation of computation resource. We prove the NP-hardness of this joint optimization problem. To tackle this challenging problem, we first propose an optimal algorithm to obtain the optimal SIC ordering and computation resource allocation in two stages: the convex computation resource allocation optimization followed by the combinatorial SIC ordering optimization. To reduce the computational complexity, we design an efficient heuristic algorithm for the SIC ordering optimization. As a good feature, the proposed low-complexity algorithm suffers a negligible performance degradation in comparison with the optimal algorithm. Simulation results demonstrate the benefits of NOMA in reducing the task execution latency.

Mobile edge computing (MEC), which enables smart terminals to actively offload computation workloads to computational servers deployed at the edge of networks, has provided an efficient approach to address the intensive computation requirement in mobile Internet applications. In this paper, we investigate the delay-aware computation offloading via MEC for Internet of Things (IoT) with secrecy provisioning. Specifically, we consider a scenario where a malicious eavesdropper intentionally overhears the IoT devices’ offloaded computational data. Taking into account the secrecy outage due to the eavesdropper’s overhearing, we formulate a joint optimization of the secrecy-provisioning, computation offloading, and radio resource allocation (including time and power allocations), with the objective of minimizing the overall delay in finishing the computation requirement of the IoT device. Despite the nonconvexity of the joint optimization problem, we propose an efficient algorithm to compute the optimal computation offloading solution. By exploiting the optimal offloading decision of each IoT device, we further consider the scenario of a group of IoT devices offloading computation workloads to the edge server, and investigate how the edge server optimally selects the devices for providing the computation offloading service while subject to the limited energy budget and the time-slot budget of the edge server. We propose an efficient algorithm to find the optimal selection of the devices. We present extensive numerical results to validate the effectiveness of our proposed algorithms and show the impact of the secrecy requirement.

Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users' offloading decision process under dynamic environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under dynamic environment.

Narrow-band Internet of Things (NB-IoT), one of the emerging paradigms of low power wide area networks (LPWAN) for Internet of Things (IoT), has been envisioned as a promising solution to enable massive connectivity, cost-efficient, and highly reliable Internet of Thing (IoT) systems in future smart cities. In this work, we build up an indoor environment-temperature monitoring system based on NB-IoT. We present a detailed design of our system and illustrate the key technologies. Based on our system and the collected data (i.e., the temperature data), we further design an abnormality-detection mechanism based on the support vector machine (SVM). We provide experimental results to show the performance of our designed system and the proposed abnormality-detection mechanism.

The emerging non‐orthogonal multiple access (NOMA) has been considered as a promising scheme to reach the goals of 5G cellular systems. By enabling a group of mobile users (MUs) to share a same frequency channel and adopting the successive interference cancellation to mitigate the co‐channel interference, NOMA can improve the spectrum efficiency compared with the orthogonal multiple access (OMA). This study proposes a joint optimisation scheme of the channel bandwidth and the transmit‐power allocations for the NOMA downlink transmission, which aims at minimising the overall resource consumption cost including both the spectrum consumption and the power consumption, while satisfying the MUs' traffic requirements. In spite of the non‐convexity nature of the joint optimisation problem, this study characterises the connection between the channel bandwidth and the associated transmit powers for the MUs. Based on this connection, this study transforms the joint optimisation problem into an equivalent bandwidth optimisation problem, and further proposes an efficient algorithm to compute the optimal bandwidth allocation (which enables us to derive the corresponding transmit powers for the MUs). Extensive numerical results are provided to validate the proposed algorithm and the advantage of the proposed joint channel bandwidth and power allocations for the NOMA transmission.

As cellular networks are evolving toward the fifth generation/long-term evolution systems, cellular radio access networks are expected to provide high throughput and reliable connectivity for massive number of smart devices (SDs), which leads to the emerging narrowband Internet of Things (NBIoT), a cellular-assisted low-power wide area IoT system. Driven by the potential critical missions, such as transportation safety and video surveillance that require high throughput and lowpower consumption, we investigate the small-cell assisted traffic offloading for NB-IoT systems. Taking into account the offloading through small cells operating on unlicensed bands, we account for the secrecy-outage issue in which some malicious eavesdroppers might intentionally overhead the offloaded data delivered to small cells. We first formulate a joint traffic scheduling and power allocation problem to minimize the total power consumption of SDs, while satisfying both the traffic throughput requirement and secrecy-requirement. Despite the nonconvexity of the problem, we propose an efficient algorithm to compute the optimal offloading solution. With the per-SD's optimal offloading solution, we further investigate a multi-SDs multi access-points (APs) scenario, in which different SDs select different APs for providing offloading service to minimize the overall offloading-cost for all SDs. Specifically, we formulate an optimal SD-AP pairing problem to find the optimal pairing between the SDs and APs. Numerical results have been provided to validate our proposed algorithm and show the performance gain of our proposed traffic offloading for the NB-IoT systems.

The rapid growth of mobile traffic has heavily overloaded the cellular networks, making it increasingly desirable to offload mobile users' (MUs') traffic to small-cell networks. In this paper, we study the MUs' optimal uplink traffic offloading scheme based on the new paradigm of small-cell dual-connectivity (DC). Through DC, an MU can flexibly schedule its traffic between a macro-cell base station (BS) and a small-cell access point (AP) via two different radio interfaces. To optimize the overall network radio resource usage, we jointly optimize the BS' bandwidth allocation as well as the MUs' traffic scheduling and power allocation. Specifically, for reducing the bandwidth usage, the BS prefers to allocate the MUs small amount of bandwidth to encourage the MUs to utilize the small-cell networks. However, excessive traffic offloading can lead to severe interferences among MUs, which increase the MUs' power consumption. Hence, our joint optimization strikes a proper balance between these two aspects. Despite the non-convexity of the proposed joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. The key idea is to exploit the layered-structure of the joint optimization problem, and decompose it into the BS' bandwidth allocation problem (on the top-level) and the MUs' traffic scheduling and power allocation problem (as a subproblem). Such a decomposition enables us to exploit the hidden convexity of the MUs' problem and the monotonic structure of the BS' problem for an effective algorithm design. Numerical results show that our proposed algorithm can achieve the global optimum solution with significantly reduced computational time. Moreover, the proposed traffic offloading scheme can significantly reduce the overall system cost, in comparison with using the fixed bandwidth allocation or traffic scheduling schemes.

The emerging non-orthogonal multiple access (NOMA), which enables mobile users (MUs) to share same frequency channel simultaneously, has been considered as a spectrum-efficient multiple access scheme to accommodate tremendous traffic growth in future cellular networks. In this paper, we investigate the NOMA downlink relay-transmission, in which the macro base station (BS) first uses NOMA to transmit to a group of relays, and all relays then use NOMA to transmit their respectively received data to an MU. In specific, we propose an optimal power allocation problem for the BS and relays to maximize the overall throughput delivered to the MU. Despite the non-convexity of the problem, we adopt the vertical decomposition and propose a layered-algorithm to efficiently compute the optimal power allocation solution. Numerical results show that the proposed NOMA relay-transmission can increase the throughput up to 30 percent compared with the conventional time division multiple access (TDMA) scheme, and we find that increasing the relays' power capacity can increase the throughput gain of the NOMA relay against the TDMA relay. Furthermore, to improve the throughput under weak channel power gains, we propose a hybrid NOMA (HB-NOMA) relay that adaptively exploits the benefit of NOMA relay and that of the interference-free TDMA relay. By using the throughput provided by the HB-NOMA relay for each individual MU, we study the multi-MUs scenario and investigate the multi-MUs scheduling problem over a long-term period to maximize the overall utility of all MUs. Numerical results demonstrate the performance advantage of the proposed multi-MUs scheduling that adopts the HB-NOMA relay-transmission.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

Dual-connectivity (DC), an emerging paradigm in the recent 3GPP specification, is envisioned as a promising solution to enhance mobile users' (MUs') traffic offloading by aggregating radio resources at both macro and small cells. In this paper, we investigate the energy-efficient DC-enabled traffic offloading through small cells which are powered by the on-grid power supply and harvesting renewable energy from nature. In spite of reducing the on-grid power consumption, powering traffic offloading by energy harvesting (EH) leads to the offloading outage due to the intermittency in EH power-supply, which degrades the offloading throughput. Therefore, to reap both the advantages of DC and the EH-supply, we propose a joint traffic scheduling and power allocation scheme that aims at minimizing the total on-grid power consumption, while accounting for the offloading outage and guaranteeing the MU's quality of service (QoS) requirement. In spite of the non-convexity nature of the joint optimization of traffic scheduling and power allocation, we propose an algorithm to efficiently compute the optimal offloading solution. Numerical results are provided to validate our proposed algorithm and the performance gain of the proposed DC-enabled traffic offloading scheme.

Ambient energy harvesting has emerged as a promising technique to improve the energy efficiency and reduce the total greenhouse gas emissions for green relay networks. In this paper, we study the joint relay selection and power control problem for the Decode-and-Forward energy-harvesting wireless relay network. In particular, the problem formulation is to maximize the end-to-end system throughput by a deadline under the limitations of data and energy storage. To solve the problem, we decompose such an optimization problem into two subproblems: the joint time scheduling and power control subproblem and the relay selection subproblem. Due to the convex nature of the joint time scheduling and power control subproblem, we derive the optimal solution via the primal decomposition. Based on the obtained system throughput, we can quickly select the best relay that achieves the maximum throughput. Simulation results show that the proposed algorithm can guarantee the maximum system throughput, in comparison with some existing algorithms.

The advanced non-orthogonal multiple access (NOMA) has been considered as a promising scheme to satisfy the ultimate goals of future 5G cellular networks for providing ultra-high throughput and ultra-dense connections. By enabling a group of mobile users (MUs) to simultaneously share a same frequency channel and adopting successive interference cancellation to mitigate the co-channel interference, the NOMA can significantly improve the spectrum efficiency compared with the conventional orthogonal multiple access (OMA). However, due to cellular operators' limited and crowded spectrum resources, a critical question is how to properly size the channel bandwidth for the NOMA- enabled transmission to satisfy all MUs' traffic demands. In this paper, we propose a joint optimization scheme of bandwidth and power allocations for the NOMA- enabled downlink transmission, with the objective of minimizing the overall resource consumption cost that accounts for both the spectrum consumption cost and power consumption cost. In spite of the non-convexity nature of the joint optimization problem, we propose an efficient algorithm to compute the optimal bandwidth allocation and power allocation. Numerical results validate the proposed algorithm and the performance advantage of the proposed NOMA-enabled transmission in saving the overall resource consumption cost.

Offloading data traffic from the conventional macro- cell base stations to densely deployed small-cell base stations (SBSs) has been emerging as a promising technique to support the explosion of data traffic with reduced energy consumption and improved quality of service provision. In this paper, we study the joint spectrum allocation and power allocation problem for the data offloading in energy-harvesting downlink small-cell networks. First, we formulate the resource allocation problem under the revenue maximization criterion, which is expressed as the difference between the total utility across users and the total power payment. By proving the convexity of the problem, we can compute the solution efficiently. Numerical results show that the energy-efficiency can be improved while alleviating the burden of macro-cell base station through using the resource allocation scheme proposed for data offloading.

To meet the growing demand of mobile data traffic in vehicular communications, the vehicle-to-small-cell (V2S) network has been emerging as a promising vehicle-to-infrastructure technology. Since the non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) can achieve superior spectral and energy efficiency, massive connectivity and low transmission latency, we introduce the NOMA with SIC to V2S networks in this paper. Due to the fast vehicle mobility and varying communication environment, it is important to dynamically allocate small-cell base stations and transmit power to vehicular users with considering the vehicle mobility in NOMA-enabled V2S networks. To this end, we present the joint optimization of cell association and power control that maximizes the long-term system-wide utility to enhance the long-term system-wide performance and reduce the handover rate. To solve this optimization problem, we first equivalently transform it into a weighted sum rate maximization problem in each time frame based on the standard gradient-scheduling framework. Then, we propose the hierarchical power control algorithm to maximize the equivalent weighted sum rate in each time frame based on the Karush-Kuhn-Tucker (KKT) optimality conditions and the idea of successive convex approximation. Finally, theoretical analysis and simulation results are provided to demonstrate that the proposed algorithm is guaranteed to converge to the optimal solution satisfying KKT optimality conditions.

In this paper, we investigate the cooperative traffic offloading among mobiles devices (MDs) which are interested in receiving a common content from a cellular base station (BS). For offloading traffic, the BS first sends the content to some selected MDs which then broadcast the received data to the other MDs, such that each MD can receive the entire content simultaneously. Due to each MD's limited transmit-power and energy budget, the transmission rate of the content should be properly designed, since it strongly influences whether and how long each MD can perform relaying. Therefore, different from most existing MDs cooperative schemes, we focus on a novel joint optimization of the content transmission rate and each MD's relay-duration, with the objective of minimizing the system cost accounting for the energy consumption and the cellular-link usage. To tackle with the technical challenge due to the coupling effect between the content transmission rate and each MD's relay-duration, we exploit the decomposable property of the joint optimization problem, based on which we characterize different possible cases for achieving the optimal solution. We then derive the optimal solution for each case analytically, and further propose an efficient algorithm for finding the globally optimal solution of the original joint optimization problem. Numerical results are provided to validate the proposed algorithm (including its accuracy and computational efficiency) and demonstrate that the optimal MDs' cooperative offloading can significantly reduce the system cost compared to some heuristic schemes. Several interesting insights about the cooperative offloading are also obtained.

Since non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) can achieve superior spectral-efficiency and energy-efficiency, the concept of SCN using NOMA with SIC is proposed in this paper. Due to the difference in small-cell base stations' locations, each mobile user perceives different channel gains to different small-cell base stations. Therefore, it is important to associate a mobile user with the right base station and control its transmit power for the uplink SCN using NOMA with SIC. However, the already-challenging base station association problem is further complicated by the need of transmit power control, which is an essential component to manage co-channel interference. Despite its importance, the joint base station association and power control optimization problem that maximizes the system-wide utility and at the same time minimizes the total transmit power consumption for the maximum utility has remained largely unsolved for the uplink SCN using NOMA with SIC, mainly due to its non-convex and combinatorial nature. To solve this problem, we first present a formulation transformation that captures two interactive objectives simultaneously. Then, we propose a novel algorithm to solve the equivalently transformed optimization problem based on the coalition formation game theory and the primal decomposition theory in the framework of simulated annealing. Finally, theoretical analysis and simulation results are provided to demonstrate that the proposed algorithm is guaranteed to converge to the global optimal solution in polynomial time.

In this paper, we study the power control problem for interference management in the ultra-dense small cell networks, which is formulated to maximize the sum-rate of all the small cells while keeping tolerable interference to the macrocell users. We investigate the problem by proposing a novel game with dynamic pricing. Theoretically, we prove that the Nash equilibrium (NE) of the formulated game coincides with the stationary point of the original sum-rate maximization problem, which could be locally or globally optimal. Furthermore, we propose a distributed iterative power control algorithm to converge to the NE of the game with guaranteed convergence. To reduce the information exchange and computational complexity, we propose an approximation model for the original optimization problem by constructing the interfering domains, and accordingly design a local information-based iterative algorithm for updating each small cell's power strategy. Theoretic analysis shows that the local information-based power control algorithm can converge to the NE of the game, which corresponds to the stationary point of the original sum-rate maximization problem. Finally, simulation results demonstrate that the proposed approach yields a significant transmission rate gain, compared with the existing benchmark algorithms.

In this paper, we investigate how the mobile users (MUs) can effectively offload traffic by taking advantage of the capability of dual-connectivity, which enables an MU to simultaneously communicate with a macro base station (BS) and a small-cell access point (AP) via two radio-interfaces. We formulate an optimization problem that jointly determines each MU's traffic schedule (between the BS and AP) and power allocations (between two radio-interfaces), with the objective to minimize all MUs' total cost. We first propose an effective scheme to characterize the feasibility of the joint optimization problem. Then, by exploiting the layered structure, we propose an efficient layered scheme to solve it. Numerical results are provided to validate the proposed schemes and show the performance gain via properly offloading MUs' traffic via dual-connectivity.

In this paper we proposed a cooperative spectrum sharing protocol with two-way decode-and-forward (DF) relaying. Specifically, two primary users A and B communicate with each other with the assistant of the secondary user S. The secondary user uses a fraction of power to forward the primary signals by acting as a DF relay. As a reward, the secondary user can gain spectrum access by using the remaining power to transmit its own signal. We study the optimization of power allocation such that the secondary transmission rate is maximized, while both of the primary users can achieve their target rates. Numerical simulation and comparisons are presented to illustrate the performance of the proposed spectrum sharing protocol, and both primary and secondary users can benefit from the proposed spectrum sharing protocol.

With the rapid development of smart handled devices and mobile internet services, mobile network operators (MNOs) have experienced an explosive growth in traffic demand in cellular access networks. Intelligently offloading traffic through small-cell networks has been widely considered as an efficient approach for MNOs to relieve traffic congestion in cellular access networks and accommodate more mobile users (MUs) with satisfactory quality of service (QoS). However, offloading traffic to small-cell networks might incur co-channel interference among the MUs. Such interference, if without a proper control, will lead to significant power consumptions of the MUs, which undermines the benefit of traffic offloading. In this paper, we are motivated to investigate the joint access-selection and power allocation problem, in which the MUs are appropriately selected to offload their traffic demands to different small-cell networks with proper transmit-powers. Our objective is to maximize a system-reward that takes into account both the MNO's economic reward for serving the MUs and the MUs' transmit-power consumption costs. The formulated problem corresponds to a mixed binary and non-convex optimization problem. We exploit the decomposable structure of the problem and propose an efficient algorithm to solve it. Numerical results are provided to show the performance of the proposed algorithm as well as the benefits of the proposed traffic offloading scheme.

Dynamic spectrum access via active spectrum sharing has been considered as a promising approach to improve the spectrum utilization for future wireless systems. In this paper, based on our recent study on the optimal transmit-power allocation for an active spectrum sharing system comprised of single primary-user (PU) and multiple secondary-users (SUs) [7], we move a further step to investigate a more challenging scenario comprised of multiple PUs and multiple SUs. Specifically, we formulate a joint SU-selection and power allocation problem, in which the PUs properly select different groups of SUs to share channels with and the PUs and SUs then determine the proper transmit- powers. We aim at maximizing a system reward for serving the SUs' traffic while trading off the PUs' additional power consumptions to guarantee their required quality of service (QoS). We exploit the layered structure of the joint optimization problem and propose an efficient algorithm to solve it. Numerical results are provided to validate performances of the proposed algorithm and show advantages of performing the joint SU-selection and power allocation in active spectrum sharing.

In this paper, we investigate the energy-aware data- offloading of mobile user (MU) which schedules its traffic demand to a macro Base Station (BS) and a small-cell access point (AP) simultaneously. For saving the usage of licensed spectrum, we consider that the MU uses unlicensed spectrum to offload data. The open access of unlicensed spectrum, however, results in that the MU's data offloading suffer from uncontrollable interference, which comprises the benefit of data offloading. We propose an outage-probability to quantify such an adverse influence and formulate a joint rate-splitting and power allocation problem to minimize a system-wise cost accounting for both the MU's power consumption and the BS's licensed channel usage. Despite the non-convexity of the joint optimization problem, we transform it into three rate- allocation problems under different cases and derive the respective optimal solutions, which yield the globally optimal solution for the original problem. Numerical results are provided to validate the optimal offloading-solution.

Offloading cellular mobile users' (MUs') data traffic to small-cell networks is a cost-effective approach to relieve congestion in macrocell cellular networks. However, as many small-cell networks operate in the unlicensed bands, the data offloading might suffer from a security issue, i.e., some eavesdropper could overhear the offloaded data over unlicensed spectrums. This motivates us to investigate a secrecy-based energy-efficient uplink data offloading scheme. Specifically, we consider the recent paradigm of traffic offloading via dual connectivity, which enables an MU to simultaneously deliver traffic to a macro base station (mBS) over the licensed channel and a small-cell access point (sAP) over the unlicensed channel. We formulate an MU's joint optimization of traffic scheduling and power allocation problem, with the objective of minimizing the total power consumption while meeting both the MU's traffic demand and secrecy requirement. Despite the non-convex nature of the joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. By evaluating the impact of the MU's secrecy requirement and the eavesdropper's channel condition, we quantify the conditions under which the optimal offloading solution corresponds to the full-offloading and zero-offloading, respectively. Numerical results validate the optimal performance of our proposed algorithm, and show that the optimal offloading can significantly reduce the total power consumption compared with some fixed offloading schemes. Based on the optimal offloading solution for each MU, we further analyze the scenario of multiple MUs and sAPs, and investigate how to optimally exploit the sAPs' total offloading capacity to serve the MUs while accounting for the MUs' corresponding power consumptions for offloading data. To this end, we formulate a total network-benefit maximization problem that accounts for the reward for serving the MUs successfully, the mBS's bandwidth usage, and the MUs' power consumptions. Numerical results show that the optimal solution can improve the total network benefit compared with some heuristic sAP-selection scheme.

The optimal operation of a distributed battery energy storage system (BESS) for energy arbitrage under dynamic pricing is studied in this paper, and the Pareto optimal arbitrage policy that balances the economic value and lifetime tradeoff of the BESS is obtained. Specifically, the lifetime performance of the BESS is represented by its average lifetime, i.e., the average operational duration within which its capacity stays above a certain threshold, and the value performance of the BESS is defined as the total average arbitrage value within its entire lifetime. We propose a constrained stochastic shortest path (CSSP) model to characterize the optimal value-lifetime performance pair. By exploiting the hidden structure of this CSSP problem, an efficient parallel algorithm is proposed to compute the optimal policy. We further prove the condition under which the optimal policy is Pareto optimal. This implies that the achievable optimal value-lifetime performance pair is globally optimal as long as the system-wide utility is monotonically increasing in both the value performance and the lifetime performance. We validate our proposed model and algorithm via real battery specifications and electricity market data, and the results show promising insights for both infrastructure planning and operational management of BESSs in practice.

Relay-assisted access technique has been proposed as a promising solution to improve the energy efficiency and service quality of edge users for cellular networks. In this paper, we aim to find the optimal scheduling period, optimal power allocation, and optimal user scheduling and relay selection that minimizes the total power consumption under the constraints of minimum data rate requirements for the single-cell relay-assisted cellular network. Although we assume that every user in the network is interference-free with each other due to orthogonal resource allocation, such an optimization problem is in general a mixed-integer programming, and thus the optimal solution is difficult to achieve. To make the optimization problem tractable, we decompose the problem into the power allocation optimization subproblem and the joint user scheduling and relay selection optimization subproblem. First, we obtain the optimal scheduling period approximately equal to the ratio between the number of users and the number of relays by sequentially solving these two subproblems. Furthermore, we propose a distributed joint user scheduling and relay selection algorithm based on the duality theory and auction theory. The theoretical results show that the proposed algorithm can help every user select the optimal relay and transmission time slot in polynomial time. Simulation results further show that the proposed algorithm can guarantee the minimum scheduling duration without consuming more transmit power, in comparison with other existing algorithms.

In this paper, we investigate the revenue optimization of a cellular system, which intelligently provides access services to device-to-device users (DUs) by reusing the resource-blocks (RBs) of cellular users (CUs). While charging the DUs for services, the cellular system compensates for the additional power consumption costs of the CUs to meet their required quality of service (QoS), and hence aims at achieving the best tradeoff between charging the DUs and affording the CUs' costs to maximize its own revenue. We formulate this energy-aware revenue optimization problem as a joint RB-reuse and power control problem, which we further decompose into a power control problem for each individual CU-DU pair and a DU-selection problem for selecting appropriate DUs to reuse the CUs' RBs. For each CU-DU pair, we derive the optimal power allocation in closed form and the maximum gain of the cellular system from this pair. Based on the gains of all CU-DU pairs, we then consider the DU-selection problem as maximum weighted matching on a bipartite graph and solve it by using linear relaxation. Numerical results validate our analysis regarding the optimal power allocation for each CU-DU pair and the BS's optimal selection of the DUs to reuse the CUs' RBs such that the BS's revenue is maximized.

Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTC's pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC's optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.

In the heterogeneous cellular network, each mobile station perceives different channel gains to different base stations. Therefore, it is important to associate a mobile station with the right base station with transmit power control so as to achieve substantial improvement in spectrum-efficiency and energy-efficiency. Despite its importance, the problem that maximizes the overall system revenue with minimum total transmit power consumption through joint BS association and power control has remained largely unsolved for the large-scale heterogeneous cellular network, mainly due to its non-convex and combinatorial nature. To solve this problem, this paper first proposes a single-stage formulation that captures two interactive objectives, i.e., the maximization of overall system revenue and the minimization of total transmit power consumption. The single-stage optimization problem is then efficiently solved by the proposed POSEM algorithm based on the theory of coalition formation game and the idea of simulated annealing. Finally, our analysis shows that the proposed algorithm is guaranteed to converge to the global optimal solution fast.

We propose a revenue sharing based resource allocation scheme for dynamic spectrum access (DSA) networks. In our scheme, based on a mutually agreed revenue sharing scheme, a primary network operator (PNO) actively shares its radio resource with a secondary network operator (SNO), which provides access service to secondary users (SUs) for its revenue maximization. To investigate the coupling effect between the revenue sharing and resource allocation, we formulate the interaction between PNO and SNO as a two-layered game, which includes a top layer game to model their revenue sharing and a bottom layer game to model their joint resource allocations. Specifically, in the top layer, based on their joint resource allocation decisions, the PNO and SNO form a Nash bargaining game to determine the revenue sharing scheme such that both of them can benefit from cooperation satisfactorily. Then, in the bottom layer, under the given revenue sharing scheme, the PNO and SNO form a Stackelberg game to determine their joint resource allocation decisions, which also influence their respective revenues. The two games work iteratively such that the PNO and SNO reach a final equilibrium state at which neither PNO nor SNO will change its decisions unilaterally in both layers. We propose efficient algorithms to solve both the top layer and bottom layer games and compute the final equilibrium of the two-layered game. Specifically, despite the non-convexity of joint resource allocation optimization problem in the bottom layer, we identify its hidden monotonic structure and propose an efficient algorithm, which is based on the polyblock approximation, to achieve the optimal solutions. Moreover, in the top layer, to tackle with the difficulty due to the lack of an analytical objective function for the revenue sharing problem, we explore its hidden unimodal property and propose a Brent's method based algorithm to achieve the optimal solution. Numerical results are presented to verify the performance of our algorithms and show that our revenue sharing based resource allocation scheme yields a win-win situation for the PNO and SNO.

In this paper, we investigate the cooperative resource sharing and pricing for the licensed Primary User (PU) and Cognitive Radio Networks (CRNs), where the PU jointly determines how to share its under-utilized radio resource with Secondary Users (SUs) and how to charge the SUs accordingly. Meanwhile, the SUs jointly determine how to utilize the shared radio resource from the PU and their preferred payments. Since both the PU and SUs expect to benefit from cooperation, we model their interactions as a Nash bargaining problem. Viewing the nonconvexity of bargaining problem, we first propose a two-step procedure to solve it efficiently. The two-step procedure explores the connection between the bargaining problem and its associated social optimization problem, and thus turns the original nonconvex bargaining problem into two consecutive convex optimization problems. We then propose two efficient algorithms, each with guaranteed convergence, to solve these two problems, respectively. Numerical results show that our proposed two-step procedure achieves the optimality of the bargaining problem with significantly reduced computational complexity. Also, our joint resource sharing and pricing scheme guarantees that each SU and PU can positively benefit from the cooperative bargaining, and the benefit is fairly allocated among them.

This paper proposes a real-time pricing scheme that reduces the peak-to-average load ratio through demand response management in smart grid systems. The proposed scheme solves a two-stage optimization problem. On one hand, each user reacts to prices announced by the retailer and maximizes its payoff, which is the difference between its quality-of-usage and the payment to the retailer. On the other hand, the retailer designs the real-time prices in response to the forecasted user reactions to maximize its profit. In particular, each user computes its optimal energy consumption either in closed forms or through an efficient iterative algorithm as a function of the prices. At the retailer side, we develop a Simulated-Annealing-based Price Control (SAPC) algorithm to solve the non-convex price optimization problem. In terms of practical implementation, the users and the retailer interact with each other via a limited number of message exchanges to find the optimal prices. By doing so, the retailer can overcome the uncertainty of users' responses, and users can determine their energy usage based on the actual prices to be used. Our simulation results show that the proposed real-time pricing scheme can effectively shave the energy usage peaks, reduce the retailer's cost, and improve the payoffs of the users.

Heterogeneous cellular network (Hetnets), where various classes of low power base stations (BS) are underlaid in a macro-cellular network, is a promising technique for future green communications. These new types of BSs can achieve substantial improvement in spectrum-efficiency and energy-efficiency via cell splitting. However, mobile stations perceive different channel gains to different base stations. Therefore, it is important to associate a mobile station with the right BS so as to achieve a good communication quality. Oftentimes, the already-challenging BS association problem is further complicated by the need of transmission power control, which is an essential component to manage co-channel interference in many wireless communications systems. Despite its importance, the joint BS association and power control (JBAPC) problem has remained largely unsolved, mainly due to its non-convex and combinatorial nature that makes the global optimal solution difficult to obtain. This paper aims to circumvent this difficulty by proposing a novel algorithm based on Benders' Decomposition to solve the non-convex JBAPC problem efficiently and optimally. In particular, we endeavor to maximize the system revenue and meanwhile associate every served mobile station with the right BS with the minimum total transmission power. We first propose a single-stage formulation that captures the two objectives simultaneously. The problem is then transformed in a way that can be efficiently solved using the proposed joint BS Association and poweR coNtrol algorithm (referred to as BARN) that is derived from classical Benders' Decomposition. Finally, we derive a closed-form analytical formula to characterize the effect of the termination criterion of the algorithm on the gap between the obtained solution and the optimal one. For practical implementation, we further propose an Accelerated BARN (A-BARN) algorithm that can significantly reduce the computational time. By carefully choosing the termination criterion, both BARN and A-BARN are guaranteed to converge to the global optimal solution.

In this paper, we study the revenue sharing and rate allocation for Internet Service Providers (ISPs) that jointly provide network connectivity between content providers and end-users. Without colluding, each ISP may selfishly set a high transit-price to cover its cost and maximize its own profit, which inevitably results in a loss in social profit. We model this noncooperative interaction between an “eyeball” ISP and a “content” ISP as a Stackelberg game and quantify the resulting loss in social profit. To recover the profit loss, we propose a revenue sharing contract between ISPs by modeling them as a supply chain to deliver traffic in a two-sided market. Parameterized by the profit division factor, the sharing contract coordinates ISPs' objectives such that they aim to maximize the social profit self-incentively. We further propose a Nash bargaining process to determine the profit division factor such that all ISPs are simultaneously better off compared to the noncooperative equilibrium.

In this work we study joint pricing and power allocation for Dynamic Spectrum Access (DSA) networks with Stackelberg game. In our model, Primary User (PU) is the game leader and jointly determines its power allocation (to guarantee its QoS requirement) and the interference price charged to Secondary User (SU) (to reap revenue). Meanwhile, SU is the game follower and determines its power demand in response to PU's decisions. We quantify PU's and SU's benefit from the channel sharing model by deriving the Stackelberg equilibrium. Our results show that PU's equilibrium profit is asymptotically upper bounded with its marginal power cost and rate requirement. A distributed algorithm is proposed to find the equilibrium. We also propose an incentive-compatible mechanism for PU and SU to keep the social welfare optimum cooperatively. We extend our Stackelberg game to the multiple SUs scenario, where the interference among SUs results in a noncooperative power demand subgame. We propose a low-complexity heuristic algorithm for PU to maximize its profit. Our results show that PU can benefit by selecting multiple SUs to share its channel if SUs' mutual interference is limited.

In this paper we study the joint rate allocation, routing and spectrum sharing policy for multi-hop Cognitive Radio Networks (CRNs). We formulate this cross layer optimization problem as a sequential decision process which aims to minimize the average total power consumption of CRNs in each scheduling cycle under the constraint that each Cognitive Radio (CR) user's traffic demand is guaranteed. We consider imperfect spectrum sensing in our problem formulation and address Primary Users (PUs) protection with the interference regulation. We use Dynamic Programming (DP) to solve the formulated problem and derive the optimal rate allocation, routing and spectrum sharing policy for CRNs.

Usage-based pricing has been recognized as a network congestion management tool. Internet Service Providers (ISPs), however, have limited ability to set time-adaptive usage-price to manage congestion arising from time-varying consumer utility for data. To achieve the maximum revenue, ISP can set its time-invariant usage-price low enough to aggressively encourage consumer's traffic demand. The downside is that ISP has to drop consumer's excessive traffic demand through congestion management (i.e., packet dropping), which may degrade Quality of Service (QoS) of consumer's traffic. Alternatively, to protect consumer's QoS, ISP can set its time-invariant usage-price high enough to reduce consumer's traffic demand, thus minimizing the need for congestion management through packet dropping. The downside is that ISP suffers a revenue loss due to the inefficient usage of its network. The tradeoff between ISP's revenue maximization and consumer's QoS protection motivates us to study ISP's revenue maximization subject to QoS constraint in terms of the number of packets dropped. We investigate two different QoS measures: short-term per-slot packet dropping constraint and long-term packet dropping constraint. The short-term constraint can be interpreted as a more transparent congestion management practice compared to the long-term constraint. We analyze ISP's optimal time-invariant pricing for both constraints, and develop an upper bound for the optimal revenue by considering the specified packet dropping threshold. We quantify the impact of consumer's price elasticity on ISP's optimal revenue and show that ISP should carry out a differentiated QoS protection strategy based on consumer's price elasticity in order to mitigate the revenue loss1.

In this paper we study the distributed multi-channel power allocation for spectrum sharing cognitive radio networks with QoS guarantee. We formulate this problem as a non- cooperative game GMCPA-Cwith coupled strategy space to address both the co-channel interference among secondary users and the interference temperature regulation imposed by primary systems. We investigate the properties of Nash equilibrium (N.E.) for our GMCPA-C, including the existence and QoS provisioning. Furthermore, we derive a layered structure by applying the Lagrangian dual decomposition to GMCPA-Cand design a distributed algorithm to find the N.E. via this structure. Simulation results are presented to show both the validity of our game theoretic model and the performance of our proposed algorithm. Finally, we incorporate the Pigouvian taxation into our algorithm to improve the efficiency of N.E. when social optimality is considered.

In this paper, we study the joint rate and multi-channel power allocations in spectrum sharing networks (SSNs) with balanced QoS provisioning and power saving. We formulate this cross layer problem as a non-cooperative game GJRPA in which each user aims to achieve its target data rate as exactly as

In this paper, we study the distributed multichannel power allocation (MCPA) problem for the spectrum sharing cognitive radio networks (CRNs), where secondary transceiver pairs share the same spectrum with the primary system. The problem is formulated as a non-cooperative game with coupled constraints to address the interference temperature restrictions imposed by the primary system. Existence and uniqueness of the Nash Equilibrium (N.E.) for this coupled constraints MCPA game are investigated. Distributed MCPA algorithm is proposed to approach the unique N.E. Simulation results are obtained to verify the validity of the proposed algorithm.