Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

Mobile Virtual Reality (MVR), which aims to provide high-quality VR services to mobile devices of end users, has become the latest trend in virtual reality developments. The current MVR solution is to remotely render frame data from a cloud server, while the potential of edge computing in MVR is underexploited. In this paper, we propose a new approach named MUCVR to achieve high-quality interactive MVR collaboration for multiple users by exploiting edge computing. Firstly, we design “vertical” edge–cloud collaboration for VR task rendering, in which foreground interaction is offloaded to an edge server for rendering, while the background environment is rendered by the cloud server. Correspondingly, the VR device of a user is only responsible for decoding and displaying. Secondly, we propose the “horizontal” multi-user collaboration based on edge–edge cooperation, which synchronizes the data among edge servers. Finally, we implement the proposed MUCVR on an MVR device and the Unity VR application engine. The results show that MUCVR can effectively reduce the MVR service latency, improve the rendering performance, reduce the computing load on the VR device, and, ultimately, improve users' quality of experience.

A medium access control (MAC) protocol design is proposed in this paper for next-generation industrial Internet of Things (IIoT) networks. Considering a nonfully connected network with multiple access points (APs), we aim to connect a massive number of IIoT devices densely populating the network and minimize the delay in channel access without packet collisions. To achieve this objective, we propose a device location-based medium access control design, which integrates scheduled access and carrier sensing. In our design, devices are assigned to time slots based on their locations, and the assignments are coordinated among APs to eliminate collisions while maximizing channel utilization. To analyze the performance of the proposed design, we derive the average delay each device experiences with the proposed scheduling scheme and verify our analysis via simulations of an IIoT network with 19 APs and over 17000 devices. The results show the effectiveness of the proposed design in supporting massive connections while at the same time achieving low delay.

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by jointly optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

Electricity theft detection (ETD) aims to promptly identify electricity theft by vigilantly monitoring and analyzing atypical electricity consumption time series. Existing machine learning approaches to ETD demand large training sets, leading to degraded performance when limited training samples are available. In this paper, we introduce FL2ETD, a novel few-shot learning framework to ETD. The framework consists of three core components, i.e., a feature extraction module, a representation module, and a classification module. The feature extraction module processes the electricity consumption behavior of users in both the time and the frequency domains to extract distinctive features and increase the number and the diversity of features. The representation module utilizes contrast learning to pre-train unlabeled electricity consumption data for enhancing feature representation quality. The classification module integrates feature representations for making the final decision in ETD. Extensive experiments demonstrate that FL2ETD exhibits superior performance compared to baselines, and its advantage is significant when the number of available training samples is very small (with only 338 samples).

This paper introduces a real-time collaborative sensing scheme for wireless sensor networks in time-varying environments. The objective is to maximize the sensors' performance by effectively allocating communication resources for data sharing. Specifically, we utilize digital twins (DTs) to characterize dynamic collaborative sensing demands for each sensor through data-driven methods. Building on the DT design, we propose a resource allocation scheme to optimize the communication resources allocated at each stage of collaborative sensing and determine the most effective collaborative sensing policy. By profiling sensors using DTs, the network controller can effectively coordinate the sensors without exhaustively exploring all collaborative sensing policies. Numerical results demonstrate the effectiveness of our proposed scheme in optimizing the sensing performance for all sensors.

Device-edge collaboration on deep neural network (DNN) inference is a promising approach to efficiently utilizing network resources for supporting Artificial Intelligence of Things (AIoT) applications. In this article, we propose a novel digital twin (DT)-assisted approach to device-edge collaboration on DNN inference that determines whether and when to stop local inference at a device and upload the intermediate results to complete the inference on an edge server. Instead of determining the collaboration for each DNN inference task only upon its generation, multi-step decision making is performed during the on-device inference to adapt to the dynamic computing workload status at the device and the edge server. To enhance the adaptivity, a DT is constructed to evaluate all potential offloading decisions for each DNN inference task, which provides augmented training data for a machine learning-assisted decision-making algorithm. Then, another DT is constructed to estimate the inference status at the device to avoid frequently fetching the status information from the device, thus reducing the signaling overhead. We also derive necessary conditions for optimal offloading decisions to reduce the offloading decision space. Simulation results demonstrate the outstanding performance of our DT-assisted approach in terms of balancing the tradeoff among inference accuracy, delay, and energy consumption.

This article introduces an on-demand collaborative sensing scheme for industrial Internet of Things (IIoT) sensors in time-varying sensing environments, aiming to optimize the sensing performance by effectively allocating communication resources for sensory data sharing. Particularly, we propose a novel digital twins (DTs)-empowered resource allocation solution to facilitate scalable and flexible collaborative sensing. First, DTs create mathematical models using real-time network data to characterize the dynamic resource demands in collaborative sensing. Second, the performance of mathematical models in DTs is evaluated through data-driven methods. Building on our DT design, we propose a joint collaborative sensing and DT management scheme to optimize the resource allocation for sensory data sharing and DT operation. Furthermore, we develop a DT evaluation method featuring a variational autoencoder to evaluate the accuracy of DTs and enable closed-loop DT-based resource allocation. Numerical results demonstrate the effectiveness of our proposed collaborative sensing scheme in optimizing the sensing performance for all sensors.

Edge-device collaboration has the potential to facilitate compute-intensive device pose tracking for resource-constrained mobile augmented reality (MAR) devices. In this article, we devise a 3-D map management scheme for edge-assisted MAR, wherein an edge server constructs and updates a 3-D map of the physical environment by using the camera frames uploaded from an MAR device, to support local device pose tracking. Our objective is to minimize the uncertainty of device pose tracking by periodically selecting a proper set of uploaded camera frames and updating the 3-D map. To cope with the dynamics of the uplink data rate and the user’s pose, we formulate a Bayes-adaptive Markov decision process problem and propose a digital twin (DT)-based approach to solve the problem. First, a DT is designed as a data model to capture the time-varying uplink data rate, thereby supporting 3-D map management. Second, utilizing extensive generated data provided by the DT, a model-based reinforcement learning algorithm is developed to manage the 3-D map while adapting to these dynamics. Numerical results demonstrate that the designed DT outperforms Markov models in accurately capturing the time-varying uplink data rate, and our devised DT-based 3-D map management scheme surpasses benchmark schemes in reducing device pose tracking uncertainty.

Systems that integrate distributed databases and existing blockchain platforms have recently emerged, which conveniently leverage their respective strengths to build efficient, secure, and usable data sharing and collaboration environments for different organizations. However, the performance of such systems can be limited by the native blockchain platforms due to the high latency of transactions. In this paper, we present CoralDB, a bottom-up fully redesigned hybrid system of blockchain and database, aimed at enabling untrusted organizations to collaborate and share data efficiently and securely at the database level. The storage layer of CoralDB ensures data security and system throughput through key modules such as customized block structure, consensus mechanism, and transaction pool. On top of the storage layer, a database layer is introduced, which extends the blockchain of the storage layer by incorporating connection pools, collaborative tables, and query interfaces, to enhance the usability and efficiency of data collaboration and sharing. Extensive experimental results demonstrate that CoralDB provides security assurances at the level of blockchain and enables efficient decentralized data collaboration and sharing.

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

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.

The increasing network traffic and dynamic user connections have posed challenges for cellular operators in reducing operating costs while ensuring the quality of service (QoS) for users. Cloud radio access network (C-RAN) addresses these issues by separating baseband units (BBUs) and remote radio heads (RRHs), creating a centralized BBU pool. To optimize C-RAN performance, the key is to dynamically assigning RRHs to BBUs, which is challenging due to cost and QoS constraints. In this paper, we propose a data-driven RRH-BBU mapping scheme (KC-A3C) with deep reinforcement learning (DRL) to improve the performance of large-scale C-RANs. First, we analyze a dataset from a cellular operator containing approximately 26,652 active base stations and use the features of the dataset to construct an RRH popularity metric to cluster RRHs. Second, we model the RRH-BBU mapping as a Markov decision process and use the synchronous Advantage Actor-Critic (A3C) algorithm to find the optimal mapping scheme with the highest long-term gain in a dynamic environment, considering resource utilization, RRH migration, and BBU load balancing. Evaluations using real-world datasets show that our proposed scheme outperforms baseline methods.

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.

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 joint problem of task offloading, collaborative computing, and resource allocation for multi-access edge computing (MEC) is a challenging issue. In this article, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the problem, we propose a deep reinforcement learning (DRL)-based bilevel optimization framework. The task offloading decision, computing collaboration decision, and power and subcarriers allocation subproblems are solved at the upper level, whereas the computing resource allocation subproblem is solved at the lower level. We combine dueling-DQN and double-DQN and add adaptive parameter space noise to improve DRL performance in MEC. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in energy efficiency and task completion rate compared with other DRL-based approaches and other benchmark schemes under various network parameter settings.

In this paper, we investigate the joint problem of task offloading, Unmanned Aerial Vehicle (UAV) trajectory design, and resource allocation for UAV-enabled edge computing, considering and highlighting the dependency among different tasks. The corresponding optimization problem, which is a mixed-integer problem, is formulated. To solve this problem, we propose an iterative method based on Block Coordinate Descent (BCD) to decompose the original problem into two subproblems. Given the offloading decision and resource allocation, the subproblem of UAV trajectory optimization is solved by convex optimization methods. Then, given the UAV trajectory, the subproblem of task offloading decision and the corresponding resource allocation is solved by dynamic programming and convex optimization methods. Simulation results show that our proposed method can significantly reduce energy consumption compared to the benchmark schemes.

In this paper, we investigate digital twin (DT)-assisted adaptive deep neural network (DNN) inference in the Industrial Internet of Things (IIoT). We consider a scenario that an edge server has a full-size DNN for high-accuracy inference, while an IIoT device has a lightweight DNN for fast on-device inference. The IIoT device generates computing tasks, such as object recognition, to be processed by DNN. For each task, a local controller at the network edge determines whether or not to offload the task to the edge server before it enters each layer of the lightweight DNN. The objective is to find the task offloading point that maximizes a utility including delay, inference accuracy, and on-device energy consumption. To achieve this objective, we propose an online DT-assisted task offloading scheme, which exploits DTs to capture the task processing status at the IIoT device and the workload at the edge server. Simulation results demonstrate the excellent performance of the proposed adaptive DT-assisted DNN inference on delay, inference accuracy, and on-device energy consumption.

This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes.

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80 percent while guaranteeing content freshness in heavily loaded ICVNs.

In the second part of this article, we develop a centralized packet transmission scheduling scheme to pair with the protocol designed in Part I and complete our medium access control (MAC) design for machine-type communications in the industrial Internet of Things. For the networking scenario, fine-grained scheduling that attends to each device becomes necessary, given stringent Quality-of-Service (QoS) requirements and diversified service types, but prohibitively complex for a large number of devices. To address this challenge, we propose a scheduling solution in two steps. First, we develop algorithms for device assignment based on the analytical results from Part I, when parameters of the proposed protocol are given. Then, we train a deep neural network for assisting in the determination of the protocol parameters. The two-step approach ensures the accuracy and granularity necessary for satisfying the QoS requirements and avoids excessive complexity from handling a large number of devices. Integrating the distributed coordination in the protocol design from Part I and the centralized scheduling from this part, the proposed MAC protocol achieves high performance, demonstrated through extensive simulations. For example, the results show that the proposed MAC can support 1000 devices under an aggregated traffic load of 3000 packets per second with a single channel and achieve <; 0.5 ms average delay and <; 1% average collision probability among 50 high priority devices.

In this two-part paper, we propose a novel medium access control (MAC) protocol for machine-type communications in the Industrial Internet of Things. The considered use case features a limited geographical area and a massive number of devices with sporadic data traffic and different priority types. We target supporting the devices while satisfying their Quality-of-Service (QoS) requirements with a single access point and a single channel, which necessitates a customized design that can significantly improve the MAC performance. In Part I of this paper, we present the MAC protocol that comprises a new slot structure, corresponding channel access procedure, and mechanisms for supporting high device density and providing differentiated QoS. A key idea behind this protocol is sensing-based distributed coordination for significantly improving channel utilization. To characterize the proposed protocol, we analyze its delay performance based on the packet arrival rates of devices. The analytical results provide insights and lay the groundwork for the fine-grained scheduling with QoS guarantee as presented in Part II.

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

Terrestrial-satellite networks (TSNs) are envisioned to play a significant role in the sixth-generation (6G) wireless networks. In such networks, hot air balloons are useful as they can relay the signals between satellites and ground stations. Most existing works assume that the hot air balloons are deployed at the same height with the same minimum elevation angle to the satellites, which may not be practical due to possible route conflict with airplanes and other flight equipment. In this paper, we consider a TSN containing hot air balloons at different heights and with different minimum elevation angles, which creates the challenge of non-uniform available serving time for the communication between the hot air balloons and the satellites. Jointly considering the caching, computing, and communication (3C) resource management for both the ground-balloon-satellite links and inter-satellite laser links, our objective is to maximize the network energy efficiency. Firstly, by proposing a tapped water-filling algorithm, we schedule the traffic to relay among satellites according to the available serving time of satellites. Then, we generate a series of configuration matrices, based on which we formulate the relation between relay time and the power consumption involved in the relay among satellites. Finally, the collaborative resource allocation problem for TSN is modeled and solved by geometric programming with Taylor series approximation. Simulation results demonstrate the effectiveness of our proposed scheme.

Mobile edge computing (MEC) has been recognized as a promising technology to support various emerging services in vehicular networks. With MEC, vehicle users can offload their computation-intensive applications (e.g., intelligent path planning and safety applications) to edge computing servers located at roadside units. In this paper, an efficient computing offloading and server collaboration approach is proposed to reduce computing service delay and improve service reliability for vehicle users. Task partition is adopted, whereby the computation load offloaded by a vehicle can be divided and distributed to multiple edge servers. By the proposed approach, the computation delay can be reduced by parallel computing, and the failure in computing results delivery can also be alleviated via cooperation among edges. The offloading and computing decision-making is formulated as a long-term planning problem, and a deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to achieve the optimal solution of the complex stochastic nonlinear integer optimization problem. Simulation results show that our collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.

Plug-in Electric Vehicles (PEVs) keep on penetrating the automobile market. However, uncoordinated PEV charging can impair the reliability of power grid. In this paper, an interesting problem of PEV charging power allocation is investigated, in which both power distribution and transportation constraints are considered. A novel approach for PEV charging management based on optimal power flow (OPF) analysis is proposed to optimize PEV charging energy in a power distribution system. Firstly, spatial and temporal PEV demand scheduling is introduced to maximize PEV charging service capacity while considering the maximum traveling distance of PEVs. Secondly, to ensure the scalability of the OPF analysis, a distributed optimization technique, i.e., proximal Jacobian alternating direction multiplier method, is applied to attain the optimal power allocation in a decentralized manner. The resulting PEV charging service capacity in the power distribution system is improved without violating power distribution and transportation constraints. Furthermore, kernel density estimation method is adopted to identify the PEV range anxiety constraint without the PEV battery information. Simulation results are presented to validate the effectiveness of our approach with high PEV penetration.

In this paper, we investigate service offloading in an integrated terrestrial-satellite (T-S) system. We consider the terrestrial base station (TBS) and satellites to be service providers, all user equipment (UE) to be service requesters, and the service can be content delivery, computation, etc. While offloading services to the satellites can prevent the TBS from being overloaded, the quality of service (QoS), e.g., content delivery latency, may degrade, necessitating a balance between the user preference and the utilities of the TBS and satellites. From the perspective of network management, we propose an abstract model that incorporates the utilities of the TBS, the satellites, and the UE, as well as the service capacity, service load, and service cost at the TBS and the satellites. While finding the optimal offloading decision, a problem of integer programming, is NP-hard, we develop two algorithms with low complexity for finding sub-optimal solutions of the offloading decision problem in the scenarios of one satellite and multiple satellites, respectively. Moreover, we prove that the solution found by the first algorithm is guaranteed to be optimal under the condition that the tasks for service from all UE have an identical size. Numerical results demonstrate the performance of the proposed algorithms compared to that of the optimal offloading by exhaustive search and the offloading by the greedy algorithm.

As the number of Plug-in Electric Vehicles (PEVs) is increasing in recent years, there has been a growing interest in terms of improving the charging service for on-the-move PEVs. In this paper, a task time allocation and reward scheme for advertising PEV charging station information is proposed. PEVs passing by a charging station are assigned a period of time to spread the charging station information within an interest area. To stimulate PEVs behaving cooperatively, two incentive policies provided by the charging station are studied in the task time allocation: the pre-determined reward policy and the optimal reward policy. For the former one, a fairness task time allocation scheme is developed to maximize the utility of the recruited PEVs. For the latter one, a Stackelberg game based optimization problem is formulated to obtain the optimal reward according to the utility of the charging station. An optimization tool, Geometric Water-filling, is utilized to analyze both problems efficiently. Simulation results are provided to validate the optimality of proposed schemes.

Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.

In this paper, a roadside equipment (RSE)-assisted positioning method, i.e., global positioning system (GPS)-received signal strength (RSS) hybrid, is developed for lane-level positioning, which is fundamental to many applications in intelligent transportation systems. By exploiting the potential of RSE in existing pilots all over the world, this key component in connected vehicle networks can be used to achieve greater positioning accuracy than GPS positioning. The proposed method utilizes RSS data, which is commonly available in all connected vehicle networks, to update the GPS position and improve its accuracy based on a Bayesian approach. A method for lane positioning at a specific point is presented first, and then an extension to enable real-time lane positioning is proposed. Two typical types of RSE deployments for different traffic flow demands are considered, and the performance of the proposed method in each deployment is assessed. The proposed method features higher accuracy than existing GPS positioning methods and low complexity. To evaluate the proposed method, simulations are conducted, and the results demonstrate high accuracy and robustness. Moreover, field tests are also conducted, and the outcomes show that the proposed method can recognize the lane in which the target vehicle is traveling.

This two-part paper investigates cache replacement schemes with the objective of developing a general model to unify the analysis of various replacement schemes and illustrate their features. To achieve this goal, we study the dynamic process of caching in the vector space and introduce the concept of state transition field (STF) to model and characterize replacement schemes. In the first part of this work, we consider the case of time-invariant content popularity based on the independent reference model (IRM). In such case, we demonstrate that the resulting STFs are static, and each replacement scheme leads to a unique STF. The STF determines the expected trace of the dynamic change in the cache state distribution, as a result of content requests and replacements, from any initial point. Moreover, given the replacement scheme, the STF is only determined by the content popularity. Using four example schemes including random replacement (RR) and least recently used (LRU), we show that the STF can be used to analyze replacement schemes such as finding their steady states, highlighting their differences, and revealing insights regarding the impact of knowledge of content popularity. Based on the above results, STF is shown to be useful for characterizing and illustrating replacement schemes. Extensive numeric results are presented to demonstrate analytical STFs and STFs from simulations for the considered example replacement schemes.

In the second part of this two-part paper, we extend the study of dynamic caching via state transition field (STF) to the case of time-varying content popularity. The objective of this part is to investigate the impact of time-varying content popularity on the STF and how such impact accumulates to affect the performance of a replacement scheme. Unlike the case in the first part, the STF is no longer static over time, and we introduce instantaneous STF to model it. Moreover, we demonstrate that many metrics, such as instantaneous state caching probability and average cache hit probability over an arbitrary sequence of requests, can be found using the instantaneous STF. As a steady state may not exist under time-varying content popularity, we characterize the performance of replacement schemes based on how the instantaneous STF of a replacement scheme after a content request impacts on its cache hit probability at the next request. From this characterization, insights regarding the relations between the pattern of change in the content popularity, the knowledge of content popularity exploited by the replacement schemes, and the effectiveness of these schemes under time-varying popularity are revealed. In the simulations, different patterns of time-varying popularity, including the shot noise model, are experimented. The effectiveness of example replacement schemes under time-varying popularity is demonstrated, and the numerical results support the observations from the analytic results.

Heterogeneous computing powered by remote clouds and local fogs is a promising technology to improve the performance of user terminals in the Internet of Things. In this paper, two semi-Markov decision process (SMDP)-based coordinated virtual machine (VM) allocation methods are proposed to balance the tradeoff between the high cost of providing services by the remote cloud and the limited computing capacity of the local fog. We first present a model-based planning method in which it is necessary to train the state transition probabilities and the expected time intervals between adjacent decision epochs. To facilitate training them, the SMDP is degraded into a continuous-time Markov decision process (CTMDP) in which the service requests and ongoing service completions follow a continuous-time Markov chain. The relative value iterative algorithm for the CTMDP is used to find an asymptotically optimal VM allocation policy. In addition, we also propose a model-free reinforcement learning (RL) method, where an optimal coordinated VM allocation policy is approximated by learning from the states and rewards of feedback. The simulation results show that the performance of the model-free RL method can converge to a level similar to that of the model-based planning method and outperform the greedy VM allocation method.

Incentives for distributed optimization are investigated in two types of scenarios in which network users have private valuations (objective functions). A network center aims at maximizing the sum of users' valuations in the first scenario or the sum of its own valuations in the second scenario. It is shown that nontrivial strategies can be found by a user so that it can improve its own utility by providing false information to the center without leading a distributed algorithm to diverge. It demonstrates that a dual variable based pricing mechanism in distributed optimization cannot guarantee truthful reporting. While truthful reporting can be realized by using the classic Groves mechanism in the first scenario, the possibility of incentivizing truthful reporting in the second scenario depending on whether the center is willing to consider the valuations of the users in addition to those of its own.

The problem of jamming on a multiple-input multiple-output (MIMO) Gaussian channel is investigated. We show that the existing result based on the simplification of the system model by neglecting the jamming channel leads to losing important insights regarding the effect of jamming power and jamming channel on the jamming strategy. We find a closed-form optimal solution for the problem under some positive semidefinite condition without considering simplifications in the model. If the condition is not satisfied and the optimal solution may not exist in closed-form, we find a suboptimal solution in closed-form as a close approximation of the optimal solution. Simulation results verify the effectiveness of the proposed solutions.

The problem of sum-rate maximization with minimum power consumption is studied for a decode-and-forward (DF) multiple-input multiple-output (MIMO) two-way relaying system consisting of two sources and one relay. Two scenarios are investigated. In the first scenario, the relay optimizes its own power allocation/beamforming strategy given that the strategies of the sources maximize the sum-rate of the multiple-access channel (MAC) phase. In the second scenario, the relay and the sources jointly optimize their power allocation/beamforming strategies over both the MAC and broadcasting (BC) phases. The considered problem of sum-rate maximization with minimum power consumption is shown to be nonconvex in both scenarios. For the first scenario, an algorithm is proposed to find the optimal strategy of the relay. For the second scenario, the sources and the relay find their strategies either through transferring the original nonconvex problem into corresponding convex problems or using a proposed low-complexity algorithm. Simulation results demonstrate the performance of proposed algorithms.

The problem of power allocation/channel selection in two-user games is considered. Unlike most of the game theoretic studies on resource allocation problems which consider pure strategies, this work investigates mixed strategies and mixed strategy Nash equilibrium (MSNE) that enables users to adopt more subtle strategies to improve their utilities. The necessary and sufficient conditions for the existence/uniqueness of MSNE are derived, first in a two-channel case and then in a more practical N channel case. In the two-channel game, the MSNE which maximizes the utilities of both users is found. In the N-channel game, a channel selection algorithm for the users, the outputs of which can be used to check the existence/uniqueness of MSNE, is proposed.

The problem of resource allocation among multiple users with total power and spectral mask constraints is studied based on cooperative game-theoretic approach. The problem is non-convex, and finding the optimal solution requires joint power and bandwidth allocation that renders high-complexity algorithms. Therefore, we first categorize the systems to bandwidth-dominant and power-dominant according to their bottleneck resources. Then, different manners of cooperation are adopted for each type of systems, and a two-user algorithm is developed for each case. Such categorization guarantees that the solution obtained in each case is Pareto-optimal, while the complexity is significantly reduced.

A precoding strategy for multi-user spectrum sharing over an interference channel is proposed and analyzed from a game-theoretic perspective. The proposed strategy is based on finding the Nash bargaining solution for precoding matrices in a cooperative scenario over frequency selective channels under a spectrum mask constraint. An in-time update of the precoding matrices is enabled by using time slots to guarantee the effectiveness of the bargaining solution when the number of users varies. A dual decomposition approach is exploited to construct a distributed structure for solving the bargaining problem. The proposed distributed algorithm realizes the physical process of bargaining, which is not present in the Nash bargaining theory.

In this paper, preceding strategies over interference channels are analyzed from a game-theoretic perspective. The Nash equilibrium and Nash bargaining solutions of preceding matrices, as the optimal precoding strategies in non-cooperative and cooperative cases, respectively, are derived for a two-player game over both flat fading and frequency selective channels. It is shown that the non-cooperative and cooperative solutions of precoding matrices are the same over multiple-input single- output(MISO) flat fading interference channels under a total power constraint. The solution in flat fading channel case is also extended to an M-player case.