Ultra-reliable and low-latency communication (URLLC) is a key enabler for mission-critical applications in next-generation wireless networks. However, achieving both ultra-high reliability and ultra-low latency simultaneously remains a major challenge. This paper proposes a novel Dual Prediction Scheme (DPS) for URLLC-based teleoperation systems, where predictive algorithms are deployed at both the transmitter and the receiver to jointly mitigate latency and enhance reliability. In the proposed framework, the receiver reconstructs delayed or lost packets through local prediction, while the transmitter proactively encapsulates multiple predicted future states into a single short packet to safeguard against consecutive losses. To evaluate system reliability and energy efficiency, we formulate a joint optimization problem that minimizes the average transmit power subject to URLLC constraints by jointly optimizing bandwidth allocation and prediction horizons. The resulting problem is non-convex and is efficiently solved via an iterative algorithm. Simulation results verify that the proposed DPS significantly reduces transmit power while satisfying URLLC requirements, demonstrating its strong potential for real-time and energy-efficient wireless teleoperation systems.

Unmanned aerial vehicles (UAV)-based edge computing (EC) has been a core enabler for task offloading in the last decade with a major focus on UAV energy and task processing delay minimization, especially for delay-sensitive applications that require high availability of task offloading services throughout the day, such as vehicular networks. In this work, we tackle the problem of task offloading for internet of things (IoT) devices in UAV-based EC networks by introducing compute reuse for result sharing. The goal is to minimize the energy consumption of the edge-computing UAVs and the task processing delay. By considering the original problem split into two subproblems on two different time scales: the small time scale handles the forwarding decisions and the CPU operating frequency at each UAV, while the large time scale handles the wake-sleep status of each UAV. Most existing work in this area ignores the discussion of result reuse in a UAV edge setup and considers a single agent on each time scale without sequential decision-making. Additionally, the literature concerning sending UAVs to sleep for energy savings under edge computing framework or the integration of both sequentiality and hierarchical deep reinforcement learning (DRL) under a multi-time scale framework remains unexplored in the literature. An algorithm based on a hierarchical sequential multi-agent deep reinforcement learning (HSMADRL) framework is developed. The hierarchical DRL explores patterns between the two time scales; and the sequential and multi-agent designs address the curse of dimensionality and decision-making coordination, respectively. Simulation results show that our algorithm can quickly converge and outperform baselines, while the energy computation efficiency (ECE) is maximized.

This paper introduces a novel secure split federated semantic learning (SFsL) framework to facilitate the maintenance and evolution of digital twin networks (DTNs). Efficiently updating and evolving DTNs generally involves several critical processes: semantic extraction and transmission for physical-to-virtual synchronization, virtual model transformation and verification, and ensuring the security and privacy of physical entity data. While conventional semantic communication frameworks can effectively address semantic extraction and transmission, the complexities of virtual model transformation, verification, and data security demand a more comprehensive approach. To address these challenges, the proposed SFsL framework integrates split federated learning with task-oriented secure semantic communication schemes. In addition, it incorporates a token-based semantic defence method to distinguish between adversarial and authentic semantic data and an asynchronous secure model aggregation mechanism to enhance data-sharing efficiency. The system reliability is then formulated as a stochastic optimization problem, aiming to minimize cost complexity while maintaining high accuracy during periodic model aggregation. Evaluation results, obtained using performance metrics such as privacy loss, experienced loss, accuracy, cost and reliability, demonstrate that the SFsL framework outperforms other commonly adopted security and privacy schemes, offering improved efficiency towards the maintenance and evolution of such dynamic systems. This highlights the capability of SFsL to enable adaptive, efficient and reliable network evolutions when deployed in practical DTNs with dynamic resource constraints.

As a popular service management system, the spatial crowdsourcing (SC) server is responsible for allocating nearby workers to perform tasks based on outsourced locations. However, protecting the sensitive information contained in these outsourced locations is crucial. Traditional differential privacy (DP) methods suffer from two limitations: 1) they usually rely on a trusted third party, failing to protect both worker and task location privacy simultaneously, thus risking privacy breaches; 2) they ignore the personalized privacy demands of different users. In this paper, we propose a personalized local DP-based location obfuscation (PLDPLO) scheme, thereby providing personalized privacy-preserving both worker and task locations locally while allocating high-quality tasks. To achieve this, we introduce a personalized location indistinguishability (PLI) model, a new personalized Laplace mechanism achieving local DP, to jointly provide the protection of worker locations and different privacy levels for different workers. To address task privacy, we present a spatial mapping indistinguishability (SMI) algorithm to obfuscate task locations based on a random response mechanism, thereby ensuring data utility. Additionally, we propose a Zipf-Poisson model-based task allocation graph (ZPTAG) algorithm to perform one-task-multiple-workers allocation and achieve a high competitive ratio, which reduces the move distance of workers. Our PLDPLO scheme guarantees ϵ-LDP. Extensive experiments over real datasets demonstrate that our scheme achieves over 89% data utility for task allocation and outperforms state-of-the-art methods while providing personalized privacy levels.

The rapid proliferation of the Internet of Things has given rise to a multitude of real-time applications, which pose significant computing challenges for resource-constrained users. Air-ground collaborative mobile edge computing (AGMEC) emerges as an innovative solution, integrating aerial and terrestrial computing paradigms to provide flexible, efficient services that significantly enhance data processing capabilities. This paper focuses on the freshness of task data in AGMEC networks, characterized by the emerging metric of age of information (AoI). Due to limited spectrum resources and network coverage gaps, we introduce non-orthogonal multiple access (NOMA) and active reconfigurable intelligent surface (RIS) technologies to facilitate efficient task offloading. We formulate a joint optimization problem of uncrewed aerial vehicle trajectory, active RIS beamforming, and task offloading strategy to minimize the network’s average AoI under multidimensional constraints. Considering the non-convex nature and the dynamic characteristics of the AGMEC environment, we develop an action adjuster-based deep deterministic policy gradient (AADDPG) algorithm. The innovative design of the action adjuster enables the algorithm to not only achieve efficient processing of hybrid action spaces but also effectively protect UAV battery performance. Simulation results demonstrate that the proposed AADDPG algorithm significantly improves AoI performance compared to other benchmark algorithms. Additionally, the results corroborate the efficacy of both NOMA and active RIS in minimizing AoI for AGMEC networks.

With the advent of 6G networks, digital twin (DT) systems have become critical for real-time monitoring, decisionmaking, and control across various sectors. A digital twin is a virtual representation of a physical twin (PT), enabling continuous interaction and data exchange. However, real-time communication between the DT and PT incurs variable costs as users change locations, significantly affecting system efficiency and user experience. In mobile environments, minimizing these communication costs is essential for maintaining DT performance, as increased latency and resource demands arise with user mobility. Consequently, an optimal dynamic placement strategy for DTs on edge servers is crucial to reducing communication overhead while ensuring responsiveness. This work introduces an optimization framework leveraging Lyapunov optimization to model and minimize communication costs between the Human digital twin (HDT) and PT, considering task drops during twin migration. The proposed solution dynamically adapts to user movements and network conditions, ensuring efficient real-time interactions with minimal costs. Evaluation results demonstrate the effectiveness of our approach in reducing communication costs and task drops while maintaining data exchange quality and reliability in 6G-enabled DT systems.

Maintaining robust physical-to-virtual twin connectivity remains a critical challenge in the deployment of highfidelity digital twin networks (DTNs). To ensure that each virtual twin (VT) in such DTNs is a true replica of its counterpart physical twin (PT), a reliable and cost-effective connectivity framework is essential, especially considering the potential mobility of PTs in the physical environment. This paper thus presents a new multi-modal PT trajectory prediction-enabled multi-connectivity framework where the counterpart VT model is proactively duplicated along multiple predicted paths to compensate for the effects of prediction errors, thereby avoiding communication loss between such PT-VT pair. While such a proposed scheme can improve the overall reliability of the network, the resulting connectivity solution may suffer from network congestion issues, complicating the overall cost. To address this, we analyze the trade-offs between overall reliability and cost and formulate an optimal VT placement problem aimed at minimizing delay, longterm connectivity cost, and prediction errors. We then propose a deep reinforcement learning-based adaptive VT placement strategy for cost-effective, mobility-aware PT-VT connectivity. Simulation results show the solution effectively meets the reliability and cost requirements of DTNs.

Multi-beam low earth orbit (LEO) satellites, with their wide coverage, high communication rates, low latency, and flexibility, are essential components in the 5G and 6G eras. However, challenges such as uneven ground user distribution, multi-dimensional resource coupling, inter-beam interference, and dynamic network topology complicate efficient resource management. This paper presents a joint beam hopping and resource allocation (BHRA) scheme within a Digital Twin (DT)empowered multi-beam LEO satellites network. The complex resource management problem is divided into two sub-problems: traffic-satellite allocation and joint multi-satellite BHRA. First, cell traffic is allocated among satellites to balance load using deep reinforcement learning (DRL). Then, Hierarchical Proximal Policy Optimization (HPPO) optimizes multi-satellite beam hopping and resource allocation to meet demand. Extensive simulation results demonstrate that the proposed method reduces satellite load imbalance by approximately 83%, achieves the highest throughput compared to other algorithms, and generalizes well as traffic demand increases.

In computer multimedia, Federated Learning (FL) enables clients to contribute their multimedia data, thereby improving the overall accuracy of the model. However, clients’ sensitive multimedia data could still be inferred through the submitted local models in FL. As a result, differential privacy (DP) techniques have been adopted to protect clients’ sensitive multimedia data. But existing works primarily focused on adding fixed noise to data, gradients, or loss functions, which can negatively affect the model’s accuracy and convergence. To address this issue, we propose a novel differential privacy federated learning framework (DP-FedAR) which consists of two modules, i.e., an adaptive budget allocation method and a reversion mechanism. Specifically, based on model similarity, an adaptive allocation rule is proposed to assign privacy budgets in real time for each training round. Then, in order to avoid exhausting the privacy budget of each client too early in the whole training process, a reversion mechanism is further devised to identify clients’ historical models that mostly resemble the current global model. Theoretical analyses demonstrate that our proposed DP-FedAR can converge and has a strict privacy guarantee. Moreover, extensive simulations validate that our proposed DP-FedAR outperforms existing algorithms in terms of training accuracy. More precisely, the accuracy of DP-FedAR surpasses that of counterparts, with an average improvement of 8% to 12%

Maintaining continuous quality of experience (QoE) for adaptive video streaming in internet of vehicle (IoV) networks is significantly challenging due to rapid user mobility and highly dynamic network conditions. To address this, we present a network digital twin (NDT)-enhanced QoE optimization scheme tailored for the unique demands of IoV environments. Our approach integrates a context-aware gated recurrent unit (GRU) within the NDT to proactively predict short-term bandwidth fluctuations and enable anticipatory bitrate adaptation. We introduce a user-centric QoE model that dynamically incorporates the real-time perceptual QoE factors derived from vehicular user digital twins (VUEDTs). Furthermore, we formulate a joint optimization problem that simultaneously determines optimal video bitrate and resource allocation to maximize user-specific QoE under high-mobility IoV scenario. This problem is modeled as a Markov decision process and solved using a proximal policy optimization learning framework. Simulation results show that our proposed scheme can achieve significantly improved user QoE when compared to other baseline schemes.

The rapid advancement of wireless and cyber-physical systems has driven a growing demand for real-time sensor data in cyber environments. While existing solutions attempt to meet these demands, achieving both high-throughput processing and robust data integrity remains a significant challenge. This paper proposes an end-to-end distributed data pipeline and blockchain-enabled framework that integrates online anomaly detection, scalable data aggregation, and secure verification to ensure reliable cyber evolution. An Apache Kafka-based streaming pipeline ingests high-velocity sensor data and employs a dynamic bucketing strategy that finalizes buckets based on data volume, elapsed time, and network gas costs. Once validated, each bucket’s canonical sensor data representation is hashed and committed on-chain for tamper-evident storage. To enhance efficiency and security, the framework supports both Merkle tree and Verkle tree cryptographic data structures for comparative analysis. Implemented on a private Ethereum-like blockchain, our system efficiently handles large-scale sensor ingestion while enabling per-record verification. By integrating real-time anomaly correction, cryptographic proof mechanisms, and on-chain commitments, our solution delivers trustworthy, verifiable sensor streams tailored to the low-latency and high-reliability needs of next-generation systems.

Human digital twin (HDT) is an innovative concept that creates digital representations of humans to support human-centric services by enabling real-time data synchronization between the physical twin (PT) and the virtual twin (VT). This PT-VT synchronization process is inherently data-intensive, requiring efficient resource management, especially in resource-constrained environments. Semantic communication has emerged as a promising alternative to traditional data-driven methods. However, single-encoder models may struggle to meet the dynamic requirements of HDT applications, particularly when resources are limited. To address these challenges, this paper introduces a multi-encoder semantic communication model that dynamically allocates resources—such as bandwidth and computational frequency—on specific application demands. The framework is formulated as a mixed-integer nonlinear programming (MINLP) problem and is solved using a genetic algorithm (GA) based approach. Simulation results demonstrate that the proposed model optimizes synchronization while effectively managing power consumption, outperforming traditional single-encoder models in terms of accuracy, latency, and resource efficiency. This dynamic multi-encoder approach offers a scalable and adaptable solution for future HDT applications.

By fully exploring the edge computing “supply-demand” relationship between the mobile edge computing (MEC) servers and the differentiated application requests, the computing pricing (i.e.“, supply”) and allocating (i.e.“, demand”) can be coordinated well for the practical network consisting of heterogeneous users and MEC operator. In this paper, the fair-aware computing pricing, beneficial offloading (i.e., obtaining positive utility) and local computing adjustment are jointly discussed under a pricing-enabled MEC. By considering heterogeneous application requests, fair service demand and limited computing provisioning, a multi-objective composite utility optimization is developed to maximize the user utility and the MEC operator profit simultaneously. Therein, the fair service condition is proposed, under which each user can experience a similar chance to obtain beneficial offloading. In order to solve the goal problem with undetermined objective function and conditions, a fair service enabled pricing and allocating algorithm (FS_PAA) with extremely low complexity is proposed by exploiting classification discussion method and convex optimization. Our FS_PAA reveals the explicit relationship between the optimal offloading decision and computing pricing, and the explicit relationship between the optimal computing pricing and the maximum computing provisioning, which helps to provide an effective reference for practical edge computing deployment. Simulation results show that our FS_PAA can 1) ensure fair offloading services for practical differentiated requests; 2) provide green offloading service for more users; 3) greatly improve the utilization of edge computing resource.

In the operational context of a cross-device federated learning (FL), the efficient allocation of resources, such as transmission powers, channels, and computation resources, significantly impacts overall performance. Existing research in cross-device FL has predominantly concentrated on either resource allocation to enhance training accuracy or incentivizing participation, while ignoring their integrated designs for further improving the performance in cross-device FL. Different from existing work, in this paper, we jointly integrate the power allocation, channel assignment, user selection, and allocation of computation frequency into the design of incentive mechanism, where each mobile user plays a dual role as both a buyer and a seller. Because of complex resource allocation, truthfulness guarantee in a dual role scenario, and unavailable prior information, the considered mechanism design problem is challenging. To tackle such combinatorial problem, we propose a Reinforcement Auction Mechanism (RAM), comprising two layers. The upper layer features a Hybrid Action Reinforcement Learning scheme to learn the outcomes of user selection and payments. In the lower layer, each selected mobile user optimizes its resources to maximize its utility. Theoretical analyses affirm that our proposed RAM ensures individual rationality and truthfulness. Extensive simulations have been conducted to validate the effectiveness of the proposed RAM.

Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (VTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized VTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT.

Low-Earth orbit (LEO) satellites utilizing beam hopping (BH) technology offer extensive coverage, low latency, high bandwidth, and significant flexibility. However, the uneven geographical distribution and temporal variability of ground traffic demands, combined with the high mobility of LEO satellites, present significant challenges for efficient beam resource utilization. Traditional BH methods based on GEO satellites fail to address issues such as satellite interference, overlapping coverage, and mobility. This paper explores a Digital Twin (DT)-based collaborative resource allocation network for multiple LEO satellites with overlapping coverage areas. A two-tier optimization problem, focusing on load balancing and cell service fairness, is proposed to maximize throughput and minimize inter-cell service delay. The DT layer optimizes the allocation of overlapping coverage cells by designing BH patterns for each satellite, while the LEO layer optimizes power allocation for each selected service cell. At the DT layer, an Actor-Critic network is deployed on each agent, with a global critic network in the cloud center. The A3C algorithm is employed to optimize the DT layer. Concurrently, the LEO layer optimization is performed using a Multi-Agent Reinforcement Learning algorithm, where each beam functions as an independent agent. The simulation results show that this method reduces satellite load disparity by about 72.5% and decreases the average delay to 12ms. Additionally, our approach outperforms other benchmarks in terms of throughput, ensuring a better alignment between offered and requested data.

In this paper, we introduce third-party intelligent reflecting surfaces (TIRSs) into the physical layer security aware wireless communication system, where a central legitimate transmitter is designed to transmit secret signals to a group of legitimate receivers in the presence of the threat from an active eavesdropper (EV). Due to the channel reshaping ability of TIRSs, they are able to not only help legitimate pairs (LPs) enhance the secure transmission rate but also assist EV in improving the eavesdropping performance. Furthermore, with the potential selfishness, TIRSs may dynamically choose to ally with LPs or EV in exchange for potential benefits (e.g., payoffs). This leads to complex dynamic ally-adversary relationships among LPs, EV, and TIRSs under unpredictable wireless channel conditions. To address this issue, we formulate a repeated coalition formation game (RCFG) with dynamic decision-making to model the long-term strategic interactions among LPs, EV, and TIRSs. In particular, we theoretically analyze the existence of Nash equilibrium in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based approach for obtaining such an equilibrium. Simulations examine the feasibility of the proposed approach and show its superiority over counterparts.

With the rapid growth of internet of things (IoT) devices, edge computing has emerged as a crucial technology for delivering low-latency and resource-efficient services. However, the surge in edge computing capabilities poses challenges in efficiently managing result caching and deduplication to effectively utilize storage and processing resources. This study introduces a novel approach that harnesses an adaptive enhanced initiation genetic algorithm (AEIGA) for result deduplication/relocation in collaborative and distributed edge computing networks, with the goal of enhancing task offloading and result delivery. Our work proposes an optimization framework that integrates deduplication/relocation strategies to minimize redundancy, improve latency, and optimize storage across edge servers. The proposed AEIGA addresses the NP-hard nature of the formulated optimization problem by efficiently exploring the vast solution space to find optimal or near-optimal solutions. Simulation results demonstrate significant improvements of the proposed AEIGA in various network performance metrics. Our findings highlight the effectiveness of employing AEIGA for result deduplication/relocation in edge computing, offering a scalable solution to meet the escalating demands of IoT networks.

In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing work, we allow UAVs to dynamically cluster into different swarms, i.e., each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. Meanwhile, UAVs are required to dynamically schedule their energy replenishment, application placement, trajectory planning and task delegation. With the aim of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of dynamic clustering and scheduling is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we further reformulate this optimization problem as a combination of a series of strongly coupled multi-agent stochastic games, and then propose a novel reinforcement learning-based UAV swarm dynamic coordination (RLDC) algorithm for obtaining the equilibrium. Simulations are conducted to evaluate the performance of the RLDC algorithm and demonstrate its superiority over counterparts.

In this paper, a repeated coalition formation game (RCFG) with dynamic decision-making for physical layer security (PLS) in wireless communications with intelligent reflecting surfaces (IRSs) has been investigated. In the considered system, one central legitimate transmitter (LT) aims to transmit secret signals to a group of legitimate receivers (LRs) under the threat of a proactive eavesdropper (EV), while there exist a number of third-party IRSs (TIRSs) which can choose to form a coalition with either legitimate pairs (LPs) or the EV to improve their respective performances in exchange for potential benefits (e.g., payments). Unlike existing works that commonly restricted to friendly IRSs or malicious IRSs only, we study the complicated dynamic ally-adversary relationships among LPs, EV and TIRSs, under unpre-dictable wireless channel conditions, and introduce a RCFG to model their long-term strategic interactions. Particularly, we first analyze the existence of Nash equilibrium (NE) in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based algorithm for obtaining such equilibrium. Simulations examine the feasibility of the proposed algorithm and show its superiority over counterparts.

In this article, we consider incentive mechanism designs in asynchronous federated learning (FL) systems. With the consideration of unique characteristics inherent in asynchronous FL, such as dynamic participating and multiminded IoT nodes such as mobile users (MUs), requirements of model training (i.e., training accuracy and convergence time), and limited uplink bandwidth, we formulate considered system as an online incentive mechanism design problem, where each MU is not only a buyer for communication resource but also a seller for computation service. To address the challenges involved in the design, we first derive the relationship between the number of participants and the global training accuracy in asynchronous FL. Then, based on that, we propose a novel mechanism, called the online incentive mechanism for asynchronous FL (OIMAF). To the best of our knowledge, this is the first work to design incentive mechanisms for asynchronous FL. Furthermore, in order to obtain a more robust mechanism, an improved online mechanism, called the two-shot-based online incentive mechanism (TOIM), is proposed by using OIMAF as a building block. Theoretical analyses show that our proposed online incentive mechanisms can guarantee individual rationality, truthfulness, a sound performance, and solution feasibilities. We further conduct comprehensive simulations to validate the effectiveness of our proposed mechanisms.

Secure data sharing is imperative in human digital twin (HDT) systems due to the continuous communication requirements among physical and virtual twins, making data security and privacy essential concerns. Previous works have emphasized the significance of blockchain technology in mitigating security challenges within digital twin systems. Nevertheless, existing blockchain-based solutions often fall short of meeting the specific latency and throughput demands of HDT systems, primarily attributed to the complicated consensus process of conventional blockchain solutions. As a result, this paper introduces a novel reputation-enhanced shard-based Byzantine fault-tolerant scheme designed for zero-trust HDT systems. We propose a parallel validation-based reputation-enhanced practical Byzantine fault tolerance consensus framework to address the need for improved throughput and reduced latency during data-sharing processes. This framework incorporates a priority-based block-appending process to prevent forking attacks, ensuring that critical aspects of the blockchain-enabled framework, such as security and decentralization, remain uncompromised. Moreover, we formalize the communication process among validators and their computation resource allocation as a Markov decision process. We then adopt the branching duelling Q-network approach to address the challenge posed by the large dimensions of the action space in our formulated problem. The results demonstrate that the proposed framework significantly enhances authentication, authorization, and validation processes in HDT through increased throughput and reduced latency, providing a robust solution for secure and efficient data sharing in HDT systems.

Container–based microservice provisioning, with its elasticity in terms of the layered structure, enables the sharing of common layers among different edge computing tasks, both within and across edge servers (ES). However, due to the potential hardware breakdowns, each ES may prone to failures, affecting its lifetime (i.e., the time-length that an ES works continuously without interruptions), and in turn leading to the collapse of their hosted/provided microservices or the other ESs’ microservices requesting common layers from it. To address such an issue, in this paper, we study the microservice deployment optimization with layer sharing for maximizing the system-wide reliability while satisfying all tasks’ delay requirements. Considering dynamic task generations and the asynchronization of various decision variables with different triggers, we design an online optimization algorithm by leveraging an improved Lyapunov technique integrating randomized rounding, Lagrangian method and convex optimization, which iteratively solves the problem over different timescales. Theoretical analyses and simulations evaluate the performance of the proposed solution, showing that it can achieve an increase of 12.4% in reliability and a reduction of 28.57% in total delay, compared to the counterparts.

Due to the potential of revolutionizing a variety of human-centric services, human digital twin (HDT) is envisioned to become an important part of our daily life. The HDT applications need to frequently collect and process data obtained from individuals and their environment, analyzing each physical twin while updating its corresponding virtual twin, which will consume a large amount of computing, storage and sensing resources cumulatively. Meanwhile, running HDT applications, such as emotion recognition, naturally contains the executions of several dependent tasks. Considering the resource limitations of mobile terminals, we enable dependent task offloading to mitigate terminal load and reduce the latency of HDT applications. Specifically, this paper proposes an energy and cost-aware offloading algorithm for dependent tasks with edge-cloud collaboration to empower HDT applications. We show that the problem of dependent task offloading under constraints of service cost and terminal energy consumption is NP-hard. The complexity of task interdependency makes the offloading decision under dual constraints even more challenging. The proposed offloading algorithm firstly generates task paths based on task interdependency and computation load, deriving the initial solution. Then, task reassignment and CPU frequency scaling methods are utilized to further optimize the obtained solution. Simulation results illustrate that our approach can achieve better performance in terms of makespan and service success ratio compared to the existing approaches.

Data timeliness, privacy, and security are key enablers for data-sharing systems to support time-sensitive and mission-critical systems and applications. While blockchain-enabled data sharing frameworks can offer reliable security and privacy when properly implemented, the timeliness of data and the related latency are important issues that can limit the adoption of blockchain in large-scale mission-critical applications. This paper thus carried out a performance analysis of the blockchain-enabled data-sharing framework from latency and data age perspectives to investigate the suitability of blockchain technology in data sharing systems. To achieve this, the uniqueness of such systems such as transactions validation latency, transaction generation rate, waiting time, blockchain-appending rate, and overall communication latency were jointly studied. The communication latency was characterized following the spatiotemporal modeling approach. We further adopted the practical Byzantine fault tolerance (PBFT) consensus protocol due to its well discussed suitability in large-scale data sharing applications and captured the validation stages of such a PBFT scheme using the Erlang distribution of order$k$. Simulations results show that various influential system parameters must be carefully considered when adopting blockchain technology in time-sensitive data sharing applications. This will guide the adoption of blockchain technology in various data sharing applications and systems.

Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs' corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT's inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs' construction and PTs' task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts.

In this paper, a novel hierarchical game framework for physical layer security (PLS) aware wireless communications with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model strategic interactions among LUs, JAs and EVs. Particularly, we first analyze stability conditions of the trilateral coalitions and propose a hedonic coalition selection and formation algorithm for reaching the stable coalition partition in each time slot. On top of this, we propose a deep reinforcement learning (DRL) based solution, which can achieve the equilibrium with long-term performance guarantees for the hierarchical game running over multiple time slots with dynamic evolutions. Simulations evaluate the proposed solution and show its superiority over counterparts.

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users’ rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user’s diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region.

Mobile edge computing (MEC) enables communication users with limited computation power to offload computation-intensive tasks to the edge server, thus dramatically enhancing the limited computing capabilities of the users. As the reality of scarce spectrum resources and the energy-constrained nature of communication users, this paper introduces non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) techniques to achieve more efficient task offloading in MEC. To minimize the number of computationally failed tasks while simultaneously satisfying different quality of service (QoS) requirements of users, a joint resource management problem of the spectrum, computation, and energy resources is formulated. Due to the non-convexity of the offloading optimization problem and the stochastic nature of the constructed MEC environment, a multiple agents deep deterministic policy gradient (MADDPG)-based resource management algorithm is proposed to manage each user’s multidimensional resources without collaborating. The simulation results show that compared to other benchmark schemes, the proposed algorithm can effectively improve both the communication and computational performances in MEC.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. Unlike existing studies, for providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs’ task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining: 1) large-timescale decisions, including which edge server should be selected to access, and whether service migration or task rerouting should be chosen for each MD in each large time frame; and 2) small-time scale decisions, including how computing and communication resources should be allocated among MDs with task offloading requests in each small time slot. Then, we propose an online algorithm based on the improved Lyapunov method, together with an iterative algorithm integrating randomized rounding and Lagrange dual techniques, which solves the problem to asymptotic optimum in terms of the long-term average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution and show its superiority over counterparts.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. For providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs' task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining$i$) large-timescale decisions, including access selection and service migration or task rerouting selection for each MD, and ii) small-time scale decisions, including computing and communication resource allocations. Then, we propose a two-timescale low-complexity algorithm based on the improved Lyapunov method, which solves the problem to asymptotic optimum in terms of the long-term system-wide average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution, and show its superiority over counterparts.

In this paper, an energy efficient scheduling problem for multiple unmanned aerial vehicle (UAV) assisted mobile edge computing is studied. In the considered model, UAVs act as mobile edge servers to provide computing services to end-users with task offloading requests. Unlike existing works, we allow UAVs to determine not only their trajectories but also decisions of whether returning to the depot for replenishing energies and updating application placements (due to limited batteries and storage capacities). Aiming to maximize the long-term energy efficiency of all UAVs, i.e., total amount of offloaded tasks computed by all UAVs over their total energy consumption, a joint optimization of UAVs, trajectory planning, energy renewal and application placement is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we reformulate such problem as three coupled multi-agent stochastic games, and then propose a novel triple learner based reinforcement learning approach, integrating a trajectory learner, an energy learner and an application learner, for reaching equilibriums. Simulations evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

In this paper, a novel hierarchical game framework for physical layer security (PLS) with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under the uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model the strategic interactions among all three parties, i.e., LUs, JAs and EVs, in PLS. Particularly, we first analyze stability conditions of the trilateral coalitions. On top of this, we further propose a deep reinforcement learning (DRL) based approach for reaching the equilibrium with long-term performance guarantees for the hierarchical game. Simulations evaluate the proposed solution and show its superiority over counterparts.

In recent years, COVID-19 has spread rapidly around the world, leading to a global pandemic, which has become an unprecedented crisis for almost every country in the world. In this paper, we propose a novel federated learning (FL) algorithm to train a sensitivity-specificity-variable COVID-19 diagnosis model. By FL, patients' data stays at each hospital locally, and thus the privacy of patients is reserved. However, the commonly used FL algorithms, such as FedAvg cannot perform COVID-19 diagnosis efficiently because they did not consider the impact of noise and heterogeneity in the chest X-ray (CXR) data of different hospitals. Moreover, they commonly assumed that hospitals would voluntarily participate in FL without payments. To this end, our FL algorithm integrates a novel data selection module to distinguish participants having data with low noise, high representative distribution, and a payment scheme to incentivize each participant according to their contributions. Our contribution evaluation method is based on the Shapley value method widely applied in coalitional games. Compared to the existing works, our solution does not need to train models repeatedly, which significantly reduces the time and computation resource consumption, while achieving a competitive performance as shown in experiments.

This paper studies the dynamic application placement for edge computing with a variety of random task arrivals (or task offloading requests). Since the storage capacity of the edge server is inherently limited, for better serving mobile devices with heterogeneous computation demands, the edge server is required to update its application placement, leading to a potential energy-latency paradox (i.e., frequent updates may introduce a high energy consumption while infrequent updates may result in the growth of latency). To this end, we propose a novel application placement policy, consisting of a response threshold and a waiting duration for installing and uninstalling the application for each type of task, respectively. Furthermore, we leverage the vacation queue for analyzing the performance of such system, and formulate a joint optimization problem for deriving the optimal configurations of application placement, along with the computation resource allocations, in minimizing the average service latency with a desired energy constraint. A branch-and-bound method integrating an inner convex approximation approach is proposed, and then evaluated with numerical simulations which demonstrate its superiority over counterparts.

In this article, an energy-efficient scheduling problem for multiple unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) is studied. In the considered model, UAVs act as mobile edge servers to provide computing services to end-users with task offloading requests. Unlike existing works, we allow UAVs to determine not only their trajectories but also the decisions of whether returning to the depot for replenishing energies and updating application placements (due to their limited batteries and storage capacities). With the aim of maximizing the long-term energy efficiency of all UAVs, i.e., the total amount of offloaded tasks computed by all UAVs over their total energy consumption, a joint optimization of UAVs’ trajectory planning, energy renewal, and application placement is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we reformulate such optimization problem as three coupled multiagent stochastic games. Since the prior environment information is unavailable to UAVs, we propose a novel triple-learner-based reinforcement learning (TLRL) approach, integrating a trajectory learner, an energy learner, and an application learner, for reaching equilibriums. Moreover, we analyze the convergence and the complexity of the proposed solution. Simulations are conducted to evaluate the performance of the proposed TLRL approach, and demonstrate its superiority over counterparts.

Ensuring reliable update and evolution of a virtual twin in human digital twin (HDT) systems depends on any connectivity scheme implemented between such a virtual twin and its physical counterpart. The adopted connectivity scheme must consider HDT-specific requirements including privacy, security, accuracy and the overall connectivity cost. This paper presents a new, secure, privacy-preserving and efficient human-to-virtual twin connectivity scheme for HDT by integrating three key techniques: differential privacy, federated multi-task learning and blockchain. Specifically, we adopt federated multi-task learning, a personalized learning method capable of providing higher accuracy, to capture the impact of heterogeneous environments. Next, we propose a new validation process based on the quality of trained models during the federated multi-task learning process to guarantee accurate and authorized model evolution in the virtual environment. The proposed framework accelerates the learning process without sacrificing accuracy, privacy and communication costs which, we believe, are non-negotiable requirements of HDT networks. Finally, we compare the proposed connectivity scheme with related solutions and show that the proposed scheme can enhance security, privacy and accuracy while reducing the overall connectivity cost.

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance.

In this paper, we consider task offloading in edge computing systems, where tasks are offloaded by the base station to resourceful mobile users. With the consideration of unique characteristics in practical edge computing systems, such as dynamic arrival of computation tasks, and energy constraints at battery-powered mobile users, we formulate an incentive mechanism design problem by jointly optimizing task offloading decisions, and allocation of both communications (i.e., power and bandwidth), and computation resources. In order to tackle the nonlinear issue in the designed mechanism, a novel online incentive mechanism is proposed. We first convert the original mechanism design problem into several one-shot design problems by temporally removing the energy constraint. Then, we propose a new mechanism design framework, called the Integrate Rounding Scheme based Maxima-in-distributional Range (IRSM), and based on that, design a new incentive mechanism for each one-shot problem. Finally, we reconsider energy constraints to design a new nonlinear online incentive mechanism by rationally combining the previously derived one-shot ones. Theoretical analyses show that our proposed nonlinear online incentive mechanism can guarantee individual rationality, truthfulness, a sound competitive ratio, and computational efficiency. We further conduct comprehensive simulations to validate the effectiveness and superiority of our proposed mechanism.

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users’ computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users’ spectrum, which greatly improves the spectrum efficiency and effectively relieves the massive access problem of 5G and beyond networks. In this paper, we first study the outage performance of the greedy best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. In order to tackle the admission fairness problem of the BU-SGF scheme, we propose a fair SGF scheme by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), in which the GF user with the best channel relative to its own statistics will be admitted. Moreover, by employing the theories of order statistics and stochastic geometry, the outage performances of both BU-SGF and CS-SGF schemes are analyzed. Theoretical results show that both schemes can achieve full diversity orders only when the served users’ data rate is capped, which severely limits the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive analytical expressions of the two schemes’ outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses.

In this paper, a joint optimization of sensor activation and mobile charging scheduling for industrial wireless rechargeable sensor networks (IWRSNs) is studied. In the considered model, an optimal sensor set is selected to collaboratively execute a bundle of heterogeneous tasks of production-line monitoring, meeting the quality-of-monitoring (QoM) of each individual task. There is a mobile charger vehicle (MCV) which is scheduled for recharging sensors before their charging deadlines (i.e., the time instant of running out of their energy). Our goal is to jointly optimize the sensor activation and MCV scheduling for minimizing the energy consumption of the entire IWRSN, subjected to tasks’ QoM requirements, sensor charging deadlines and the energy capacity of the MCV. Unfortunately, solving this problem is non-trivial, because it involves solving two tightly coupled NP-hard problems. To address this issue, we design an efficient algorithm integrating deep reinforcement learning and marginal product based approximation algorithm. Simulations are conducted to evaluate the performance of the proposed solution and demonstrate its superiority over counterparts.

In this paper, an energy-efficient resource management framework for industrial Internet of Things (IIoT) with closed-loop control on end devices, edge servers (ESs) and cloud center (CC) is studied. In the considered model, each ES aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of i) sensors’ sampling rate adaption, ii) ESs’ preprocessing mode selection and iii) edge-cloud communication and computing resource allocation, is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Performance analyses and simulation results show that the proposed algorithm is superior compared to counterparts in terms of energy efficiency and delay performance under service satisfaction constraints.

In this paper, a multi-layer edge computing frame-work for the virtual machine (VM) placement and computation offloading in industrial Internet of Things (IIoT) is proposed. Unlike most existing works, we focus on addressing the temporal dependency among tasks in an IIoT task flow, and consider that there is a stringent requirement on its completion time (including the transmission time, computation time and waiting time). For striking a balance between the system completion time and the energy consumption while satisfying the storage capacity of edge servers (ESs), completion deadline of time-sequential task flows, and placement requirements of VMs, we design a many-to-one matching game (MGVDA) to jointly determine the optimal VM placement and task offloading decisions. Finally, we prove that the resulted matching game solution is effective and stable. Simulation results examine the efficiency of the proposed MGVDA and show its superiority over the counterparts.

The human digital twin (HDT) is a new paradigm that possesses the ability to revolutionize the current healthcare systems. With HDT, ensuring an efficient connectivity scheme between each human-virtual twin pair remains a significant problem. As the concept of HDT is new, conventional connectivity schemes cannot meet the unique requirements of HDT in terms of reliability, security and privacy. This paper thus proposes an edge-assisted connectivity scheme for HDT and adopts an integrated blockchain and federated learning techniques to ensure security and privacy. To minimize long-term average connectivity cost, we formulated the connectivity problem as a Markov decision process and adopted the deep deterministic policy gradient (DDPG) algorithm to learn the optimal connectivity policy in terms of connectivity cost. The obtained results were then compared with the conventional deep Q-network-based solution. The results show that the proposed DDPG-based connectivity solution is feasible to perform the connectivity process better by optimally allocating system resources, thus reducing the overall connectivity cost, while ensuring data security and privacy.

A part of off-the-shelf wireless communication chips can only provide very coarse-grained information of the distance range between the transmitter and receiver. Exploiting such chips for precise wireless indoor positioning (WIP) becomes very valuable since their costs are comparatively low. For the WIP systems built on the coarse-grained communication chips, this article discusses the optimal anchor node deployment problem for fingerprint localization. The problem is very challenging since it implies three particularly important requirements of adaptive localization precision, unique fingerprint, and a minimum number of anchor nodes. To minimize the required number of anchor nodes while satisfying the three requirements, we propose two anchor node deployment algorithms, namely, ANDAs and WANDA, both of which are developed for mediate and large-area deployment, respectively. We first prove the effectiveness of the chip in maintaining robust fingerprints and formulate the deployment optimization problem as a minimum attribute reduction problem. Then, to deal with the high computational complexity involved, ANDA is proposed by significantly reducing the search space of the optimization problem. To practically address the combination explosion problem for large-area deployment, the idea of area partitioning is adopted. Investigating anchor nodes at different grid vertices in subareas that may have different sharing degrees, WANDA is proposed to achieve an overall anchor node deployment optimization. Simulation and experimental results demonstrate the validity and the superiority of ANDA and WANDA over counterparts. For large-area deployment, compared to ANDA, WANDA can further decrease the total amount of anchor nodes by 17%.

Blockchain and cloud-edge computing techniques are promising technologies for next-generation, secure, and privacy-preserving data-sharing systems. By integrating these technologies, the data demands of many data users, such as research institutes, hospitals, manufacturers, etc., can be met. Despite this promising integration, it is yet to be understood how the vulnerability and uncertainties of wireless communication links between the data producers, blockchain systems, cloud-edge computing-based platforms, and data users, as well as unstable validation parameters can affect the overall performance of such blockchain-enabled data-sharing systems. In this article, we considered a collaborative data-sharing scheme, where multiple data providers and data users collaborate to accomplish data-sharing tasks through the proposed blockchain and cloud-edge computing schemes. We considered the spatial distribution of data providers and data users to follow the independent homogeneous Poisson point process, while the transactions generation rate at each node was also modeled using an independent Bernoulli process. We then obtained analyses for some selected performance metrics of interest and evaluate the performance of the system. The obtained results showed that the proposed analyses can be useful in investigating the performance of any blockchain-enabled data-sharing system. This will aid successful deployments of effective data-sharing systems.

Edge–cloud collaboration is critical in the Industrial Internet of Things (IIoT) for serving computation-intensive tasks (e.g., bearing fault monitoring) that require low-response delay, low energy consumption, and high processing accuracy. In this article, an energy-efficient resource management framework for IIoT with closed-loop control on end devices, edge servers, and cloud center is studied. In the considered model, each edge server aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of: 1) sensors’ sampling rate adaption; 2) edge servers’ preprocessing mode selection; and 3) edge–cloud communication and computing resource allocation is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Particularly, the Lyapunov optimization method is first utilized to decompose the long-term problem into a series of instant ones [mixed-integer nonlinear programming (MINLP) problems], and then a Markov approximation algorithm is applied to solve such instant problems to near optimum with the consideration of future impacts. Performance analyses and simulation results show that the proposed algorithm is feasible under long-term service satisfaction constraints, and its energy consumption and service delay are approximately 20% and 28% lower than those of the benchmark schemes, respectively.

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the non-convexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collaboration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm.

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs’ strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts.

With the increasing wide applications of wearable wireless networks, body-to-body networks (BBNs) have become significantly important to provide timely and reliable data delivery services. For a specific BBN, assessing its theoretically achievable Quality of Service (QoS) is necessary, especially on the key performance metrics of end-to-end delay distribution and packet loss ratio. The existing analysis models in the literature mainly focused on 1-D space scenarios. In this article, BBN in a 2-D area is considered, where mobile nodes freely and stochastically move along lanes. By introducing two new definitions: 1) node entrance probability and 2) network entrance probability, a systematically analytical framework for end-to-end delay distribution and packet loss ratio is presented. The proposed analytical framework is built on three critical techniques: 1) the Markov chain to model node behaviors; 2) the first passage theory to calculate node entrance probability and network entrance probability; and 3) the central limit theory to decrease the computation time for summing up per-hop delay. Simulation results demonstrate the effectiveness and accuracy of our proposed analysis model.

In this paper, joint resource management for device-to-device (D2D) communication assisted multi-tier fog computing is studied. In the considered system model, each subscribed mobile end user can choose to offload its computation task to either an edge server deployed at the base station via the cellular connection or one nearby third-party fog node via the direct D2D connection. After receiving offloading requests from all end users, the network operator determines the optimal management of the fog computing system, including both computation and communication resource allocations, according to its service agreements with end users, energy cost of edge-server processing and total expense in renting third-party fog nodes. With the objective of maximizing the network management profit, a joint multi-dimensional resource optimization problem, integrating link scheduling, channel assignment and power control, is formulated. An optimal solution algorithm is proposed based on the idea of branch-and-price for addressing this complicated mixed integer nonlinear programming problem. To facilitate the practical implementation in large-scale systems, a suboptimal greedy algorithm with significantly reduced computational complexity is also developed. Simulation results examine the efficiency of the proposed D2D-assisted fog computing framework, and demonstrate the superiority of the proposed resource allocation algorithm over the counterparts.

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states.

In this paper, a cumulative distribution function (CDF)-based scheduling scheme for uplink non-orthogonal multiple access (NOMA) network is investigated. With considering imperfect successive interference cancellation (SIC) and SIC power constraint, closed-form expressions for the outage probability of two scheduled users are derived in cognitive-radio-inspired power allocation (CPA) scenario. To get more insights, high SNR approximations of the outage probabilities are given, and the results reveal that the two users can achieve a diversity order linear with the number of users. Simulation results validate the accuracy of the analytical expressions.

In this paper, a novel computation offloading framework for edge computing is proposed. Unlike existing studies, this work considers that for offloading those computation-intensive tasks, mobile users are allowed to intentionally defer the declarations of their offloading requests and reserve some time for local pre-processing. By doing so, the offloading cost (including edge service charge and transmission cost) may be reduced because of less edge service demand, while the delay cost may increase due to later report. To strike the balance, each mobile user can strategically and selfishly determine a best timing of when to declare its offloading request (or the time-length of its local preprocessing). To characterize the resulted strategic interactions, a computation offloading game built upon a queueing model with strategic queue timing is formulated. Theoretical analyses and simulations evaluate the performance of the proposed equilibrium solution and demonstrate its superiority over counterparts.

In this article, an online truthful mechanism is designed for mobile crowdsensing systems. Traditionally, the scenario where participants arrived at the platform in an online manner has been widely discussed in existing works. On the contrary, we focus on random task arrival case to design an online truthful mechanism by jointly considering the cost budget and the requirement of sensed data of each participant. Specifically, when the task arrives, the platform must make decisions in a sequence to select a specific number of participants to obtain a better competitive ratio (CR). To address this issue, an online strategy-proof incentive mechanism is designed to minimize the social cost of the whole system and achieve truthfulness by applying the auction framework. Moreover, in order to further improve the CR of the online algorithm, a more efficient online scheme is proposed if more information on the participants is available at the platform. Theoretical and simulation results demonstrate the effectiveness of our proposed online truthful mechanisms.

In this paper, a mobile edge computing framework with multi-user computation offloading and transmission scheduling for delay-sensitive applications is studied. In the considered model, computation tasks are generated randomly at mobile users along the time. For each task, the mobile user can choose to either process it locally or offload it via the uplink transmission to the edge for cloud computing. To efficiently manage the system, the network regulator is required to employ a network-wide optimal scheme for computation offloading and transmission scheduling while guaranteeing that all mobile users would like to follow (as they may naturally behave strategically for benefiting themselves). By considering tradeoffs between local and edge computing, wireless features and noncooperative game interactions among mobile users, we formulate a mechanism design problem to jointly determine a computation offloading scheme, a transmission scheduling discipline, and a pricing rule. A queueing model is built to analytically describe the packet-level network dynamics. Based on this, we propose a novel mechanism, which can maximize the network social welfare (i.e., the network-wide performance), while achieving a game equilibrium among strategic mobile users. Theoretical and simulation results examine the performance of our proposed mechanism, and demonstrate its superiority over the counterparts.

Resource management in multi-cell multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is challenged by computational complexity, flexible clustering, and potential channel correlation. In this paper, we focus on a combined resource allocation problem: NOMA mobile user (MU) clustering and the base station (BS) selection, to improve system data rate. Different from sum data rate maximization and max-min fairness, we introduce a new objective function, i.e., relative fairness, which integrates MU fairness into system data rate optimization to overcome the domination effect of BS in advantaged situations of sum data rate improving. Moreover, we derive the closed form solution of MIMO-NOMA resource allocation for a single cluster, and it can be employed for any size of cluster. Furthermore, we propose a new two-side coalitional matching approach to jointly optimize MIMO-NOMA clustering and BS selection, which is able to balance the tradeoff between MUs' individual benefits and the overall network performance. The proposed approach is core stable. Pauta-criterion is employed on system performance evaluation to provide a judgement on win-win solutions. In simulation, extensive comparisons provide insightful understanding of our proposed MIMO-NOMA clustering strategy, relative fairness, and the proposed two-side coalitional matching approach.

This paper discusses incentive mechanism design for collaborative task offloading in mobile edge computing (MEC). Different from most existing work in the literature that was based on offline settings, in this paper, an online truthful mechanism integrating computation and communication resource allocation is proposed. In our system model, upon the arrival of a smartphone user who requests task offloading, the base station (BS) needs to make a decision right away without knowing any future information on i) whether to accept or reject this task offloading request and ii) if accepted, who to execute the task (the BS itself or nearby smartphone users called collaborators). By considering each task's specific requirements in terms of data size, delay, and preference, we formulate a social-welfare-maximization problem, which integrates collaborator selection, communication and computation resource allocation, transmission and computation time scheduling, as well as pricing policy design. To solve this complicated problem, a novel online mechanism is proposed based on the primal-dual optimization framework. Theoretical analyses show that our mechanism can guarantee feasibility, truthfulness, and computational efficiency (competitive ratio of 3). We further use comprehensive simulations to validate our analyses and the properties of our proposed mechanism.

In this paper, an online truthful mechanism integrating task executor selection, computation and communication resource allocation is proposed. Different from most existing work in the literature that was based on offline settings, in our system model, upon the arrival of a smartphone user who requests task offloading, the base station (BS) needs to make a decision right away without knowing any future information. By considering each task's specific requirements in terms of data size, delay, and preference, we formulate a social-welfare-maximization problem and propose a novel online mechanism to solve it. Both theoretical analyses and numerical results show that our mechanism can guarantee feasibility, truthfulness, and computational efficiency with a competitive ratio of 3.

In this paper, a novel queueing game framework for computational workload assignment and strategic computing speed control in fog computing is proposed. Unlike most existing studies in the literature, our work jointly addresses two practical issues related to fog computing, i.e., i) computation tasks offloaded by mobile users are generated dynamically over the time; and ii) each third-party fog node can strategically allocate its computing resource (or control its computing speed) for balancing the tradeoff between the reward gained from executing offloaded computation tasks and the cost incurred by the dissatisfied service to its own subscribed tasks. To describe the long-term performance of the computation task distribution/assignment and inherent strategic interactions among fog nodes, a noncooperative game upon a queueing model is formulated. Based on this, an adaptive algorithm is designed to jointly determine the optimal task distribution and the equilibrium computing speed of each fog node. Theoretical analyses and simulation results examine the performance of the proposed approach and demonstrate its superiority over counterparts.

Content dissemination, in particular, small-volume localized content dissemination, represents a killer application in vehicular networks, such as advertising distribution and road traffic alerts. The dissemination of contents in vehicular networks typically relies on the roadside infrastructure and moving vehicles to relay and propagate contents. Due to instinct challenges posed by the features of vehicles (mobility, selfishness, and routes) and limited communication ability of infrastructures, to efficiently motivate vehicles to join in the content dissemination process and appropriately select the relay vehicles to satisfy different transmission requirements is a challenging task. This paper develops a novel edge-computing-based content dissemination framework to address the issue, composed of two phases. In the first phase, the contents are uploaded to an edge computing device (ECD), which is an edge caching and communication infrastructure deployed by the content provider. By jointly considering the selfishness and the transmission capability of vehicles, a two-stage relay selection algorithm is designed to help the ECD selectively deliver the content through vehicle-to-infrastructure (V2I) communications to satisfy its requirements. In the second phase, the vehicles selected by the ECD relay the content to the vehicles that are interested in the content during the trip to destinations via vehicle-to-vehicle (V2V) communications, where the efficiency of content delivery is analyzed according to the probability that vehicles encounter on the path. Using extensive simulations, we show that our framework disseminates contents to vehicles more efficiently and brings more payoffs to the content provider than the conventional methods.

The heterogeneous vehicular networks (HetVNETs), which apply the heterogeneous access technologies (e.g., cellular networks and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and practical paradigm to enable vehicular service applications on the road. However, with different costs in terms of latency time and price, how to optimize the connection along the vehicle's trip toward the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access networks, we first model the cost of each vehicle to download the requested content by jointly considering the vehicle's requirements of the requested content and the features of the available access networks, including conventional vehicle to vehicle communication and the heterogeneous access technologies. A coalition formation game is then introduced to formulate the cooperation among vehicles based on their different interests (contents cached in vehicles) and requests (contents to be downloaded). After forming the coalitions, vehicles in the same coalition can download their requested contents cooperatively by selecting the optimal access network to achieve the minimum costs. The simulation results demonstrate that the proposed game approach can lead to the optimal strategy for the vehicle.

This paper investigates a buffer-aided cooperative NOMA system, where a source is connected to multiple destinations through a buffer-aided NOMA relaying. We aim to maximize a long-term average network utility by jointly optimizing working mode selection, admission control, and power allocation in each time slot, subject to queue stability constraints and both the peak and average power constraints at the relay. To solve this problem, by leveraging the recently-developed Lyapunov optimization framework, we design an Online Adaptive Transmission (OAT) strategy, which converts the original long-term optimization problem into a series of online admission control and power allocation subproblems in each time slot, without loss of optimality. By considering the fact that the power allocation problem is non-convex in nature and thus cannot be solved with a standard method, we transform it into an equivalent convex optimization based on the specific feature of the formulation, and then propose a Fast Decision Criteria (FDC) based algorithm to derive the optimal solution with high computational efficiency. Extensive simulation results are provided to evaluate the performance of the proposed OAT strategy with FDC based power allocation for the buffer-aided cooperative NOMA system, and demonstrate that the proposed strategy can significantly outperform multiple benchmark schemes.

In this paper, the scheduling management of delay-constrained medical packet transmissions in Internet of Things (IoT)-based healthcare networks is studied. Unlike most existing works in the literature, we focus on beyond wireless body area network (beyond-WBAN) communications, i.e., data transmissions between smart WBAN-gateways (e.g., smartphones) and the base station (BS) of remote medical centers. In our model, various medical packets are randomly aggregated at each gateway (which ordinarily stands for one patient), and their delay-constrained beyond-WBAN transmission requests are immediately reported to the network controller (i.e., BS) with different priority levels reflecting their medical importance. The BS schedules the uplink beyond-WBAN transmissions by forming a queueing system which addresses specific medical-grade quality of service requirements, including the priority awareness and the delay constraints of medical packet transmissions. By taking into account the natural device intelligence of smart gateways in IoT-based networks, we design a truthful and efficient mechanism which can prevent gateways from strategically misreporting the priority levels of medical packets, while incentivizing the BS to manage the transmission scheduling according to the desired manner. Both theoretical and simulation results examine the feasibility of the proposed mechanism and demonstrate its superiority over the counterparts.

In this paper, the management of medical packet transmissions in electronic health (e-health) networks is studied. Unlike most existing works, we focus on beyond wireless body area network (beyond-WBANs) communications, i.e., data transmissions between WBAN-gateways (e.g., smart phones) and the base station of remote medical centers, and consider a delay-dependent prioritized transmission scheduling which jointly takes into account both the criticality of medical packets and their starving time (i.e., experienced delays). In our model, medical packets are randomly aggregated at WBAN-gateways, and their transmission requests are reported to the base station with different priority class information. The base station manages the beyond-WBAN transmissions following a constructed queueing system with a delay-dependent priority discipline. For maximizing the network social welfare while preventing unexpected strategic behaviors from smart gateways, we design a truthful and efficient mechanism, called DPMT. Theoretical and simulation results examine the feasibility of the proposed mechanism and demonstrate its superiority over counterparts.

In this paper, an online incentive mechanism in crowdsensing systems is studied. Different from most of the existing works which considered the smartphone users arriving at the crowdsourcer in an online fashion, we concentrate on the uncertain task arrivals, and consider the smartphone user allocation problem by jointly taking the cost capacity of each smartphone user and the sensing data quality requirement into consideration. In our model, since the tasks arrive at the crowdsourcer in an online manner, the crowdsourcer must make decisions timely to choose a suitable subset of smartphone users to achieve a sound competitive ratio (compared to the offline solution) once the tasks arrive. For the purpose of minimizing social cost in the whole system and achieving truthfulness, an online strategy proof incentive mechanism is designed by applying randomized auction framework. Theoretical and simulation results verify the effectiveness of the proposed online incentive mechanism.

In this paper, the management of delay-sensitive medical packet transmissions in beyond wireless body area networks (beyond-WBANs) is studied. The considered system addresses the random arrival of sensed medical packets at each WBAN-gateway, which are categorized into different classes (one class of emergent alarms and multiple classes of non-emergent routines). Upon receiving a medical packet, the associated gateway immediately declares a beyond-WBAN transmission request to the base station (BS). With the consideration of medical-grade quality of service (mQoS) requirements, the beyond-WBAN transmissions of heterogeneous packets are scheduled by following the constructed queueing models with specifically designed priority disciplines. By further considering the potential strategic behaviors of smart gateways, a non-cooperative delay-dependent prioritized queueing game for the beyond-WBAN transmission management is formulated. After that, we propose a novel analytical framework to jointly characterize the queueing performance and the properties of the game equilibrium. Theoretical and simulation results justify the feasibility and applicability of our designed transmission management system in beyond-WBANs.

In this paper, a downlink cellular traffic offloading framework with social-aware device-to-device (D2D) content sharing and proactive caching is studied. In the considered system model, each user equipment (UE) is intelligent to determine which content(s) to request/cache and to share according to its own preference. As the central controller, the base station (BS) can establish cellular transmissions and/or incentivize D2D communications for content dissemination (including proactive caching). By taking into account wireless features, social characteristics, and device intelligence, we formulate a welfare maximization problem integrating power control, channel allocation, link scheduling, and reward design. To solve this complicated problem, we propose a novel mechanism which consists of a newly developed optimization approach, called basis transformation method, for the joint resource management, and a specially devised pricing scheme for the reward determination. Theoretical and simulation results examine the desired properties of our proposed mechanism, and demonstrate its superiority in improving social welfare, network capacity, and utility of the BS.

In this paper, the priority-aware interference-avoidance scheduling for multi-user coexisting wireless networks with heterogeneous traffic demands is addressed. Both admission control and throughput maximization for admitted users are studied. These problems are addressed by a proposed sequential solution framework where at each step a large-scale linear program with a large number of variables is required to be solved. To efficiently solve the large-scale program, an accelerated column generation based method is proposed. In the proposed method, an efficient greedy initialization algorithm is first put forward by exploiting the proposed solution structure. After that, both upper and lower bounds on the optimal objective function of each optimization problem are derived, which are used to significantly alleviate the dependence of the whole solution procedure on deriving optimality of problems. Simulation results show that the proposed algorithm can effectively and efficiently handle the coexistence of multiple users with heterogeneous priorities and traffic demands.

Multiple-input, multiple-output non-orthogonal multiple access (MIMO-NOMA) approach has been considered as a promising multiple access technology for the fifth generation (5G) networks to improve the system capacity and the spectral efficiency. In this paper, we propose a linear optimization approach to jointly optimize beamforming vectors and power allocation coefficients for a MIMO- NOMA cluster, and then minimize the total power consumption through mobile user (MU) clustering. The proposed approach avoids the peer effect during MU clustering and can obtain a closed-form expression of the cluster beamforming matrix. Then, we employ our approach in two different MIMO-NOMA scenarios termed as MIMO- NOMA1 and MIMO-NOMA2. Based on the analytical results, we propose a ranking scheme to obtain the clustering result for the large-scale MIMO-NOMA networks. Simulation results show that MIMO-NOMA1 is better than MIMO-NOMA2, and the proposed approach is superior in finding power efficient MIMO-NOMA clusters than counterparts.

In this paper, we investigate energy efficient packet transmission strategies for wireless body area networks (WBANs) with rechargeable sensors. For practical implementations, we propose a multi-threshold based transmission strategy by taking into account the channel state, battery state and number of buffered packets in the system. A discrete Markov arrival process (DMAP) is introduced to jointly model channel correlations and energy allocations. After that, with given thresholds and corresponding energy allocations, a level dependent Quasi-Birth- and-Death Markov chain is constructed to evaluate the system performance. According to the derived performance metrics, we formulate an optimization problem to find optimal thresholds for energy efficiency maximization with reasonable performance provisioning. Extensive simulations are conducted to verify our proposed queueing analytical model and demonstrate perfor- mance gains of our proposed strategy.

In this paper, the design of priority-aware truthful mechanisms for multi-class delay-sensitive medical packet transmissions in electronic health (e-health) networks is studied. Unlike most of existing works, we focus on beyond wireless body area network (beyond-WBAN) communications, and consider the absolutely prioritized transmission scheduling as the realization of medical-grade quality of service, i.e., more critical medical packets have to always be transmitted prior to the ones with less emergency. In our model, medical packets arrive randomly at each WBAN-gateway (which ordinarily stands for one patient), and their beyond-WBAN transmission requests are reported to the network regulator (i.e., the base station) with different packet priorities which reflect their medical importance. The base station then dynamically manages the beyond-WBAN transmission service by formulating a multi-class multi-server priority queueing system. Taking into account the potential strategic behaviors from smart gateways, we design a truthful mechanism which can guarantee that all gateways will honestly report the actual priorities of their medical packets, while at the same time incentivize the base station to provide channel usages for e-health services. Theoretical analyses and simulation results examine the desired properties of our proposed mechanism, and demonstrate its feasibility and superiority compared to counterparts.

In this paper, we study dynamic load-balancing spectrum decision for a cognitive radio network (CRN) that dynamically distributes packets from the secondary user (SU) to different available primary channels. We consider two different classes of services at the SU, i.e., delay sensitive (DS) and best effort (BE) services, and assign a higher priority to the DS services. We apply priority queuing model to address this priority issue in the CRN. Based on the queuing model, two Markov decision processes (MDPs) are formulated with objectives to minimize the average delay of both services while guaranteeing the priority of the DS services. Reinforcement learning is applied to find the optimal solutions when the traffic and channel characteristics are unknown. To address the computational complexity issue in the MDP solutions, we propose a myopic method based on the estimated packet sojourn time, which is derived by formulating a phase type distribution. Simulation results demonstrate the effectiveness of all proposed algorithms for load-balancing spectrum decision. It also shows that the proposed myopic scheme can achieve significant reduction on computational complexity with a cost on the delay performance of low priority BE services.

In this paper, we propose a spectrum auction mechanism for secondary spectrum access in cognitive radio networks. Different from existing works in the literature, the time-dependent buyer valuation information is employed in the proposed mechanism so that the primary spectrum owner (PO) can determine more favorable spectrum allocations and pricing functions in order to maximize the expected auction revenue. In addition, to exploit the temporal spectrum reusability, the proposed mechanism allows each secondary wireless user to declare its specific time preferences, including service starting time, delay tolerance, and service length. By further considering the heterogeneities in secondary wireless service provisioning, the proposed mechanism is able to support heterogeneous forms (continuous or disjointed spectrum usages) of secondary spectrum requests. Specifically, at the beginning of the auction frame, secondary wireless users report their different spectrum usage requests along with the bidding prices, while the PO decides a single-step spectrum allocation and calculates the payment for each winner based on not only the received bids but also the known time-dependent valuation information. Theoretical analyses and simulation results show that the proposed auction mechanism can satisfy all desired economic properties, and can improve the spectrum allocation efficiency and auction revenue compared with counterparts.

In this paper, the incentive mechanism design issue for device-to-device (D2D) content-sharing communications is discussed. In literature, most of works are based on auction/game theory, where all content owners first report their ask prices/costs towards the base station (BS) which finally decides only one winner from them to transmit data towards the content requester. One disadvantage of these works is that content owners may be frequently activated to provide auction/game information (such as prices/costs), leading to high energy consumption, but finally may not win to gain benefit. To address this, we propose a sequential posted price mechanism where the BS sends offers with posted prices to content owners in sequence and activates only one owner each time. The BS stops sending new offers as long as there is already an owner accepting an offer or when the BS finds the expected cost of sending a new offer is larger than the cost of direct transmission. The optimal posted prices and offer- stopping rule of the BS are derived by the backward principle of dynamic programming. Simulation results show that the proposed mechanism can effectively limit the proportion of content owners being activated while the BS maintains an acceptable expected cost.

In this paper, the design of a truthful mechanism for prioritized medical packet transmissions in e-health networks is studied. Unlike most of existing works, we focus on beyond wireless body area networks (beyond-WBANs), and consider the absolutely prioritized scheduling, i.e., emergent medical signals have to always be transmitted prior to normal ones. In our model, medical packets arrive randomly at each WBAN-gateway, and their transmission requests are reported to the base station (BS) with different packet priorities. The BS then dynamically manages the beyond-WBAN transmission service by following the proposed mechanism constructed on a multi-class multi-server queueing system. Theoretical and simulation results demonstrate that the proposed mechanism can guarantee all gateways to truthfully report their packet priorities, and can incentivize the BS to provide exclusive channel usages for e-health services.

In this paper, an incentive-compatible mechanism for transmission scheduling in electronic health (e-health) networks with delay-sensitive medical packets is studied. Unlike existing works in the literature, we focus on the beyond wireless body area network (beyond-WBAN) communications. In the considered system, medical packets arrive randomly at each gateway (which ordinarily stands for one patient), and their transmission requests are reported to the network regulator (i.e., the base station) with specific delay sensitivities that reflect their medical signal severities. The base station then determines the order of transmission by formulating a priority queue. With the construction of the packets' utility and the base station's profit functions, we analyze the characteristics of the service system and design an incentive-compatible mechanism such that all gateways will be forced to report the actual delay sensitivities of their medical packets. Theoretical analyses show that our proposed mechanism can maximize the profit of the base station (i.e., minimize the total waiting cost from all medical packet transmissions) while guaranteeing higher service priorities to more emergent medical packets. Numerical results examine the properties of the proposed mechanism, and demonstrate its feasibility in providing economic incentives for all individuals.

In this paper, the relay selection issue in buffer-aided relay networks is studied. Different from traditional relay networks, buffer occupancy state has great effects on system performance. Keeping buffers always close to full or empty may lead to potential buffer overflow or data unavailability, which will ultimately affect system throughput and transmission delay jitter. In order to avoid this negative situation, we propose a new threshold-based selection strategy which can achieve the transmission capacity balance between input and output links of every relay buffer. Simulation results show that the proposed strategy can effectively prevent buffers from becoming always close to full or empty and, thus, result in great performance improvement compared to the counterpart.

In this paper, we investigate an online spectrum auction problem in cognitive radio networks with uncertain activities of primary users (PUs). In our framework, a primary base station (PBS), acted as the spectrum auctioneer, leases its under-utilized channels to secondary users (SUs) who request and access spectrum on the fly. Different from most of existing works in online spectrum allocation, we focus on a more practical situation that the auctioneer (or the PBS) has no prior knowledge of PUs' activities so that its channel states are not static. In order to balance the auction profits from granted SUs' spectrum requests and the potential penalties caused by incomplete services to PUs, we introduce the idea of virtual spectrum sellers and formulate the problem as an online double spectrum auction. We then propose a novel online admission and pricing mechanism which also considers the reusability of wireless spectrum. Theoretical analyses are provided to prove that our auction algorithm satisfies all desired economic properties in terms of budget-balance, individual rationality and truthfulness. Simulation results show that our proposed auction algorithm can increase the utility of the PBS, enhance spectrum utilization and achieve better satisfaction for SUs compared to counterparts.

Abstract In this paper, channel assignment for spectrum sensing is studied in multi‐channel cognitive radio (CR) networks to maximize the number of channels satisfying sensing performance (called available channels). Beginning with a nonlinear integer programming problem, we derive the upper bound of optimal value through many‐to‐many assignment problem and then propose three algorithms for both centralized and distributed scenarios. In centralized case, a heuristic scheme is proposed based on the signal‐to‐noise ratios (SNRs) over all primary channels (PCs). Then, a greedy scheme is proposed to reduce the reported information from the CRs. In distributed case, a novel scheme with multi‐round operation is designed following the coalitional game theory. In each round, each CR selects some PCs based on SNRs. Then, the CRs selecting the same channel play coalitional game, and thereby, multiple games are played concurrently over multiple channels. Finally, the best coalition for each channel is chosen among the formed coalitions to perform the cooperative spectrum sensing. The simulation results show that the proposed schemes can significantly increase the number of available channels. Copyright © 2013 John Wiley & Sons, Ltd.

Spectrum auction has been considered as an economically incentive way to motivate both primary spectrum owners (POs) and secondary users (SUs) to participate in dynamic spectrum access (DSA). In this paper, we propose a new combinatorial spectrum auction framework for the scenarios that each PO has multiple channels to sell and each SU demands multiple channels. Moreover, the heterogeneity in terms of POs' channel bandwidths and SUs' demands is also considered. The winner determination problem (WDP) in the proposed auction framework can be formulated as a multiple multidimensional knapsack problem (MMKP) and both upper bound and an approximation algorithm with polynomial time are developed. A tailored pricing mechanism is adopted in the payment design to ensure truthfulness and individual rationality. Numerical results show that our proposed auction algorithm can improve the spectrum allocation efficiency compared to counterparts.

The dynamic spectrum access (DSA) among multiple heterogeneous primary spectrum owners (POs) and secondary users (SUs) in recall-based cognitive radio networks is investigated in this paper. In our framework, SUs demand a different amount of spectrum for their transmissions. Each PO provides a portion of radio resources for leasing and also offers its own primary users (PUs) a certain degree of quality of service (QoS). Furthermore, POs are allowed to have different spectrum trading areas and as well as heterogeneous activities between POs' users. We propose a Two-stage resource allocation scheme with combinatorial Auction and Stackelberg Game in spectrum Sharing (TAGS) to deal with the allocation problem in such a complicated system. In the first stage, a spectrum allocation is decided by running a geographically restricted combinatorial auction without the consideration of spectrum recall. In the second stage, a Stackelberg game is formulated for all users to determine their best strategies with respect to the potential spectrum recall. Both theoretical and simulation results prove that TAGS provides a feasible solution for the problem and ensures the desired economic properties for all individuals.

In this paper, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general statistical model that can handle bursty primary user (PU) channel usage. The proposed model describes idle periods with a discrete platoon arrival process (PAP) and describes busy periods with a discrete phase type (PH) distribution. This channel model is referred to as a PAP-PH process. We further introduce a proactive access scheme as the potential application of the proposed channel model and use it to compare the performance of the proposed model, in terms of spectrum utilization and interference probability, with two traditionally encountered channel usage models, i.e., the geometrically distributed idle-busy period model and the phase type distributed idle-busy period model, under both bursty and non-bursty channel scenarios. Numerical results show that with the proposed model, the proactive access scheme can guarantee the interference threshold to the PU and can be used for both bursty and non-bursty spectrum use patterns.

ABSTRACT In this paper, a novel spectrum‐sensing scheme, called adaptive dual‐radio spectrum‐sensing scheme (ADRSS), is proposed for cognitive radio networks. In ADRSS, each secondary user (SU) is equipped with a dual radio. During the data transmission, with the received signal‐to‐noise ratio of primary user (PU) signal, the SU transmitter (SUT) and the SU receiver (SUR) are selected adaptively to sense one channel by one radio while communicating with each other by the other one. The sensing results of the SUR are sent to the SUT through feedback channels (e.g., ACK). After that, with the sensing results from the SUT or the SUR, the SUT can decide whether the channel switching should be carried out. The theoretical analysis and simulation results indicate that the normalized channel efficiency, defined as the expected ratio of time duration without interference to PUs in data transmission to the whole frame length, can be improved while satisfying the interference constraint to PUs. After that, an enhanced ADRSS is designed by integrating ADRSS with cooperative spectrum sensing, and the performance of ADRSS under imperfect feedback channel is also discussed. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT In this paper, the main properties of digital television broadcasting signals based on the Digital Video Broadcasting—Terrestrial (DVB‐T) standard are analyzed, and these properties are utilized to design a new pilot‐aided detector for spectrum sensing in cognitive radio networks. The proposed detector consists of a processing unit and a combination and decision unit. In the processing unit, multiple statistics that correspond to different enhanced pilot components are computed. In the combination and decision unit, three newly proposed combination schemes are adopted to combine these statistics, and then, a final decision on the presence or absence of the DVB‐T signals is made on the basis of the Neyman–Pearson criterion. The proposed pilot‐aided detector exploits both the periodic continual and scattered pilots that are intrinsic in the DVB‐T signals, processes the observed data timely, experiences short sensing duration, and requires no time synchronization information. Furthermore, the proposed pilot‐aided detector is able to distinguish DVB‐T signals from interference. Theoretical analysis and simulation results show that spectrum bands that are not currently occupied by the DVB‐T systems can be detected accurately by using the proposed pilot‐aided detector. Simulation results also demonstrate the significant performance gain of the proposed detector compared with the counterparts.Copyright © 2011 John Wiley & Sons, Ltd.

This paper investigates the performance of a wideband cognitive radio network where each cognitive user can assemble multiple primary channels. Two channel assembling schemes are considered: a constant channel assembling (CCA) and a variable channel assembling (VCA). In the variable channel assembling scheme, cognitive users assemble their channels on the basis of the number of detected residual channels that are unoccupied by primary users or cognitive users. The effects of imperfect spectrum sensing (with false alarms and misdetections) are taken into account and it is assumed that spectrum handover is implemented in the secondary network. These channel assembling schemes are analyzed by using Continuous-time Markov chains (CTMC), and the system performance is evaluated in terms of throughput, blocking probability, and forced termination probability. Numerical results show that channel assembling achieves lower forced termination probability, but does not increase achieved system throughput and leads to higher blocking probability. They also show that VCA outperforms CCA in terms of throughput and forced termination probability.

In this paper, a distributed cooperative attack on multi-channel cooperative spectrum sensing is proposed. In the proposed attack, attackers play two coalitional games to maximize the number of invaded channels in a distributed manner. In the first game, attackers play with their fellow ones to allocate an optimal number of attackers for each channel, while in the second game, they play in the coalitions with other honest cognitive radios so as to conquer as many channels as possible. Simulation results show that the proposed attack can considerably decrease the number of available channels with a low attack cost.

In this paper, channel assignment in cooperative spectrum sensing is studied for multi-channel cognitive radio networks. Based on the information from each secondary user, e.g., primary signal-to-noise ratios over all channels, a centralized scheme is proposed, which assigns channels to different secondary users for sensing so that the number of available channels, which meet the sensing performance requirements in terms of miss detection and false alarm probabilities, can be greatly increased. By further taking the communication overhead into account, a greedy scheme is proposed to reduce the reporting information from the secondary users to the base station. The simulation results demonstrate that both schemes can significantly increase the number of available channels, while the latter also shows advantages in reducing the signaling overhead.

A reliability-based iterative majority-logic decoding algorithm for regular low-density parity-check (LDPC) codes was recently proposed by Huang et al. In this paper we present an improved version of that algorithm by introducing a different reliability measure for each check-sum of the parity-check matrix, and taking it into account in the computation of the extrinsic information that is used to update the reliability measure of each received bit in each iteration. Some simulations results are given, which show that the new algorithm, while requiring very little additional computational complexity, not only achieves a considerable error performance gain over the standard one, but also, importantly, outperforms the iterative decoding based on belief propagation (IDBP), especially for short and medium block length finite-geometry (FG) LDPC codes.

This paper considers a cognitive radio network where the cognitive user has a constant bandwidth which is K times that of a primary user. The primary system consists of constant M primary channels and the cognitive system consists of N = [M/K] channels, where [x] is the largest integer not greater than x. The effects of imperfect spectrum sensing (with false alarms and misdetections) are analyzed using a Markov chain. Explicit expressions for state dependent transition rates are derived for the case M = 6 and K = 2, and the system performance is evaluated in terms of throughput, blocking probability, and forced termination probability.

This paper presents a hybrid decoding algorithm for low-density parity-check (LDPC) codes based on the interior point method with barrier function for linear programming (LP) decoding introduced by Wadayama . First, an efficient implementation of Wadayama's algorithm is presented. The main idea behind the modification is to approximate the barrier function for the fundamental polytope defining the code so that it contains only one linear constraint for each of the parity-check constraints. A two-stage hybrid decoding which combines the interior point decoding and a low-complexity decoding algorithm for LDPC codes is then proposed. Simulation results show that the approximations introduced in the proposed algorithms do not result in any performance degradation, while considerably reducing the decoding complexity and latency.

A new channel model for binary additive noise communication channel with memory, called weighted queue-based channel (WQBC), is introduced. The proposed WQBC generalizes the conventional queue-based channel (QBC) such that each queue cell has a different contribution to the noise process, i.e. the queue cells are selected with different probabilities. Suitably selecting the modeling function, the generalization introduced by the WQBC does not increase the number of modelling parameters required compared to the QBC. The statistical and information-theoretical properties of the new model are derived. The WQBC and the QBC are compared in terms of capacity and the accuracy in modeling a family of hard decision frequency-shift keying demodulated correlated Rayleigh and Rician fading channels. It is observed that the WQBC requires a much smaller Markovian memory than the QBC to achieve the same capacity, and provides a very good approximation of the fading channels as the QBC for a wide range of channel conditions.

This paper presents an improvement of the reliability-based iterative majority-logic decoding algorithms for regular low-density parity-check (LDPC) codes proposed by Huang et al. We improve the computation of the extrinsic information that is used to update the reliability measure of each received bit in each iteration with some kind of reliability measures of the check-sums that are orthogonal on the considered bit. The improved algorithm achieves a significant gain over the standard one with only a modest increase in computational complexity.

In this paper, a distributed cooperative multi-channel spectrum sensing scheme is proposed for the non-infrastructure based cognitive radio networks. The proposed scheme has iterative property and is carried out round-by-round. In each round, each secondary user selects a few primary channels as the candidates for sensing based on primary signal-to-noise ratio. Then, the users with the same selected channel collaboratively form coalitions through coalitional game and thereby multiple games are played concurrently over multiple channels. After generating stable coalitional structure, the best coalition on each channel is chosen to perform the cooperative spectrum sensing. The simulation results show that the proposed scheme can significantly increase the number of available channels, which can be sensed with predefined miss detection and false alarm probabilities.

In this paper, a new MAC protocol, called Reliable Busy Tone Multiple Access with Neighboring Information Table (RBTMA-NIT), is proposed for safety applications in Vehicular ad hoc networks (VANETs). Derived from the conventional busy tone multiple access (BTMA), RBTMA-NIT introduces three mechanisms to reduce the transmission delay of emergency messages: 1) the original emergency message and its copies are assigned two different priorities; 2) a table, called neighboring information table (NIT), is built to reduce the redundant transmissions; and 3) the contention policy is improved by selecting feasible nodes, setting suitable contention window and limiting the transmission time of message on each node. Simulation results demonstrate that the proposed MAC protocol can effectively reduce the covering time during which all nodes in the system have received the emergency message.

In this paper, a hybrid decoding algorithm for finite-geometry low-density parity-check (FG-LDPC) codes is proposed. The algorithm is based on the interior point method with barrier function introduced by Wadayama. First, an efficient implementation of Wadayama's algorithm is presented. The main idea behind the modification is to approximate the barrier function for the fundamental polytope defining the code so that it contains only one linear constraint for each of the parity-check constraints. A two-stage hybrid decoding which combines the interior point decoding (IPD) and a low-complexity decoding algorithm for FG-LDPC codes is then proposed. In the first stage, the interior point decoding is used to generate a search point. If the first stage decoding fails, the decoding is continued by the low-complexity algorithm that is initialized by the result of the IPD. Compared with a conventional iterative message-passing (IMP) decoder, the proposed hybrid algorithm achieves better bit-error rate (BER) and frame-error rate (FER) for small block lengths at medium to high signal-to-noise ratio (SNR).

In this paper, a cognitive radio (CR) network composed of K secondary users who cooperatively sense a channel using the k-out-of-K fusion rule to determine the presence of the primary user is studied. The sensing-throughput tradeoff problem is investigated in a realistic environment where both the sensing channels and reporting channels are characterized by fading channels. It is observed that taking into consideration the probability of reporting error in the CR network increases the sensing time and reduces the maximum average throughput of the secondary users. To mitigate the effect of the probability of reporting error, a transmit diversity based cooperative spectrum sensing method using randomized space-time block coding (RSTBC) is proposed. Simulations results show that the spatial diversity gain induced by RSTBC significantly decreases the sensing time and improves the throughput of the secondary users.

In this paper, a general framework, called probability-based solution (PBS), is proposed to balance the trade-off between the accessibility (in terms of the success probability to find a route reaching the destination) and the performance (in terms of spectral efficiency, outage probability or energy consumption) of distributed routing schemes in multi-hop wireless networks. In the PBS, the candidate nodes (receivers) in each hop are first separated into two groups based on a direction either diverging from, or converging to, the direct line between the source and destination. Then, one of the best nodes from the two groups is selected according to a carefully-defined probability by considering the uncertainty in the subsequent hops. The simulation results demonstrate that integrating the proposed PBS with conventional distributed routing schemes can significantly improve the accessibility and effectively guarantee other system performance. Moreover, the PBS can also be combined with facing routing to solve the connectivity hole problem.

In this paper, an efficient implementation of the interior point algorithm recently proposed by Wadayama for linear programming (LP) decoding of low-density parity-check (LDPC) codes is presented. The main idea behind the modification is to approximate the barrier function for the fundamental polytope defining the code so that it contains only one linear constraint for each of the parity-check constraints. Simulation results show that the approximations introduced do not result in any performance degradation, while considerably reducing the decoding complexity and latency.

In this paper, two novel distributed routing schemes, named adaptive-information-moving-rate routing scheme (AIMR) and adaptive-information-moving-distance-and-link-rate routing scheme (AIMDLR), are proposed for multi-hop wireless networks by jointly considering the number of hops and the link states. With one-hop information only, both schemes aim at improving the network spectral efficiency under two different bandwidth sharing methods (i.e., throughput-maximization bandwidth sharing and equal-time bandwidth sharing), respectively. In addition, a general scheme, called probability-based scheme (PBS), is proposed to improve the accessibility of distributed routing schemes, which is denoted by the success probability of finding a route reaching the destination. In the PBS, the node selection in each hop is based on a well-defined probability, which takes into account the locations of the transmitter and the receiver at each hop and the uncertainty in the subsequent hops. By combining the PBS with the AIMR and the AIMDLR, the proposed probability-based AIMR (PAIMR) and probability-based AIMDLR (PAIMDLR) can not only improve the accessibility significantly but also achieve higher effective spectral efficiency compared to the counterparts. Simulation results are finally presented to demonstrate the advantages of the proposed routing schemes.

In this paper, a greedy sub-channel redistribution routing scheme (GSRRS) is proposed for multi-hop wireless OFDMA networks. In GSRRS, between any two neighboring nodes, different sub-channels are routed over different paths including the direct one-hop path and the two-hop path with one intermediate node. Compared to the traditional routing scheme where all the sub-channels route over the same path, GSRRS can improve not only the link rate between two neighboring nodes but also the capacity of the shortest path derived by the Dijkstra's algorithm between any source-destination pair.

In this paper, designing channel sensing policies for cognitive radio networks is discussed. A discrete-time semi- Markov channel model is first introduced, which facilitates the analysis on more general channel occupancy behaviors and possible asynchronism among primary and secondary users. Based on the characteristics of the channels at the stationary state, multiple channel sensing policies have been proposed for different network scenarios with homogeneous channels, heterogeneous channels, and sequential channel sensing. Both analytical and simulation results are given to demonstrate the effectiveness of the proposed channel sensing policies.

A mobile ad-hoc network (MANET) is usually power constrained due to the limited battery energy on each node. For MANETS, energy efficiency is crucial for the design of new routing protocols. In this paper, a new energy-aware routing algorithm, called Energy Saving Ad-hoc On-demand Distance Vector (ESAODV) routing, is proposed. In the route discovery process of ESAODV, intermediate nodes estimate the current average energy of the network (CAEN) as a comparison threshold to determine how to respond to the received route request (RREQ) packets. After that, the effects of ESAODV on network performance are addressed. Analytical and simulation results show that the proposed ESAODV can effectively protect the energy-overused nodes and can greatly prolong the network lifetime.

In this paper, a new four-antenna based structure (FAS) is introduced for capacity improvement in future cellular networks with frequency reuse factor of 1. In FAS, each cell consists of one omni-directional antenna located at the cell center and three 120-degree directional antennas located at the cell edge. In order to achieve effective system bandwidth allocation among four antennas, novel spectrum planning schemes for both downlink (DL) and uplink (UL) are proposed. Theoretical analysis and simulation demonstrate that the proposed cellular structure can significantly improve the system capacity in both DL and UL, especially at the cell edge.

In this paper, two distributed routing schemes, named adaptive information moving rate (AIMR) and adaptive information moving distance and link rate (AIMDLR), are proposed for multi-hop fixed relay networks. Both routing schemes aim to maximizing spectral efficiency in terms of throughput-oriented spectral efficiency (TOSE) and fairness-oriented spectral efficiency (FOSE), respectively, while remaining distributed properties without involving network-wide information. Moreover, the route convergence issue which is inherent to some distributed routing schemes is also discussed by proposing a new routing convergency (RC) scheme. Simulation results demonstrate that the proposed routing schemes can significantly improve the spectral efficiency with high convergency probability.

In this paper, performance of wireless opportunistic schedulers in multiuser systems is studied under a dynamic data arrival setting. Different from the previous studies which mostly focus on the network stability and the worst case scenarios, we emphasize on the average performance of wireless opportunistic schedulers. We first develop a framework based on Markov queueing model and then analyze it by applying decomposition and iteration techniques in the stochastic Petri nets (SPN). Since the size of the state space in our analytical model is small, the proposed framework shows an improved efficiency in computational complexity. Based on the established analytical model, performance of both opportunistic and non-opportunistic schedulers are studied and compared in terms of average queue length, mean throughput, average delay and dropping probability. Analytical results demonstrate that the multiuser diversity effect as observed in the infinite backlog scenario is only valid in the heavy traffic regime. The performance of the opportunistic schedulers in the light traffic regime is worse than that of the non-opportunistic round-robin scheduler, and becomes worse especially with the increase of the number of users. Simulations are also performed to verify the accuracy of the analytical results.

In this paper, a finite-state phase-type semi-Markov channel (FSPHMC) model is proposed for wireless channels with correlated fading. By introducing a phase type distributed sojourn time between channel state transitions, the proposed model generalizes the traditional Finite-State Markov Channel (FSMC) model where the sojourn time in each channel state is assumed to satisfy geometric distribution. Thanks to the flexibility of the phase type distribution, the resultant FSPHMC model is applicable to much wider range of practical fading channels. For facilitating the implementation of FSPHMC model in practical scenarios, a special case, FSPHMC model with negative binomial sojourn time (FSPHMC-NB), is also presented under constraint computational complexity. Compared with the traditional FSMC model, the FSPHMC-NB demonstrates considerable improvement in that it matches the true state duration distribution without a significant increase in complexity. Simulation results are given to validate the flexibility and versatility of the proposed FSPHMC model.

In this paper, the throughput performance of the access links (i.e., base station to mobile station and relay station to mobile station) is analyzed for the wireless relay networks based on IEEE 802.16j. An isolation band based frequency reuse scheme (IBFRS) is proposed, which introduces an isolatio

The impact of user relaying on the behavior of a relay node, which acts as the source node at the same time, is analyzed in a wireless relay network at the packet level. The analysis process models the behavior of the relay node as a queueing system and represents the service for its own packet transmission as an M/G/1-type Markov chain. By considering the fact that the maximum number of packet arrivals is ordinarily limited in a practical system, the M/G/1-type Markov chain is further reformatted into a quasi-birth-death (QBD) process through re-blocking so as to simplify the analysis and obtain the associated performance, such as average packet transmission delay. As an application of the results arising from the analysis, a new relay node selection scheme, based on a utility function approach that jointly considers the channel and the queue conditions at the relay node, is proposed. Numerical results show that the proposed analysis model is quite accurate and the proposed relay node selection scheme is effective in balancing cooperative diversity gain and packet transmission delay.

In this paper, designing an effective user relaying algorithm, in terms of relay node selection and power allocation, is discussed for amplify-and-forward wireless relay networks. The objective is to simplify the application of user relaying in practical wireless communication networks so that the system capacity can be improved with low computational complexity and system overhead. Beginning with the derivation of a tight threshold-based sufficient condition on the feasibility of a relay node, i.e., ensuring that user relaying via the node can achieve a larger channel capacity than direct transmission, a semi-distributed user relaying algorithm is proposed. In the proposed algorithm, each relay node can make decision on its feasibility individually, and the ultimate decision on the relay node selection among multiple feasible ones is made in a centralized manner. Since there is no need on exchanging channel state information among different network nodes, the proposed algorithm is simple for implementation and suitable for practical applications, which have stringent constraints on system overhead. By comparing with the centralized user relaying algorithm, which requires global channel state information of the whole network, the proposed semi-distributed algorithm can provide comparable system capacity, but has significantly reduced computational complexity.

In this paper, the flow-level performance of opportunistic scheduling in orthogonal frequency division multiplexing (OFDM) networks is studied. The analysis accounts for the applications with a dynamic number of competing flows, such as continuous transfers of file transport protocol (FTP) or web browsing sessions. An analytical model is developed to extend the multi-class processor-sharing model in single-carrier networks to multi-carrier OFDM networks, where the total service rate varies with the number of flows. Based on the analytical model, the scheduling gains in both OFDM-TDMA (time division multiple access) and OFDMA (orthogonal frequency division multiple access) networks are evaluated for low and moderate signal-to-noise ratio (SNR). Different from previous works, we focus on the scheduling performance at the flow level and consider a dynamic network setting with random sized service demands. Furthermore, we use stochastic comparison techniques to examine the effects of physical-layer characteristics, such as fading speed and channel frequency selectivity, on flow-level performance. Simulations are performed to verify the analytical results.

Bandwidth allocation, in terms of distributing available data slots among different users, is studied for QoS provisioning in IEEE 802.16 networks. By considering the Automatic Repeat reQuest with Selective Acknowledgement (ARQ-SA) scheme for erroneous wireless channels, a mathematical model is established to theoretically analyze the delay performance of transmitting Service Data Unit (SDU) under a multiuser environment. The analytical results indicate that the delivery delay of the SDU is dominated by the time spent for the first transmission of all its Protocol Data Units (PDUs). Based on this observation, a novel dynamic bandwidth allocation algorithm is proposed and a detailed performance analysis is provided. Simulation results show that the proposed bandwidth allocation algorithm can significantly improve the delay performance of SDUs and ensure the fairness among different users.

In this paper, real-time video transmission with quality of service (QoS) assurance over orthogonal frequency division multiplexing (OFDM) wireless systems is studied. Three quasi-optimal subcarrier allocation schemes, namely regular, delay tolerant and adaptive, are proposed. In the proposed schemes, effective throughput per subcarrier employing adaptive forward error correction (FEC) coding in a Rayleigh fading channel is evaluated, and a capacity matrix that governs all active users and all assignable subcarriers is formulated. The Munkres algorithm is used in the schemes to achieve optimal subcarrier assignment. Performance analyses in terms of spectral efficiency and packet loss probability are presented. Multi-user multichannel diversity is investigated to exploit the innate capacity gain in terms of fading variation and delay tolerance. The proposed schemes are scalable to both single-cell and multi-cell circumstances. Numerical results demonstrate that the proposed schemes can effectively achieve quasi-optimal spectral utilization and significantly improve system throughput.

Reuse partitioning (RP), a promising technique for constructing high efficient multi-cell OFDM systems, has brought a lot of attentions from both industry and academia. In this paper, by considering the characteristics of various service flows, we first introduce a new performance metric, called flow-satisfaction-degree (FSD), and then formulate an optimization problem for maximizing the FSD over the whole system. We then propose a FSD-based scheduling (FSDS) algorithm for the static RP system. Simulation results demonstrate that the proposed algorithm greatly outperforms the traditional static allocation scheme in terms of FSD and system throughput for the fully loaded system.

The paper proposes a distributed connection admission control and resource allocation scheme for IEEE 802.16 networks. Rather than investigating the short-term variation of wireless channel conditions for different sub-carriers, the study focuses on relative long-term capacity planning at the base station on a time-of-a-day basis for each subscriber station and mobile user. With the proposed scheme, each subscriber station performs distributed admission control to decide whether or not to accept connection requests originated by its end users based on the granted capacity aiming at optimizing the overall revenue. A cross-layer design and optimization cooperated with a suite of distributed signaling are addressed and implemented such that the network capacity can be assigned efficiently. Simulation results are given to demonstrate the efficiency of the proposed scheme.

In this paper, transmitting the real-time video with QoS provisioning over orthogonal frequency division multiplexing (OFDM) wireless channels is studied. Efficient subcarrier allocation schemes, namely regular, delay tolerant and adaptive, are proposed for real-time video streaming with QoS satisfaction to achieve near-maximal frequency efficiency. The Munkres algorithm is properly applied to the schemes to assure the optimality of subcarrier assignment. Multiuser multichannel diversity is investigated to exploit the innate multiplexing gain from fading variation and delay tolerance.

In this paper, the capacity of an orthogonal variable spreading factor code division multiple access (OVSF-CDMA) system supporting convergent variable rate video and data services is studied. An analytical approach for evaluating the system outage probability based on interference analysis is introduced. An optimization model to achieve maximum video and data throughput with QoS assurance and satisfactory outage probability is proposed. Numerical results demonstrate the effectiveness of the proposed approaches.

In this paper, adaptive transmission of scalable multi-layered video with quality of service (QoS) assurance over wireless channels is studied. By properly formulating the channel fading as a finite state Markov channel (FSMC) model, three rate adaptation schemes, namely, assured-rate-allocation, neighbor- interleaving, and swing-loaded schemes, are proposed to exploit the inherent multiplexing gain. An analytical model for QoS performance evaluation of video transmission over time-varying erroneous channels is derived. The accuracy of the analytical model is validated by simulations. Analytical and simulation results demonstrate that the proposed rate adaptation schemes can effectively improve the channel utilization and the system throughput.

Abstract In this paper, a self‐organizing map (SOM) scheme for mobile location estimation in a direct‐sequence code division multiple access (DS‐CDMA) system is proposed. As a feedforward neural network with unsupervised or supervised and competitive learning algorithm, the proposed scheme generates a number of virtual neurons over the area covered by the corresponding base stations (BSs) and performs non‐linear mapping between the measured pilot signal strengths from nearby BSs and the user's location. After the training is finished, the location estimation procedure searches for the virtual sensor which has the minimum distance in the signal space with the estimated mobile user. Analytical results on accuracy and measurement reliability show that the proposed scheme has the advantages of robustness and scalability, and is easy for training and implementation. In addition, the scheme exhibits superior performance in the non‐line‐of‐sight (NLOS) situation. Numerical results under various terrestrial environments are presented to demonstrate the feasibility of the proposed SOM scheme. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, a resource allocation scheme is proposed for wireless relay networks to achieve maximum system throughput in terms of achievable average mutual information with low computational complexity. A relay network, which has single source node, multiple relay nodes and single destination node, is analyzed from the information theory point of view. Both sufficient and necessary conditions are derived for the best relay node selection, and a close-form of optimal power allocation between the source and the relay nodes is obtained. Application of the proposed resource allocation schemes in practical relay networks with incomplete channel state information is also studied. Simulation results are presented to demonstrate the effectiveness of the proposed resource allocation schemes in wireless relay networks.

In this paper, efficient rate adaptation schemes for scalable multi-layered video transmission over wireless links with quality of service (QoS) assurance are proposed. An analytical model for QoS performance evaluation on video transmission over time-varying erroneous channels is derived. The accuracy of the analytical model is validated by simulations. Analytical and simulation results show that the proposed rate adaptation schemes can effectively improve the channel utilization and the system throughput.

In this paper, the utilization of real-time video service in the downlink of an orthogonal variable spreading factor code division multiple access (OVSF-CDMA) system is studied. By modeling the video traffic and wireless channel as a joint Markov modulated process, and properly partitioning the states of the Markov process, an adaptive rate allocation scheme is proposed for real-time video transmission with quality of service provisioning while achieving high channel utilization. The scheme is applicable for packet switching and frame-by-frame real-time video transmission, and incorporates both the physical layer and network layer characteristics. For QoS provisions, the closed form expressions of packet delay and loss probability are derived based on the Markov model. Analytical and simulation results demonstrate that the proposed scheme can significantly improve the channel utilization over the commonly used effective bandwidth approach.

This paper proposes a practical joint power allocation and scheduling scheme for medium access control (MAC) layer design in ultra-wideband (UWB) networks. The scheme increases system spectral efficiency and reduces power consumption by fully considering the properties of the UWB, such as its capacity in supporting parallel transmission and providing accurate positioning, and its stringent constraint on computational complexity. Practical implementations of the proposed scheme are achieved by introducing a margin based power allocation scheme and an exclusive region based scheduling scheme. The margin based power allocation scheme is carried out based on each link's own information, and is simple to implement, while the exclusive region based scheduling scheme takes into account the interaction among different links. Simulation results show that the proposed power allocation and scheduling schemes exhibit good performance in terms of the average number of slots per frame and the power consumption reduction.

The paper studies downlink channel utilization for real-time video traffic transmission in an orthogonal variable spreading factor code division multiple access (OVSF-CDMA) system. By modeling video traffic as a Markov modulated process and properly partitioning the states of the Markov process, an adaptive rate allocation scheme is proposed for real-time video transmission with quality of service (QoS) satisfaction while achieving high channel utilization. The scheme is applicable for packet switching and frame-by-frame real-time video transmission, and incorporates both physical layer and network layer characteristics. The QoS requirements include stringent packet delay and packet loss rate. Analytical and simulation results demonstrate that the proposed scheme can significantly improve the channel utilization over the commonly used effective bandwidth approach.

In this paper, an optimal downlink resource management scheme for heterogeneous packet transmission in orthogonal frequency-division multiplexing (OFDM) wireless communication systems is proposed. By making use of the channel impulse response and the properties of the OFDM physical layer, a resource management scheme is developed by integrating power distribution, subcarrier allocation, and the generalized processor sharing (GPS) scheduling. The scheme can: 1) maximize system throughput; 2) guarantee the required signal-to-noise ratio for heterogeneous traffic; 3) provide fairness to all the traffic admitted in the system; and 4) satisfy the total transmission power constraint. For practical implementation, a simplified power and subcarrier allocation algorithm, a robust H/sub /spl infin// channel estimation algorithm, and a truncated GPS (TGPS) scheduling scheme are introduced. Simulation results show that the proposed resource management scheme exhibits good throughput performance.

A self-organizing map (SOM) scheme for mobile location estimation in a direct-sequence code division multiple access (DS-CDMA) system is proposed. The scheme performs nonlinear mapping between the measured pilot signal strengths from nearby base stations and the user's location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the non-line-of-sight (NLOS) situation. Numerical results under various terrestrial environments are presented to demonstrate the feasibility of the proposed SOM scheme.

In this paper, a downlink resource management scheme with adaptive modulation and dynamic generalized processor sharing (DGPS) scheduling is proposed for orthogonal frequency-division multiplexing (OFDM) wireless communication systems. By making use of the frequency diversity among the subcarriers allocated to the same user and the inherent delay tolerance of the nonreal-time traffic, the proposed scheme can achieve maximum system throughput while guaranteeing the required symbol-error-rate (SER), providing fairness to all multimedia traffic, and satisfying the total transmission power constraint. Simulation results show that the proposed resource management scheme exhibits desirable performance.

In this paper, the joint time-frequency domain channel estimation problem in orthogonal frequency-division multiplexing (OFDM) wireless communication systems is transformed to a set of independent time-domain estimation problems. A robust channel estimation algorithm based on the H/sub /spl infin// filtering approach is proposed to estimate the channel fading in the time domain. The estimation criterion is to minimize the worst possible amplification of the estimation errors in terms of the exogenous input disturbances such as multiplicative and additive noise. The criterion is different from the traditional minimum estimation error variance criterion for the Kalman estimation algorithm, and requires no a priori knowledge of the disturbance statistics. It is shown that the proposed channel estimation algorithm is more robust compared with the Kalman estimation counterpart in terms of model uncertainty, and is more suitable to practical OFDM wireless communication systems.

In this paper, downlink resource management scheme is proposed for heterogeneous packet transmission in orthogonal frequency-division multiplexing (OFDM) wireless communication systems. By making use of the properties in the physical layer of the OFDM system, the resource management scheme is developed by integrating power allocation, subcarrier allocation, and generalized processor sharing (GPS) scheduling. With the scheme, maximum system throughput can be achieved, while guaranteeing the required signal-to-noise ratio (SNR) for heterogeneous traffic, providing fairness to all the traffic in the system, and satisfying the total transmission power constraint. For practical implementation, a truncated GPS (TGPS) scheduling and a simplified power and subcarrier allocation algorithm are introduced. Simulation results are given to demonstrate the performance of the proposed resource management scheme.

In this paper, an expectation and maximization (EM) channel estimation algorithm is proposed for orthogonal frequency-division multiplexing (OFDM) wireless communication systems. The algorithm provides minimum mean square error channel fading estimates without requiring a priori knowledge of the channel fading statistics. Simulation results show that the proposed EM channel estimation algorithm without information on channel fading statistics can provide nearly the same performance as the Kalman estimation algorithm with perfect information on channel fading statistics, and is more suitable for practical OFDM wireless communication systems.

A novel intercarrier interference (ICI) cancellation scheme for orthogonal frequency-division multiplexing (OFDM) wireless communication systems is proposed. By equivalently representing the OFDM system with a synchronous code-division multiple access (SCDMA) model, the schemes for the multiple access interference (MAI) cancellation in the SCDMA model can be directly applied to the ICI cancellation in the OFDM system. In addition, the signal detection delay can be further improved by making use of the correlation between the neighboring subcarriers. Simulation results to demonstrate the effectiveness of the proposed ICI cancellation scheme are presented.

A novel channel estimation algorithm for a fast fading channel in multicarrier code-division multiple-access (MC-CDMA) systems is proposed. The estimation algorithm considers the interchannel interference (ICI). With the channel estimation, the system performance in terms of the signal-to-interference plus noise ratio (SIR) is analyzed. Simulation results are given to demonstrate the effectiveness of the proposed estimation algorithm.