Real-world multivariate time series often exhibit multi-scale temporal dynamics and intricate inter-variable dependencies, making long-term forecasting particularly challenging. In this work, we propose a global token-driven multi-scale forecasting framework with dual-attention fusion. To capture multi-scale periodic patterns, the input sequence is segmented into multi-scale patches based on candidate periods, with the dominant ones derived via fast Fourier transform. Global tokens are then introduced as shared representations to integrate information from the temporal and variable dimensions, and a multi-scale patch-token interaction module is designed to establish interactions between the patches and global tokens, enabling the capture and aggregation of temporal dependencies across different scales. A dual-attention fusion module, employing both self-attention and cross-attention mechanisms, is then proposed to capture intrinsic and context-aware variable correlation among variables. To integrate cross-variable and cross-scale information into patch representations, a global information fusion module is designed. Finally, a period-aware weighting approach is devised to adaptively fuse the multi-scale predictions. Comprehensive experiments demonstrate that the proposed framework achieves state-of-the-art performance across various real-world datasets.

Heterogeneous satellite communication systems consisting of geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellites have attracted significant attention due to their complementary advantages in wide coverage and low-latency transmission. In this paper, we investigate the joint data scheduling and precoding problem in multi-antenna GEO-LEO heterogeneous satellite systems. Jointly considering the dynamic network topology, time-varying channel conditions, stochastic traffic arrivals, and queue dynamics, we model the problem as a long-term average cache queue length minimization problem under quality of service (QoS) constraints. To tackle the formulated mixed-integer non-convex optimization problem, we decompose it into two subproblems, i.e., data scheduling subproblem and precoding subproblem, and design a nested iterative solution framework. To address the data scheduling subproblem, we formulate it as a Markov decision process (MDP) and put forward a proximal policy optimization (PPO)-based data scheduling algorithm. Given the state and action of the MDP, the joint optimization problem is reduced to a precoding subproblem, which can then be transformed into a weighted mean square error minimization problem, and is efficiently solved via the Lagrangian dual method. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Deep reinforcement learning has demonstrated outstanding performance in autonomous driving (AD). However, independent single vehicle training struggles to cope with complex traffic environments, while collaborative training across multiple vehicles causes security risk in data sharing. To address these challenges, this paper proposes a dual-layer blockchain-enabled federated reinforcement learning algorithm (DBFRL) for personalized AD. The proposed DBFRL algorithm constructs a FRL architecture based on a dual-layer blockchain to ensure data security during training. Practical driving behaviors data from the HighD dataset are used to classify driving styles into three categories: timid, normal and aggressive. Correspondingly, the personalized multi-objective reward functions are designed to reflect individual driving preferences. Then, the improved TD3 algorithm with different experience replay buffers and prioritized experience replay mechanism are using in the local model training. Furthermore, the reputation values of connected autonomous vehicles are introduced to ensure high-quality global model aggregation. The effectiveness of the DBFRL algorithm is validated on the CARLA simulator. Simulation results confirm that it significantly improves training performance and preserves data safety simultaneously.

To address the problems of low resource utilization and deployment efficiency caused by sudden changes in network states due to large-scale complex network access and the random arrival of service requests during dynamic service function chain (SFC) deployment, a novel SFC deployment method for IoT networks based on large language model (LLM) policy distillation is proposed. First, an SFC deployment framework based on teacher-student agent policy distillation using LLM is constructed. Through the policy distillation mechanism, the MARL-based student agents are guided to efficiently learn the SFC deployment policies generated by the LLM-driven teacher agents. Second, we design a teacher agent composed of three modules: a state perceiver, a task-planning decision-maker, and a result evaluator. The state perceiver predicts node resource availability using an LLM-based spatiotemporal forecasting method. The decision-maker leverages LLM reasoning to generate candidate deployment policies. The evaluator selects policies based on load-balancing metrics. Finally, the student agents, considering constraints such as network resources and latency, build an optimization model aimed at maximizing resource utilization and SFC deployment rewards, and introduce a Teacher-Student Policy Distillation-based Multi-Agent Soft Actor-Critic (TSPD-MASAC) algorithm to solve this optimization problem. Simulation results demonstrate that, in complex network environments with dynamic resource states and diverse service requests, the proposed method achieves more accurate resource state perception, accelerates algorithm convergence, and significantly improves resource utilization and overall deployment performance compared with baseline schemes.

To address the issues of lagging virtual network function (VNF) migration caused by the dynamic changes in resource requirements in Industrial Internet of Things (IIoT) and the unsatisfactory prediction effect due to the neglect of the connection relationships between nodes in resource requirement prediction, a digital twin(DT)-assisted VNF migration algorithm with spatiotemporal large language model-based resource demand prediction is proposed. Firstly, a large language model-based resource prediction method that combines graph convolutional networks and multi-head self-attention mechanism is introduced to effectively capture spatio-temporal correlations and predict future resource requirements. Next, in order to ensure a deterministic quality of service (QoS) and optimize migration decisions, a DT-assisted VNF migration model composed of energy consumption, latency, and load balancing is constructed, and a joint optimization model for VNF migration and DT re-association aimed at maximizing the long-term utility of the system is established. Finally, a migration algorithm based on heterogeneous multi-agent proximal policy optimization is proposed to solve the VNF migration problem according to the resource requirements predicted by the model. To address the problem of excessive synchronization delays of DT nodes originally associated with nodes after migration, a counterfactual multi-agent algorithm is proposed to solve the problem of DT re-association. Simulation results show that the proposed algorithm improves prediction accuracy, ensures load balancing, and reduces system energy consumption and synchronization delays.

To address the issue of inaccurate synchronization between physical devices and their corresponding digital twins (DT) in Industrial Internet of Things (IIoT), which is caused by sensing errors, unreliable wireless transmission, and outdated information, we propose a DT information synchronization strategy for IIoT based on dual time scale network slicing (NS) orchestration. Firstly, a DT-driven IIoT slicing architecture is proposed to provide isolation for heterogeneous Quality of Service (QoS) requirements. On this basis, to quantify synchronization performance, a DT fidelity model is established, incorporating sensing accuracy, data transmission reliability, and the Age of Information (AoI). To fully utilize the network resources and improve the accuracy of DT synchronization information, a dual time-scale model is constructed, where the large time scale handles DT association and inter-slice resource allocation according to the users’ demand, while the small time scale is responsible for intra-slice scheduling of power, bandwidth, and observation frequency. To solve the formulated optimization problem, we design a hierarchical deep reinforcement learning framework that adopts Deep Recurrent Q-Network (DRQN) and Counterfactual Multi-Agent Prioritized Experience Replay Compound-Action Actor-Critic (COMA-PER-CA2C) algorithms. Simulation results demonstrate that the proposed method significantly improves DT fidelity and resource utilization in various IIoT scenarios.

Spectrum cartography (SC) based on tensor completion has been extensively studied in recent years, with most algorithms operating in two-dimensional (2D) regions and based on balanced spectrum tensors only. To meet the growing demand for three-dimensional (3D) SC, expanding the 2D tensor completion algorithms to 3D versions is either straightforwardly applicable with an intrinsic accuracy deficiency or technically challenging to process the 3D-structured spatial data meticulously. Additionally, these algorithms suffer significant performance degradation when encountering unbalanced spectrum tensors. To address these issues, we propose multi-band 3D SC algorithms based on unbalanced spectrum tensors sporadically collected in 3D space across multiple bands. The unbalanced spectrum tensors are first transformed into balanced tensors using the 3D-based ket augmentation algorithm (3DKA), overcoming the unbalanced tensor’s incapability to leverage its low rank for estimating the entire tensor. Subsequently, tensor train matricization is employed to obtain more balanced matrices and improve tensor completion accuracy. Finally, tensor completion is accomplished using either the 3DKA-aided parallel matrix factorization (3DKA-PMF) or the Frobenius norm-based singular value decomposition-free (3DKA-SVDF) algorithm. Simulations show that the 3DKA-PMF algorithm achieves a minimum improvement of 20.06% over the conventional PMF algorithm. Notably, the 3DKA-SVDF algorithm exhibits slightly inferior performance but significantly shorter runtime—at most 58.3% of that of the 3DKA-PMF algorithm.

With the evolution of 6G technologies, spectrum scarcity has emerged as a critical challenge. Semantic communication, as a content and task-oriented paradigm, offers a promising solution to alleviate spectrum limitations. We propose a novel resource allocation scheme based on a signal-to-interference-plus-noise ratio (SINR)-aware feature optimization mechanism (SFOM) for dynamic semantic image transmission (DSIT). To better characterize semantic transmission efficiency, we define a new performance metric, image semantic spectrum efficiency (ISSE), which employs semantic features as information conveyors rather than traditional bit-level representations. To maximize ISSE under image quality constraints, we formulate a joint optimization problem involving multiple discrete variables, including compression ratio (CR), channel assignment, and power allocation. To address this problem, we develop an SFOM-driven proximal policy optimization (SFOM-PPO) algorithm that evaluates the significance of semantic feature channels and models the nonlinear relationships among the peak signal-to-noise ratio (PSNR), CR, and SINR, enabling adaptive semantic feature selection and optimal resource allocation under dynamic communication conditions. Experimental results demonstrate that the proposed SFOM-PPO significantly outperforms baselines in ISSE performance, achieving an effective balance between image transmission quality and resource efficiency. This work provides initial insights into semantic spectral-efficient resource management and suggests a potential framework for future wireless semantic image transmission systems.

The Internet of Things (IoT) employing symbiotic radio (SR) technology encounters challenges such as low throughput and susceptibility to double fading. To address these challenges, this paper integrates non-orthogonal multiple access (NOMA) with simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) technology in an SR system, introducing a novel transmission model termed STAR-RIS-assisted NOMA-SR with hybrid active-passive communication. The proposed model operates in three phases. In the first two phases, when the primary system’s licensed spectrum is occupied, the backscatter devices (BDs) utilize backscatter communication (BC) to establish a symbiotic relationship with the primary system. Specifically, in Phase 1, STAR-RIS enhances the energy harvesting (EH) of BDs via the reflection mode, while in Phase 2, it aids both the primary and secondary systems via the transmission mode. In Phase 3, when the licensed spectrum is idle, STAR-RIS facilitates the active communication (AC) of BDs via the transmission mode. To maximize the total throughput of BDs while guaranteeing the primary system’s target throughput, we formulate a non-convex optimization problem and develop a block coordinate descent (BCD)-based resource allocation scheme. The problem is decomposed into subproblems and solved using successive convex approximation (SCA), variable substitution, and semi-definite relaxation (SDR) to jointly optimize transmission time, beamforming, STAR-RIS reflection and transmission coefficients, as well as BDs’ power allocation and reflection coefficients. Numerical results show that the proposed scheme enhances the total throughput of BDs by 14.36%, 43.43%, 67.78%, and 439.69% compared to four baseline schemes.

Semantic communication reduces data transmission by transmitting task-relevant semantic information, offering a new approach for efficient transmission in resource-constrained scenarios. To address the issues of inefficiency and data loss caused by bandwidth constraints and dynamic channel variations in satellite communications, this paper proposes a semantic communication architecture based on the Transformer and Generative Adversarial Networks (GANs) called TransGAN. This architecture employs a Transformer to extract core semantic features from telemetry data, effectively compressing the data to reduce bandwidth usage, while the generator learns the distribution of core semantic features through adversarial training, enabling it to generate realistic samples even when semantic information is missing. Furthermore, TransGAN is designed with a robust adversarial optimization strategy to enhance recovery performance in high-noise or packet loss scenarios within dynamic channel environments. Experimental results show that TransGAN significantly outperforms existing methods under various SNR and rates of semantic loss, achieving efficient and reliable semantic recovery and providing a new technological pathway for satellite Telemetry data transmission.

With the widespread adoption of edge computing, service migration is critical for meeting real-time computing demands and ensuring service continuity. However, the dynamic and uncertain nature of edge computing-enabled networks, characterized by fluctuating topologies, bandwidth, and resources, significantly complicates migration decisions. Existing strategies rely on precise analytical models and reward functions but struggle with generalization and adaptability. This paper proposes a novel service migration strategy driven by active inference for edge computing-enabled networks. Unlike traditional approaches, it eliminates the need for explicit reward functions, instead leveraging a cognitive optimization mechanism where decisions are guided by minimizing free energy. This allows the system to maintain efficient service migration across a wider range of edge scenarios, with enhanced generalization and flexibility. Simulation results show that the proposed strategy outperforms existing approaches by reducing latency and improving adaptability to varying environments, highlighting its superiority in service migration for edge computing-enabled networks.

In millimeter wave (mmWave) communication systems, beam alignment is doomed to play a vital role in ensuring directional link performance. In this paper, we propose a novel transformer-based angle prediction scheme to achieve fast and effective beam alignment. Transformer is one of the hottest seq2seq models in recent times, which is utilized to build the mapping relationship between the geographic position and beam alignment angles of users in this paper. Simulation results demonstrate the performance of the proposed scheme in terms of prediction accuracy and achievable sum rate.

In this work, we explore the task execution problem within unmanned aerial vehicle (UAV)-assisted low Earth orbit (LEO) satellite offloading networks. We define a system cost function that includes both energy consumption and task dropping cost, and formulate the joint power allocation, task offloading and scheduling, and UAV flight trajectory planning problem as a constrained system cost minimization problem. Given that the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we decompose the problem into four subproblems, i.e., IoT device task transmission subproblem, UAV trajectory design subproblem, power allocation subproblem, and task offloading and computing scheduling sub-problem. We propose an iterative algorithm for the first three subproblems and a heuristic for task offloading and computing scheduling subproblem. The simulation results reveal that our proposed method achieves superior performance compared to the existing algorithm.

The identification of the root causes of failures in complex Internet of Things (IoT) systems has always presented a significant challenge. Despite the extensive range of algorithms and technologies already available for Root Cause Analysis (RCA) in various fields, there remains a lack of RCA methods specifically designed for IoT systems. The present paper proposes an IoT system root cause analysis framework based on dynamic causal graphs, assisted by Large Language Models (LLMs), called LLMs-DCGRCA. Firstly, in response to the issues with traditional causal hypothesis methods, which rely on human experience and suffer from key variable omission and incorrect causal direction assumptions, this paper proposes a method for generating causal hypotheses for IoT systems by using knowledge graphs to enhance the performance of LLMs. Secondly, in response to the challenge that traditional causal learning methods in IoT scenarios struggle to capture causal relationships across the temporal dimension, this paper proposes a dynamic causal graph learning method that incorporates causal constraints. Finally, in response to the limitations of traditional root cause analysis methods in IoT scenarios in accurately capturing the dynamic characteristics of anomaly propagation, this paper proposes a cumulative root cause localization method based on dynamic causal graphs. The evaluation of LLMs-DCGRCA is conducted using IoT trace data collected from simulation environments and GAIA, a widely-used public dataset in the field of intelligent operations and maintenance. The evaluation results demonstrate that LLMs-DCGRCA achieves average HR@7 improvements of 14.04% and 9.35% compared to baseline methods on the two datasets, respectively.

To address the diverse user service requirements in network slicing (NS) and the challenges of synchronization accuracy and low latency in Digital Twin (DT) deployment, this paper proposes a joint optimization strategy for digital twin-based dual-time domain slice resource management and DT deployment in the Internet of Vehicles (IoV). First, a demand-driven slice resource management scheme is introduced to mitigate Quality of Service (QoS) degradation caused by insufficient resource allocation under fluctuating user demands. Network performance indicator weights are dynamically adjusted using demand enhancement factors. Second, to minimize the impact of DT synchronization on resource allocation and address challenges like low latency and delay bias, DT utility is quantified from three perspectives: completeness, load contribution, and resource reliability. Finally, a price incentive mechanism with dynamic load adjustment is designed to balance the supply and demand of DT and service resources. This joint optimization problem is an NP-hard mixed-integer nonlinear problem with dual time-domain coupling, which can be decomposed into utility maximization strategies in different time domains. In the long-time domain, a Q-value-based Deep Transfer Reinforcement Learning (QDTRL) algorithm is used for DT deployment, while in the short-time domain, a Long Short-Term Memory - Multi-Agent Proximal Policy Optimization (LSTM-MAPPO) algorithm is applied for resource allocation and DT synchronization weight adjustment. Simulation results show that, compared to baseline schemes, the proposed strategy achieves higher utility, accelerates convergence, and effectively allocates resources and synchronizes DT.

With the rapid development of the Internet of Things (IoT), many IoT devices are accessing the network. However, existing networks cannot fully meet the strict and diverse requirements for delay and reliability in delay-sensitive services. Dynamic changes in service requests and the states of service nodes cause a lack of guaranteed end-to-end (E2E) network slicing delay determinism. To address this issue, we propose a digital twin (DT)-assisted network slicing resource allocation scheme. By integrating DT and network slicing, we first construct a DT-assisted E2E network architecture, and construct the base and mapping models in the proposed architecture. Second, we use the stochastic network calculus (SNC) theory to analyze the E2E delay violation probability and characterize the relationship between delay and service reliability under given traffic arrival distributions and delay constraints. Then, we construct a joint resource allocation problem of time-frequency, computation, storage, and bandwidth resources to maximize the utility of the infrastructure provider while guaranteeing the deterministic delay. Furthermore, a multiagent deep reinforcement learning algorithm in a distributed architecture is used to solve the complex optimization problem, achieving efficient network resource allocation. Simulation results demonstrate that the proposed resource allocation scheme meets the requirements for deterministic delay and enhances system utility.

To address the challenges faced by vehicles using integrated sensing and communication (ISAC) devices in maintaining sensing quality while maximizing the timeliness of sensing information, as well as the allocation of limited edge computing resources when large amounts of sensing data are uploaded to edge servers (ESs), we propose an integrated sensing, communication resource allocation, and digital twin (DT) placement scheme based on DT in IoV. First, a frame structure with an adjustable ISAC slot ratio was designed. Under the constraints of sensing mutual information and uplink communication rate, the timeliness of sensed data is enhanced by minimizing the Age of Information (AoI) during data transmission. Second, a vehicle DT (VDT) placement cost model, incorporating both latency and energy consumption, was constructed. The optimal VDT placement strategy was derived by minimizing placement costs, leading to efficient allocation of edge computing resources. Furthermore, considering the impact of vehicle mobility on real-time interaction between vehicles and VDTs, a migration utility based on vehicle migration latency and migration energy consumption between edge nodes was designed. On this basis, a joint system utility maximization optimization model was established, integrating sensing data AoI utility, VDT placement cost, and migration utility. Due to the continuous and discrete variables in the optimization problem, we develop an algorithm based on multiagent deep reinforcement learning (MADRL) to solve the problem, called SCDTP-MPDQN. Simulation results demonstrate the superiority of the proposed scheme in enhancing sensing information utility and system utility.

As industrial IoT networks evolve, they become increasingly vulnerable to cyberattacks, such as Denial of Service and backdoor attacks, which lead to anomalies in data streams (e.g., unusual spikes or drops in traffic, sudden changes in device behavior, or irregular communication patterns). To address the challenge of detecting these anomalies amidst dynamic data distributions and diverse abnormal patterns, this article proposes a supervised contrastive learning-based spatiotemporal variational autoencoder (SC-STVAE) for anomaly detection in online data streams. A multihead graph attention network (MD-GAT) is utilized to capture feature correlations, while a temporal convolution network serves as the hidden layer in the variational autoencoder. This enables SC-STVAE to learn both feature correlations and temporal dependencies. To resolve the issue of ambiguous positive and negative boundaries, supervised contrastive learning is introduced within the STVAE, improving boundary distinction and detection accuracy. To mitigate performance degradation due to data drift, an event-triggered elastic weight consolidation algorithm is introduced, which updates model parameters based on reliability thresholds. Additionally, a fuzzy entropy-weighted anomaly score, which measures the error between reconstructed data and original inputs by computing the weighted sum of the mean squared error across each dimension, is introduced. Experimental results demonstrate superior performance in terms of accuracy, recall, and F1 score compared to benchmark algorithms.

Aiming at the problems of data synchronization and resource limitation in smart driving on Internet of Vehicles (IoV), we investigate the construction of digital twins (DTs) and resource allocation on IoVs with the aim of achieving the optimal system performance. DT is a key technology in the future of autonomous driving. By digitizing physical components, it can effectively predict, analyze, and optimize various services of intelligent driving. First, we propose an accurate DT construction scheme (SL-ADTC) based on swarm learning (SL), which uses distributed learning algorithms to predict the queuing waiting time at relay nodes and the update frequency of DTs to solve data deviation and desynchronization for constructing DTs. Second, to address the problem of insufficient local computational resources of the task vehicles, a DT-based task division cooperative processing (DT-TDCP) mechanism is proposed. Then, considering the contradiction between task delay and system energy consumption, a joint optimization problem of computational offloading decision and resource allocation is proposed to minimize the total system cost under the premise of guaranteeing the high accuracy of DTs. Regarding the problem that the state space dimension is too high in reinforcement learning and the algorithm has difficulty in convergence, a DT-assisted multiagent classification proximal policy optimization (MACPPO) algorithm is proposed. Numerical results show that the proposed schemes outperform several benchmark schemes in terms of DTs construction accuracy, service latency, and system energy consumption.

The average peak age of information (PAoI) and the average age of information (AoI) are crucial for control policies in the satellite Internet of Things (S -IoT) observation environment. However, most existing studies fail to accurately analyze AoI in specific scenarios, and the impact of backoff algorithms on AoI and PAoI remains to be thoroughly investigated. To optimize these two metrics simultaneously and improve access performance, this paper proposes a joint backoff algorithm based on information freshness and NE equalization. First, to address the issues of low data transmission efficiency and inaccurate decision making in satellite IoT, discrete time Markov chains are introduced to assess the information freshness under satellite IoT, and the relationship between the node’s retreat phase and PAoI is depicted. Second, to solve the problem of access congestion in satellite IoT, the satellite coverage area is divided into different levels according to the threshold value of the spreading factor. A access strategy consistent with the NE equilibrium is defined, a revenue function based on the probability of successful transmission is designed, and a closed form mixed strategy Nash equilibrium (NE) is found using an optimization method. Finally, a value differentiated joint backoff algorithm based on information freshness and NE equalization is proposed. The simulation results show that the algorithm can improve the system throughput rate and reduce the average PAoI and AoI under satellite IoT.

To address issues related to reduced neural training accuracy, increased latency, and high energy consumption due to resource limitations of edge servers in the Internet of Vehicles (IoV), this article proposes a joint optimization scheme for edge collaboration and resource allocation in the digital twin-assisted IoV (DT-IoV) with integrated sensing and communication (ISAC). First, a digital twin-assisted environment-aware collaborative mechanism (DT-EACM) is proposed. The DT system makes edge collaboration decisions based on task requirements and available resources, selecting either the ISAC mode or the coordinated environment-aware instruction transmission (InsT) mode. Second, an analysis of deviations in communication, sensing, and computing resources within the collaboration mechanism has been conducted, and a joint optimization problem for edge collaboration and resource allocation is formulated with the goal of minimizing cumulative latency and energy consumption. Finally, since the above problem is a mixed-integer programming problem, it is transformed into a Markov decision process (MDP). A resource scheduling algorithm based on twin-delayed deep deterministic policy gradient (TD3) is proposed, combined with the prioritized experience replay (PER) mechanism to improve algorithm convergence speed and stability, thus minimizing cumulative latency and energy consumption in edge task processing. The simulation results demonstrate that the proposed algorithm effectively reduces cumulative training latency and energy consumption in edge task collaboration compared to other algorithms.

In the complex dynamic environment of the Internet of Things (IoT), the massive terminal devices generating stochastic network service requests pose significant challenges to Service Function Chain (SFC) deployment, particularly in dynamic configuration and resource utilization efficiency. To address these issues, this paper proposes an SFC deployment algorithm incorporating incremental learning with resource-aware mechanisms. First, an incremental learning-based deep-width neural network model is designed, which integrates the spatiotemporal feature extraction capabilities of Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN) with the continuous learning ability of the Broad Learning System (BLS), enabling dynamic prediction of Virtual Network Function (VNF) resource demands. Then, based on the prediction results, resource capacity awareness is used to assess the VNF deployment potential of nodes. Under the constraints of delay and network resources, an optimization problem is formulated with the objectives of maximizing the benefit-cost ratio of SFC deployment and minimizing deployment energy consumption. Finally, to solve this optimization problem, a multi-agent generative adversarial imitation learning algorithm is proposed, which guides the agents’ policy learning through expert experience to improve algorithm performance. Simulation results show that the proposed algorithm performs well in terms of enhancing node resource awareness, SFC deployment benefits, and acceptance rates.

This paper investigates physical layer security (PLS) in wireless interference networks. Specifically, we consider confidential transmission from a legitimate transmitter (Alice) to a legitimate receiver (Bob), in the presence of non-colluding passive eavesdroppers (Eves), as well as multiple legitimate transceivers. To mitigate interference at legitimate receivers and enhance PLS, artificial noise (AN) aided interference alignment (IA) is explored. However, the conventional leakage minimization (LM) based IA may exhibit confidential signal cancellation phenomenon. We theoretically analyze the cause and then establish a condition under which this phenomenon will occur almost surely. Moreover, we propose a means of avoiding this phenomenon by integrating the max-eigenmode beamforming (MEB) into the traditional LM based IA. By assuming that only statistical channel state informations (CSIs) of Eves and local CSIs of legitimate users are available, we derive a closed form expression for the secrecy outage probability (SOP), and establish a condition under which positive secrecy rate is achievable. To enhance security performance, an SOP constrained secrecy rate maximization (SRM) problem is formulated and an efficient numerical method is developed for the optimal solution. Numerical results demonstrate the effectiveness and the usefulness of the proposed approach.

In the coexisting new radio-based access to unlicensed spectrum (NR-U) and WiGig networks (CNWNs), directional-sensing-based listen-before-talk (LBT) mechanisms, i.e., directional LBT (dirLBT) and paired LBT (pairLBT), have been proposed to address the exposed node problem caused by traditional omnidirectional LBT (omniLBT) mechanism. In this paper, we are the first to leverage the stochastic geometry to analyze the large-scale CNWN performance when NR-U base stations (NBSs) adopt the directional-sensing-based LBT mechanisms. The analytical expressions for the downlink successful transmission probabilities (STPs) of CNWNs are derived and validated by Monte Carlo simulations. Based on these STPs, the area spectral efficiency (ASE) of CNWNs is derived. Equipped with these results, the effect of NBS sensing threshold, density and sensing beamwidth on the STP and ASE performance are analyzed numerically. Moreover, the STP and ASE performance are compared when NBSs adopt dirLBT, pairLBT and omniLBT mechanisms. Furthermore, the asymptotic ASE of CNWNs when NBS density approaches infinity is derived and validated. The results show that directional-sensing-based LBT mechanisms outperform the omniLBT mechanism in terms of ASE in the CNWNs, especially in ultra-densely deployed scenarios. Under our simulation environment, the dirLBT mechanism can improve the ASE by up to 82.5% as compared with the omniLBT mechanism. Additionally, the NBS sensing threshold for directional-sensing-based LBT should be higher than −73 dBm to achieve a better STP and ASE as compared with that without adopting LBT in NBSs. Besides, there exists an optimal NBS density to maximize the STP and ASE of CNWNs, and when NBS density becomes larger than$200,000$NBSs per$\text {km}^{2}$, deploying more NBS has limited enhancement on the ASE. These results indicate that directional-sensing-based LBT mechanisms should be employed in the ultra-densely deployed CNWNs, and the NBS sensing threshold and sensing beamwidth should be carefully chosen to ensure the superiority of directional-sensing-based LBT mechanisms.

To overcome the impact of information leakage, obstacle blocking, channel uncertainties, and hardware impairments (HWIs) in wireless communication systems, we design a robust secure transmission strategy for a multi-reconfigurable intelligent surface (RIS)-aided communication system with HWIs and channel uncertainties, where a multi-antenna base station (BS) serves multiple wireless users aided by multiple RISs and overcomes information leakage caused by multiple eavesdroppers. Based on bounded channel uncertainties, a total transmit power minimization problem is investigated subject to the secrecy rates of users, the maximum transmit power of the BS, and the phase shifts of RISs. To deal with the formulated non-convex problem with parameter perturbations, it is transformed into a deterministic problem by using the worst-case approach, S-procedure, and successive convex approximation. Then, the problem is decomposed into an active beamforming and artificial noise subproblem and a passive beamforming subproblem. The subproblems are converted into convex ones via the semi-definite relaxation method, singular value decomposition, penalty function, and eigenvalue decomposition approaches. Finally, an iteration-based robust resource allocation algorithm is proposed. Simulation results verify that by deploying more RISs or increasing the number of reflection elements, the impacts of eavesdroppers and HWIs can be effectively decreased even with channel estimation errors.

In multi-user adversarial scenarios involving external malicious jamming and internal co-channel interference, environmental instability and increased decision-making dimensions cause traditional deep reinforcement learning (DRL)-based anti-jamming schemes to suffer from insufficient exploration. Agents must choose policies from a large action set, leading to a significant decline in anti-jamming performance. To address these issues, this paper proposes a multi-agent discrete soft actorcritic (MA-DSAC) algorithm-based collaborative anti-jamming strategy, integrating frequency, power, and modulation-coding domains. This strategy first introduces a Markov game to model and analyze the multi-user anti-jamming problem. Next, the soft actor-critic (SAC) algorithm is discretized to handle the multi-dimensional discrete action space. Finally, through information exchange between communication transceivers and based on a centralized training with decentralized execution (CTDE) framework, it is extended to a multi-agent DRL algorithm to achieve efficient multi-user cooperative anti-jamming. Simulation results show that in various anti-jamming scenarios with both fixed-mode and intelligent jammers, the proposed anti-jamming strategy’s performance improves by more than 25% compared to traditional value-based DRL strategies, including independent deep Q-network (I-DQN) and multi-agent virtual exploration in deep Q-learning (MA-VEDQL). Furthermore, through information exchange between communication transceivers, the instability problem of multi-agent DRL is effectively alleviated, enabling the communication transceivers to balance competition and cooperation. Consequently, its anti-jamming performance improves by more than 6% compared to the independent DSAC (I-DSAC) strategy.

In complex jamming environments with multi-user spectrum sharing, existing distributed anti-jamming strategies are constrained by significant communication overhead, limited efficiency in knowledge dissemination, and low collaborative effectiveness. To address these challenges, a distributed anti-jamming strategy based on local knowledge diffusion and differential weighted fusion mechanisms (LKD-DWF-M) is proposed. In this strategy, a local knowledge diffusion mechanism is introduced to facilitate knowledge sharing among communication nodes, enabling each node to gain a comprehensive understanding of its neighbors’ behavior. Subsequently, a knowledge contribution measurement method based on mutual information is proposed, and a differential weighted fusion (DWF) mechanism is designed to effectively integrate the policy and value parameters of neighboring nodes. This integration enables accurate global value estimation while optimizing individual anti-jamming strategies. Additionally, the existence of the Nash equilibrium (NE) for each node’s policy and value parameters is theoretically established using Kakutani’s fixed-point theorem. Furthermore, through the construction of a Lyapunov function, it is demonstrated that the proposed strategy can stabilize and converge to the NE in the long-term jamming counteraction process. Simulation results indicate that, in comparison to anti-jamming strategies employing global knowledge diffusion and differential weighted fusion mechanism (GKD-DWF-M), global knowledge diffusion and average fusion (GKD-AF-M), and local knowledge diffusion and average fusion (LKD-AF-M), the proposed distributed anti-jamming strategy achieves respective improvements of 4%, 17%, and 20% in system normalized throughput under statistical jamming (SJ). Under dynamic sweeping jamming (DSJ), the system normalized throughput improves by 8%, 11%, and 11.5%, respectively; under intelligent comb jamming (ICJ), it increases by 10%, 10.5%, and 19%, respectively; and under intelligent block jamming (IBJ), it increases by 5%, 16%, and 21%, respectively. Moreover, the proposed strategy exhibits superior convergence speed compared to other strategies. When the jammer alternates between SJ, DSJ, ICJ, and IBJ, the proposed distributed anti-jamming strategy responds quickly, demonstrating robustness in dynamic jamming environments.

Vehicle digital twin (VDT) can support multiple different vehicle services through updating, and the updating of VDT faces two fundamental issues: one is the isolation of VDT, that is VDTs can run various services without being affected by others; the other one is the timeliness of VDT updates for low latency services. In this paper, we first propose to ensure the isolation of VDTs with the assistance of intelligent reflecting surface (IRS) and network slicing (NS), and obtain better update time of the VDTs within limited resources. Specifically, we divide resources for VDTs with different update requirements to ensure the isolation of VDTs and the resources required for update. On the other hand, considering that the communication performance between vehicles and base stations is affected by urban building density, we propose using an intelligent controller to achieve intelligent control of the physical channel by adjusting the phase shift of passive reflective elements to ensure better transmission performance during VDTs updating. Secondly, considering the dynamic variability of vehicles and the environment, we propose an improved deep reinforcement learning algorithm based on the actor-critic framework to allocate communication, computing resources, and adjust the phase shift of the IRS. Finally, a large number of simulation results indicate that our proposed algorithm performs better than the benchmark algorithms.

To address the low utility of resource management strategy due to diversified user Quality of Service (QoS) requirements and inaccurate information synchronization in Digital Twin Networks (DTN), we propose a dynamic slice resource management and information synchronization strategy in Internet of Vehicles (IoV) based on Digital Twin (DT). First, to realize the dynamic resource allocation between slices and users respectively to adapt to network dynamics, we propose a two-level dynamic resource management strategy based on the DT-assisted slicing architecture of IoV. Second, to guarantee the timeliness of user information transmission and realize the accuracy of the resource management strategy, we propose a DT satisfaction evaluation model including DT mapping granularity, DT timeliness, and resource residual rate to quantify the users’ satisfaction with their DTs association. Finally, we establish a joint optimization model for resource management and DT association to maximize system utility. To address the coupling between strategies, we split the optimization problem into service utility and information synchronization utility subproblems. In the service utility subproblem, we propose a Counterfactual Multi-Agents Twin-Actors Soft Actor-Critic (COMATASAC) algorithm, which can perform slice-level and user-level resource allocation and scheduling actions separately. In the information synchronization utility subproblem, we utilize the Branching Dueling Q-network (BDQ) algorithm to solve the dimensionality explosion problem, and implement the association policy between users’ DTs and servers. Simulation results show that the proposed scheme can effectively reduce the latency and improve the satisfaction of DTs deployment while guaranteeing the QoS.

Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), which promotes connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data to protect data privacy. In this paper, consider the limitation of model accuracy and communication overhead in FL, as well as further verification in the real scenarios, we propose a directed acyclic graph (DAG) blockchain-based IoV system that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-assisted asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model accuracy, which utilizes mutual distillation method to train a teacher-student model simultaneously. Moreover, a policy network will first be pre-trained by an expert data augmentation strategy through the DAFML algorithm via the behavior cloning, and be re-trained through the proposed proximal policy optimization (PPO) algorithm based autonomous driving framework. Finally, simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy, distillation ratio and autonomous driving decision.

Collaborative resource allocation is crucial for reducing overhead and enhancing resource utilization in edge computing-enabled networks. To ensure a satisfactory user experience, we recognize the importance of considering information uncertainty in resource allocation. Therefore, we explore information uncertainty in edge computing-enabled networks, especially within the complex environment of resource coupling. However, existing methods lack a comprehensive and robust solution for coordinating wireless, transport, and computing resource under this information uncertainty. This paper addresses this gap by proposing a joint optimization of access point (AP) selection, computing node association, and traffic engineering, aiming to maximize network utility under the uncertain conditions of wireless status and application QoS requirements. The constraints under these uncertainties are modeled as chance constraints, complicating the problem's solvability. We adopt the Bernstein approximation to establish convex conservative approximations of the chance constraints. Given the problem's substantial size and computational complexity, the alternating direction method of multipliers is employed to solve the approximated problem in a distributed manner. We further derive the closed solutions of the corresponding sub-problems. Extensive simulations validate the superiority of our proposed scheme, demonstrating its ability to achieve a good trade-off between meeting user requirements and optimizing resource utilization.

In this study, we investigate minimization of energy consumption in multi-UAV assisted networks. We formulate an energy minimization optimization problem with UAV trajectory design, content fetching, power allocation and content placement constraints. The problem is a mixed integer nonlinear programming (MINLP); therefore, we convert the formulated problem into semi-Markov decision process (SMDP). To tackle this SMDP optimization challenge, we introduce an option-based hierarchical deep reinforcement learning (OHDRL) approach. We designate UAV trajectory planning and power allocation as the low level action space, and content placement and content fetching as the high level option space. Through simulations, we demonstrate the effectiveness of the proposed OHDRL method.

Low Earth Orbit (LEO) satellite systems provide geographically unrestricted services to ground users. However, the conflict between existing resource allocation schemes and the variability of inter-beam traffic is becoming increasingly prominent. To address this issue, this paper proposes a resource allocation strategy for beam-hopping satellite networks based on Rate-Splitting Multiple Access (RSMA) technology, aiming to reduce co-channel interference while improving system resource utilization. First, by analyzing the resource allocation challenges faced by beam-hopping satellite networks, including low spectrum utilization and co-channel interference, the background and motivation for the proposed strategy are provided. Next, RSMA technology is introduced, dividing user messages into common and private parts, and a corresponding resource allocation algorithm is designed to enhance spectrum utilization and reduce co-channel interference. Through the construction of a system model and simulation experiments, the effectiveness and performance advantages of the proposed strategy are verified. This study provides new ideas and methods for resource allocation in beam-hopping satellite networks, which is significant for improving system performance and service quality.

Unmanned aerial vehicles (UAVs) are expected to act as aerial relays which forwards data packets for ground users (GUs) leveraging their advantages of low cost, high flexibility and maneuverability. One challenging problem in UAV-assisted cellular systems is how to design the efficient UAV deployment, GU association and resource allocation strategy which achieves system performance optimization. In this paper, we address the data transmission problem in a UAV-assisted cellular system with the knowledge of statistical GU positions. Stressing the energy consumption of base station (BS) and UAVs, and the cost of UAVs, we formulate the joint UAV deployment, GU association and power allocation problem as a constrained system cost minimization problem. To solve the formulated problem, we decouple it into three subproblems, i.e., UAV deployment, GU association and power allocation subproblem. Then, the UAV deployment subproblem is modeled as a Markov decision process (MDP), and an embedded multi-agent double deep $\mathbf{Q}$ network (DDQN) algorithm is proposed. Specifically, given the state and action of the MDP, we formulate and solve the power allocation subproblem and determine the transmit power of the UAVs by applying the Lagrange dual method-based algorithm. The GU association subproblem is then tackled by utilizing a proposed Kuhn-Munkres (K-M) algorithm-based scheme. Based on the obtained power allocation and GU association strategy, the reward of the MDP can be computed and the UAV deployment strategy is determined which maximizes the long-term average reward. Simulation results demonstrate the effectiveness of the proposed algorithms.

To mitigate multi-user interference at legitimate receivers and assist secure transmission, artificial noise (AN) aided interference alignment (IA) is explored. The feasibility of traditional leakage minimization (LM) based IA is analyzed, including IA equations and rank constraint related to the secret signal transmission. We show that there exist two drawbacks which are not discussed in LM based IA security approaches: 1) the necessary condition for the feasibility of IA equations is loose, and 2) the secret signal cancellation may occur which makes secure transmission infeasible and in fact, there is no systematic analysis of this problem. We analyze the cause and establish a condition under which the secret signal cancellation is inevitable. Then a modified IA approach is proposed by implementing the max-eigenmode beamforming (MEB) for secure transmission. Compared with the traditional LM based IA, a tighter necessary condition can be established, and the secure transmission can be proctected. Numerical results confirm the effectiveness and usefulness of the modified IA approach.

The uncertain factors such as network status, measurement errors and quality of service (QoS) requirements of applications make it challenging to guarantee the performance of edge computing-enabled networks through resource allocation schemes modeled on accurate information. This paper investigates the impact of information uncertainty on resource allocation in edge computing-enabled networks. We model the resource constraints as chance constraints and jointly optimize wireless access point (AP) selection, computing node association, and traffic engineering to maximize the network utility. Since the problem contains uncertainty parameters and binary variables, it is intractable to solve. Therefore, we utilize the Bernstein approximation to derive convex conservative approximations for chance constraints. To address the unrealistic nature of the problem due to its large size and computing complexity, we employ the alternating direction method of multiplier to iterate wireless AP selection, computing node association, and bandwidth allocation in a distributed manner. Additionally, we use the convex optimization method to solve the corresponding sub-problems. Simulations are conducted to demonstrate that our proposed resource allocation scheme can satisfy more requirements and save more resources than other schemes.

The dynamic edge computing-enabled networks contain various resources, and network parameters and system models are subject to uncertainty. Despite this, there is still a lack of comprehensive online solutions for coordinating wireless, transport, and computing resources. This paper investigates the use of online convex optimization for resource allocation in edge computing-enabled networks with time-varying cost and time-varying constraint functions. Taking into account the uncertainty of wireless status, quality of service requirements, and cost function, the goal is to minimize the long-term cost by optimizing the selection of access points, association of computing nodes, allocation of computing resources, and bandwidth allocation. To address the proposed online resource allocation problem, the modified online saddle-point algorithm is employed and dynamic regret and accumulative constraint violation are defined to measure the performance of the algorithm. To reduce the computational complexity of the projection in the modified online saddle point algorithm, the projection is reformulated as quadratic programs, which can be solved efficiently by convex optimization. Finally, the effectiveness and superiority of the proposed solution are demonstrated through simulation analysis.

In this paper, we propose a low‐complexity expectation propagation (EP) detector for orthogonal time frequency space (OTFS) system with practical rectangular waveforms. In the high‐mobility scenario, OTFS is becoming a potential scheme for the sixth‐generation (6G) wireless communication system. However, the large size of the effective delay‐Doppler (DD) domain channel matrix brings unbearable computational complexity to the signal detection algorithm based on the matrix inversion. We propose a low‐complexity EP detector based on the sparsity and the block circulant structure of the effective channel covariance matrix in the DD domain. The proposed algorithm only requires log‐linear complexity. In addition, simulation results show that the proposed algorithm not only has the advantage of low complexity but also has good performance, which achieves a tradeoff between performance and complexity.

In this work, we investigate content delivery and target sensing problem in unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks where UAVs are allowed storing user-requested contents, delivering the content to users and performing target sensing as well. To jointly address the performance of content transmission and target sensing, we define utility function and formulate the UAV deployment, communication and sensing precoder design problem as a constrained utility maximization problem. As the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we transform it into two subproblems, namely, user grouping and UAV deployment subproblem, and communication and sensing precoder design subproblem, and solve the two subproblems by using an alternate iteration-based algorithm. Specifically, we first design a mean-shift-based user grouping strategy which divides users into different groups and then propose a UAV deployment strategy based on successive convex approximation (SCA)-based iterative algorithm and the first order Taylor expansion method. To solve communication and sensing precoder design subproblem, we propose a two-layer penalty-based SCA algorithm. Simulation results demonstrate the effectiveness of the proposed algorithms.

Network slicing (NS) enables flexible allocation of the Internet of Things (IoT) network resources through software-defined networking (SDN) and network function virtualization (NFV) technologies. However, dynamic variations in network traffic and resource demands can lead to node overload and failures, necessitating timely virtual network function (VNF) migration to safeguard IoT business Quality of Service (QoS). Addressing the issue of deteriorating QoS due to untimely VNF migration, we propose a digital-twin (DT)-assisted VNF migration strategy based on resource demand prediction. First, a prediction model combining convolutional neural networks, gated recurrent units, and attention mechanisms is proposed to forecast VNF resource requirements. Second, the granularity of DT synchronization information is adjusted to resolve issues of delay and high cost during DT construction. Then, a VNF migration model is constructed to maximize DT utility while reducing network costs and average resource variance. Finally, a multiagent algorithm combining long short-term memory (LSTM) and double deep Q-network (DDQN) is proposed to perform VNF migration based on their priority-driven predicted future resource demands, and a multiagent algorithm based on dueling DDQN (D3QN) and deep deterministic policy gradient (DDPG) is proposed to address the DT association problem with a mixed action space. The simulation results demonstrate that the proposed algorithm can reduce the synchronization latency of the DT, the violation rate of service-level agreements, and the service outage time rate, while improving the network load balancing capability.

Anomaly detection using multivariate time series (MTS) is critical for detecting abnormal traffic and device failures in 5G Internet of Things (IoT) devices. The current anomaly detection framework lacks the ability to model multidimensional long time series and to address issues, such as resource overhead, privacy protection, and data security in distributed learning modes within the IoT. Therefore, this article proposes an anomaly detection framework integrating knowledge distillation and swarm learning for 5G IoT (IoT-FKGDL-SL). First, to model the correlations between different variables, a new method for capturing correlations between variables through clustering is proposed. Second, to perform long-term modeling of MTS, a long-time-series anomaly detection model called IoT-FKGD is proposed, based on multiscale dilated convolution and locality-sensitive hashing (LSH) attention. Finally, a framework based on IoT-FKGD is proposed to detect traffic anomalies of IoT devices under a swarm learning architecture that incorporates knowledge distillation. The effectiveness of the IoT-FKGDL-SL framework is demonstrated by comparing it with advanced anomaly detection methods on real data sets. Experimental results show that on a long time scale, the precision, recall, and F1-score of anomaly detection using this framework all surpass those of baseline methods.

Network function virtualization (NFV) and software-defined networking (SDN) provide high-quality services to users of the Internet of Things (IoT). However, dynamic changes in network traffic and service function chain (SFC) resource requirements may result in virtual network function (VNF) migration issue and real-time triggering of VNF migration can cause service delay issues in SDN/NVF-enabled IoT networks. In this article, we propose a digital twin (DT)-assisted VNF migration strategy to effectively address this issue. The digital twin of VNF (DT-VNF) is integrated with a multitask DT migration model based on bidirectional-gated recurrent units (DTMBi-GRUs) to achieve accurate resource demands prediction. Based on this, the VNF migration strategy is formulated in advance to avoid network performance degradation. We focus on the post-migration effects on services, networks, and DTs, so migration plans for DT-VNF and a reassociation scheme are formulated to enable real-time monitoring of post-migration VNF by DT-VNF. Then, an optimization problem is formulated to minimize average network energy consumption, network resource differences, and SDN synchronization delay in order to obtain optimal strategies. In addition, considering the problem’s complexity, it is decoupled into the VNF migration problem and the DT association and migration problem. The collaborative solution involves employing the multiagent proximal policy optimization (MAPPO) and asynchronous advantage actor–critic (A3C). Simulation results confirm the superiority of the proposed algorithms over baseline algorithms.

As an important application scenario for 5G, the Internet of Vehicles (IoVs) achieves extensive and stable connections and real-time information interaction and sharing between vehicles and traffic infrastructure. To address the challenges of low-latency synchronization, high computational overhead, and multidimensional resource scheduling faced by the vehicle digital twin (VDT) construction process within IoVs, we propose a construction scheme for variable granularity VDT. First, a variable granularity VDT construction framework for IoVs is proposed to reduce the communication pressure, edge load, and energy consumption in the network. Second, the VDT utility is quantified from four dimensions: 1) completeness; 2) accuracy; 3) timeliness; and 4) energy efficiency. Then, an optimization model is established with the goal of maximizing the average utility of the system VDT, which involves joint optimization of vehicle-edge association, VDT granularity setting and resource allocation. Due to the complexity of the optimization problem, it is decomposed into subproblems of vehicle-edge association, VDT granularity setting and resource allocation, and solved using matching theory and multiagent deep reinforcement learning, respectively. Finally, simulation results verify that the proposed scheme can effectively improve the utility of VDT in different simulation scenarios while reducing system resource consumption.

Digital twins (DTs), as an effective technology for remote monitoring and management of devices, enhances the intelligence of the industrial Internet of Things (IIoT). Nonetheless, the unreliable and delayed transmission of sensory data in wireless access networks hinders the accurate reflection of DTs on the physical world. In this article, we present an intelligent dual time-scale network slicing strategy utilizing the long-term and short-term trends of network, aiming to make fuller use of network resources and improve the synchronization information accuracy of DTs. Specifically, within the dual time scale slicing framework, this strategy collaboratively optimize slice scaling and sensory information synchronization for DTs, aiming to maximize sensory information satisfaction and minimize the cost of slice reconfiguration and synchronization. First, at large time scales, we utilize slices to provide isolation and address deployment issues for DTs with different Quality of Service (QoS) requirements. At small time scales, we aim to enhance the adaptability of estimation tasks to dynamic environments through more flexible wireless resource allocation, further improving communication performance, and establishing DTs that closely resemble physical entities. Furthermore, to solve optimization problems at different time scales, we propose a two-layer deep reinforcement learning (DRL) framework to achieve efficient network resource interactions, in which the lower-layer control algorithms utilize the prioritized experience replay (PER) mechanism to accelerate the convergence speed. Finally, simulation results validate the effectiveness of the proposed strategy.

Network function virtualization (NFV) and software defined network (SDN) technologies enable flexible traffic scheduling and improve the efficiency of physical resources. However, for latency-sensitive Industrial Internet of Things(IIoT) services in Industry 4.0 and beyond, the inability of the SDN controller to synchronize the resource demand information of virtual network functions (VNFs) in a timely manner can lead to delays in VNF mapping and scheduling strategies. To address this issue, we propose a digital twin-assisted VNF mapping and scheduling algorithm that combines digital twin to assist the SDN controller in collecting data of VNFs. Firstly, we designed a digital twin-assisted and SDN/NFV-based network slicing architecture. Secondly, to reduce the synchronization delays of digital twins of VNFs, we propose a twin service node reassociation mechanism. Next, a digital twin-assisted VNF mapping and scheduling model is constructed under constraints such as CPU, storage, bandwidth resources, and quality of service (QoS) to maximize the service provider’s profit. Finally, we propose digital twin-assisted VNF mapping and scheduling algorithms based on greedy and tabu search to solve the problem. Leveraging digital twins, the SDN controller can obtain accurate resource demand information of VNFs, thereby solving the delay issue in VNF mapping and scheduling strategies. Simulation results indicate that the proposed algorithms yield favorable outcomes in terms of total profit, network service acceptance rate, average system delay of digital twins, and QoS satisfaction.

Due to the increasingly complex and dynamic network topology, as well as multiple layers in the cloud–edge–end collaboration scenarios in the Industrial Internet of Things (IIoT), service function chains (SFCs) generated from user requests are more prone to anomalies compared to traditional hardware solutions. In order to timely detect anomalies in the SFC and ensure service quality, we propose a time-series anomaly detection model based on a distributed knowledge distillation framework (DTS-KD) in this article. First, to detect each status of virtual network function (VNF) in the SFC, we propose a distributed teacher–student knowledge distillation architecture to perform anomaly detection on each link containing different VNFs. Second, to address the problem of neglecting spatial topology information of feature nodes in traditional SFC anomaly detection schemes, we propose a feature fusion-based spatial–temporal dilated convolution module encoding scheme, which utilizes spatial convolution with dilated convolution to jointly encode and capture spatial–temporal dependencies. Finally, during the knowledge transfer process between the teacher and student networks, we propose a progressive knowledge distillation algorithm, which automatically adjusts the student network learning stages by adjusting task attention weights. After training, the student network measures the presence of anomalies in the links at each moment through the reconstruction data anomaly scores, thereby completing the SFC anomaly detection at that moment. The effectiveness of this proposed method under model compression conditions is validated on the ITU AI/ML in 5G data set using four performance metrics: 1) F1 score; 2) accuracy; 3) precision; and 4) recall.

In recent years, unmanned aerial vehicles (UAVs) have experienced rapid development and have been widely used in many fields. Equipped with both communication modules and sensing modules, UAVs are capable of conducting integrated communication and target detection, thus greatly improving spectrum efficiency and system performance. In this article, we consider a scenario where multiple UAVs collaborate to detect targets and transmit the collected data to a central UAV. Addressing the problem of communication and perception scheduling, we first analyze the target detection and communication performance, and then formulate the joint communication and perception scheduling problem as two optimization problems, with the objectives being maximizing the average utility function (MAUF) and minimizing the completion time (MCT), respectively. To solve the formulated problems, we first consider the dynamic characteristics of the environment, and model the problems as two Markov decision processes. Regarding the UAVs as multiple agents, we then propose a multiagent double deep Q-network (DDQN)-based MAUF algorithm and a multiagent DDQN-based MCT algorithm to determine the communication and perception scheduling strategies of the UAVs. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Network availability and service continuity are major concerns for network operators to provide reliable communication services for Internet of Things (IoT), which are particularly challenging to achieve in virtualized network slicing environment where network services are exposed to the failure risks of both software (virtual network function (VNF) instances) and hardware (physical nodes). In general, the redundancy-based VNF backup solutions are used to improve the reliability of virtualized network slices. However, backup VNFs require the same amount of resources as the primary VNFs, which will result in high-resource cost. In this article, we propose a joint VNF partition and hybrid backup scheme for VNF orchestration, backup and mapping, whose aim is to construct the reliability-enhanced and delay-guaranteed network slices at minimum cost. Specifically, the VNF partition method divides a single VNF into multiple thinner VNFs with lower processing capacity and is expected to enhance the reliability of network slices with less additional resources. The hybrid backup scheme includes both onsite and offsite backup forms. Then, considering the time-varying network environment and IoT service requirements, we formulate the VNF orchestration, backup and mapping as a dynamic mixed integer linear programming (DMILP) problem, and model the dynamic problem as a Markov decision process (MDP). In view of the large action space of the formulated MDP, we propose a multiagent deep reinforcement learning (DRL) approach with an action space reduction strategy to achieve the dynamic VNF orchestration, backup and mapping solution. Simulation results demonstrate that the proposed joint VNF partition and hybrid backup scheme can obtain superior delay and reliability performance with low-network cost.

Multibeam satellite communication systems have been received widespread attention due to their high throughput and efficient resource utilization. In this article, we investigate the beam illumination and resource allocation problem in multibeam satellite communication systems. By jointly considering user position and service characteristics, an optics-based initial user grouping algorithm is proposed. To enhance beam coverage performance, a minimum circle algorithm is proposed to optimally design satellite beam positions and coverage radius. Given the obtained user grouping strategy, we address the difference between random user service demands and service provisioning capability of the system, and define system cost function. The joint beam illumination, subchannel, and power allocation problem is formulated as a system cost function minimization problem. To solve the formulated optimization problem, we introduce aggregate nodes to describe the characteristics of user groups, and address the beam illumination and power allocation problem of user groups. The problem is modeled as a mixed-space Markov decision process (MDP), and a parameterized deep Q-network-based joint beam illumination and power allocation algorithm is proposed. Based on the obtained resource allocation strategy for user groups, we then design user-oriented subchannel and power allocation strategy. To this end, we model the optimization problem as an MDP and propose a double deep Q-network (DDQN) algorithm-based algorithm. To address the concern that the DDQN algorithm may reach a local optimum, proximal policy optimization algorithms with discrete action space and continuous action space are proposed. Simulation results validate the effectiveness of the proposed algorithms.

Recent research has demonstrated that the heat induced by high data rate transmission could cause low-temperature burns. As a promising application for future wireless networks, unmanned aerial vehicle (UAV) communication is capable of providing high data rate transmission for ground users. Inspired by this progress, this article focuses on the resource allocation for the UAV scenario and proposes a novel framework to consider the newly mentioned phenomenon named by power-consumption outage (PCO). Specifically, we give the analysis of heat transfer model in the smartphone based on which we initially integrate the influence of PCO into the optimization problem of the UAV scenario. Furthermore, to solve the problem with joint optimization of bandwidth allocation and trajectory design, we propose a machine learning model consisting of the position prediction based on echo state network and the joint optimization based on deep reinforcement learning (DRL). Due to the continuity in action space, DRL optimization is specifically implemented by the normalized advantage function algorithm. Besides, considering the restriction for the implementation of machine learning, we propose a digital twin-enabled architecture to provide a virtual environment for the training. Simulation results show the advantage of the proposed scheme in total throughput and the adaptability for trajectory design in the presence of PCO.

Autonomous driving is state-of-the-art technology in the field of Internet of Vehicles (IoVs), considered a revolutionary solution to enhance driving safety and comfort. Collaborative path planning for autonomous driving vehicles has not been practically applied due to the low effectiveness of models trained by traditional machine methods. The Digital Twins (DTs) map the state of the autonomous vehicles (AVs) in the real world to the virtual space in order to analyze the vehicle behavior and optimize vehicle decisions. During the DTs synchronization process, the time-varying nature of the physical environment may result in loss of DTs synchronization data. In order to better ensure the fidelity of DTs and the safety of autonomous driving, we propose a DTs-assisted distributed federated reinforcement training framework with fidelity guarantee. In this framework, the DTs leverage its predictive function to fill the lost data during the vehicle synchronization to its twins, ensuring a high fidelity in mapping the vehicle state. Then the model undergoes training through distributed federated reinforcement learning within the DTs environment. The simulation results indicate that our proposed solution not only ensures high fidelity in modeling the digital twins but also enhances the utilization efficiency of vehicle speeds. Additionally, it reduces the collision probability and average task completion time for the vehicle swarm.

Considering the limited resources, user mobility and unpredictable driving environment in intelligent driving, this paper studies the optimal training efficiency of federated learning for distributed training of collision warning services with the assistance of digital twin (DT). DT is emerging as one of the most promising technologies to make the digital representation of physical components for better prediction, analysis, and optimization of various services in intelligent driving. we first propose a DT-enabled collision warning framework, including physical network layer, digital twin layer, and application layer. Then, for the cooperative training of multi-level warning models combining gate recurrent unit (GRU) and support vector machine (SVM) in the digital twin layer, we propose semi-asynchronous federated learning with adaptive adjustment of parameters (SFLAAP) scheme. We aim at minimizing the training delay of collision warning model by dynamically adjusting the training parameters according to real-time training state and resource conditions of digital space, specifically the local training times and the number of local nodes participating in the aggregation, while ensuring the accuracy of the model. Considering the complexity of the target problem, we propose parameter adjustment algorithm based on asynchronous advantage actor-critic (A3C). Experiments on the classical dataset show high effectiveness of the proposed algorithms. Specifically, SFLAAP can reduce the completion time by about 12% and improve the learning accuracy by about 1%, compared with the state-of-the-art solutions.

Multibeam satellite communication systems have received extensive attentions in recent years. By generating multiple spot beams at the transmitter of satellites, high-bandwidth connectivity to specific geographic areas can be achieved. In this paper, the joint beam illumination and resource allocation problem is studied for multibeam satellite communication systems. To address the different resource management granularity levels in the considered multibeam satellite system, we propose a hybrid offline and online resource management algorithm. Specifically, a two-step offline user grouping scheme is proposed under beam coverage and maximum user number constraints. Then, based on the obtained user grouping strategy, the online beam illumination and time-frequency resource allocation problem is studied. To this end, we jointly consider the revenue received from successful packet transmission and the energy consumption of the satellites due to data transmission, and define a system utility function. The problem of joint beam illumination and resource allocation is then formulated as a constrained utility function maximization problem. To solve the optimization problem, we introduce aggregate users (AUs) to represent the service requirements and the transmission characteristics of individual user groups, and design a proportional fairness and virtual Kuhn-Munkres-based beam illumination strategy for the AUs. Given the obtained beam illumination strategy of user groups, a two-level prioritizing scheme is proposed for the GUs and a priority and greedy-based algorithm is designed to assign time and frequency resources to the GUs. Numerical results verify the effectiveness of the proposed algorithm.

Multi-beam satellite communication systems have been widely recognized as an efficient technology for providing reliable and high-speed communication services. In this paper, we consider a multi-beam satellite communication system, which consists of a multi-beam satellite, ground cells and a ground gateway for processing information of the system. We focus on the beam scheduling, subchannel and power allocation problem to improve system performance. To jointly consider data transmission performance and power consumption, we define a utility function as the weighted sum of service queue length and satellite transmit power. To adapt to the dynamic arriving of data packets and the time-varying satellite channels, we formulate the resource allocation problem as a long-term utility function maximization problem. Since the optimization problem is a non-convex mixed integer problem, which cannot be solved using traditional convex optimization tools, we first decouple the original problem into beam scheduling subproblem and joint subchannel and power allocation subproblem. To solve beam scheduling subproblem, two beam scheduling schemes are proposed. Furthermore, three deep reinforcement learning (DRL)-based joint subchannel and power allocation algorithms are proposed to tackle joint subchannel and power allocation subproblem. Numerical results demonstrate the effectiveness of the proposed algorithms.

With its wide range of applications, the Internet shows a future trend towards abundant and diverse data resources with multiple types of identifiers (multi-identifiers). However, the legacy Domain Name System (DNS) in the current TCP/IP network architecture has failed to manage these identifiers due to the centralized security issue. While some decentralized DNS alternatives have been proposed, they also face scalability issues. In this paper, we propose a blockchain-based Hierarchical Multi-Identifier System, named H-MIS, as a DNS alternative. Specially, it realizes optimal decentralization and scalability by introducing the Zero-Knowledge rollup (ZK-rollup) solution to synchronize the upper and lower on-chain identifier data, as well as off-chain associated resource data. Finally, we implement H-MIS on Ethereum and evaluate its performance. The experimental results indicate that compared to the original MIS and Ethereum Name Service (ENS), H-MIS has advantages in such aspects as efficiency, data consumption, and Gas fees.

An efficient Intrusion Detection System (IDS) model is essential for the protection of Unmanned Aerial Vehicles (UAVs) networks against network intrusion. However, when designing IDS models using distributed data collected by UAVs, it is crucial to ensure the security and privacy of the data. Moreover, most IDS models only focus on one-time learning and lack continuous learning capabilities. To address this, we present a Federated Continuous Learning framework with a Stacked Broad Learning System (FCL-SBLS) that utilizes Digital Twin Network (DTN) to enable quick and continuous learning on new data. To enhance the efficiency and quality of the IDS model during training and aggregation, we adopt an asynchronous federated learning architecture. Additionally, we introduce a Deep Deterministic Policy Gradient (DDPG)-based UAV selection scheme assisted by DTN to aid in global IDS model aggregation. This approach ensures that the IDS model can effectively and efficiently learn from distributed data while preserving the privacy and security of the data. The presented algorithm is validated using the CIC-IDS2017 dataset, and the simulation results reveal that our algorithm achieves higher efficiency and accuracy than the existing FL scheme.

The 5G New Radio-based in unlicensed spectrum (NR-U) has been proposed to harmoniously coexist with the Wireless Gigabyte (WiGig) network in the 60 GHz unlicensed spectrum. It employs the directional listen-before-talk (dirLBT) mechanism to improve the throughput of the coexisting NR-U and WiGig networks (CNWNs). In this paper, we are the first to leverage the stochastic geometry to analyze the performance of the large-scale CNWNs. The medium access probabilities (MAPs) of NR-U base station (NBS) and WiGig access point (WAP) are both derived in closed-form. Based on these MAPs, the downlink successful transmission probabilities (STPs) of NR-U and WiGig networks, which is determined by the retaining probability of the serving NBS/WAP and the downlink coverage probability of NR-U/WiGig network, are given in analytical expressions. All these MAPs and STPs are validated by Monte Carlo simulations to verify the correctness of our proposed model. Moreover, the effect of NBS and WAP density on the mean STP of the large-scale CNWNs are analyzed numerically. The results show that the dirLBT adopted by NR-U outperforms omnidirectional LBT in terms of the mean STP, especially in ultra-densely deployed CNWNs scenario. Furthermore, there exists an optimal NBS density to maximize the mean STP. The results indicate that the dirLBT mechanism should be adopted in the densely deployed CNWNs with proper chosen of NBS density.

Edge computing network (ECN), which could process learning tasks at the edge, is considered as a potential solution to release the burden of the cloud. Meanwhile, to protect user privacy, federated learning (FL) is used in the ECN to establish models by multi-party collaborative learning on numbers of edge nodes (ENs). However, due to the frequent data interaction between the cloud server and distributed ENs, the reliability of data transmission and the privacy protection capability of the network cannot be guaranteed. In this paper, a distributed ECN is considered, to improve the learning efficiency in the multi-party FL while ensuring the reliability of ENs, a consortium blockchain enabled asynchronous federated learning (AFLChain) algorithm is proposed, which could dynamically allocate the learning tasks to ENs according to their computing capabilities. Moreover, an entropy weight-based reputation mechanism is introduced for the EN evaluation to further improve the performance of the AFLChain. Finally, the simulation results demonstrate the effectiveness of the proposed algorithms.

Unmanned aerial vehicles (UAVs) could be dispatched to areas of interest and act as intermediate relays to transmit information from areas of interest to the ground data center due to their flexible mobility. The UAV-assisted communication network consists of the aerial subnetwork and the ground subnetwork. The aerial subnetwork is forming by UAVs, which aids the ground subnetwork through air-to-air (A2A) and air-to-ground (A2G) communications link. To collect information with the minimum energy consumption of UAVs, in this paper, we jointly optimize the trajectory, the number and locations of UAVs while considering the coverage of the area. The optimization problem is a mixed integer non-convex problem, we decompose it into two sub-problems and solved separately. Firstly, the UAVs deployment (UD) algorithm is used to determine the number and locations of UAVs with the consideration of the coverage. Secondly, the improved shuffled frog-leaping algorithm (ISFLA) based on the Dubins path is proposed to optimize the trajectory of UAVs with obstacle avoidance. Finally, simulation results demonstrate that the proposed algorithm could achieve superior performance compared with algorithms in the literatures.

Numerous resource-constrained Internet of Things (IoT) devices make the edge IoT consisting of unmanned aerial vehicles (UAVs) vulnerable to network intrusion. Therefore, it is critical to design an effective intrusion detection system (IDS). However, the differences in local data sets among UAVs show small samples and uneven distribution, further reducing the detection accuracy of network intrusion. This article proposes a conditional generative adversarial net (CGAN)-based collaborative intrusion detection algorithm with blockchain-empowered distributed federated learning to solve the above problems. This study introduces long short-term memory (LSTM) into the CGAN training to improve the effect of generative networks. Based on the feature extraction ability of LSTM networks, the generated data with CGAN are used as augmented data and applied in the detection and classification of intrusion data. Distributed federated learning with differential privacy ensures data security and privacy and allows collaborative training of CGAN models using multiple distributed data sets. Blockchain stores and shares the training models to ensure security when the global model’s aggregation and updating. The proposed method has good generalization ability, which can greatly improve the detection of intrusion data.

The edge of the Internet of Things (IoT), which consists of unmanned aerial vehicles (UAVs), is vulnerable to network intrusion because software and wireless connections are used extensively in the IoT. Designing an efficient intrusion detection system (IDS) model is imperative. However, when creating IDS models with distributed data collected by UAVs, it is necessary to take precautions to protect the data’s security and privacy. Furthermore, most of the IDS models are focused on one-time learning but not on continuous learning. To this end, we propose a federated continuous learning framework with a stacked broad learning system (FCL-SBLS) based on the digital twin network (DTN), which can learn and train the IDS model on new data quickly and continuously. In order to improve the efficiency and quality of the IDS model when training and aggregation, we employ an asynchronous federated learning (FL) architecture, and a deep deterministic policy gradient (DDPG)-based UAV selection scheme assisted by DTN is proposed to help the global IDS model aggregation. The presented algorithm is validated using the CIC-IDS2017 data set, and the simulation results reveal that our algorithm achieves higher efficiency and accuracy than the existing FL scheme.

The Age of Information (AoI), which evaluates the information freshness, and the energy efficiency (EE) play key roles in cellular IoT networks. This is due to that outdated data can hardly provide any useful information for delay-sensitive applications and the IoT devices usually have limited battery life. In particular, the AoI can be significantly affected by the transmission latency, which becomes the bottleneck for the AoI performance in ultradensely deployed cellular IoT networks. Moreover, it is desirable for cellular IoT networks to achieve low AoI with high EE. The data compression (DC) can decrease the AoI and improve the EE by reducing the transmission latency. Therefore, in this work, the AoI and EE performance in a large-scale densely deployed uplink cellular IoT network are jointly analyzed with the DC technology. Specifically, the closed-form results of AoI are derived and validated by Monte Carlo simulations. Based on these results, the AoI–EE ratio is defined to evaluate the tradeoff between the AoI and the EE. Equipped with these results, the effects of compression ratio (CR) and status update packet generation rate (SUPGR) on both the AoI and the AoI–EE ratio are analyzed numerically. The results show that by jointly optimizing the CR and SUPGR, the AoI can be decreased by up to 82% and the AoI–EE ratio can be reduced by up to 83% as compared with the case that only adjusts the SUPGR without the DC. It indicates that the DC should be widely adopted in IoT devices, which can improve the information freshness with low-energy consumption, especially in an ultradensely deployed scenario.

Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-preserving distributed machine learning in the Internet of Things (IoT). However, the communication overhead caused by partial model aggregations will increase the model training latency. In this article, a multilayer blockchain-enabled hierarchical FL (HFL) network is proposed for low-latency model training while ensuring data security. Meanwhile, we theoretically analyze the bottleneck of the model accuracy with the total data distance due to the imbalanced data distribution. Moreover, the mathematical expression of the model error with respect to IoT devices (IDs) association and local data distribution is provided, then the upper bound of the model error is represented by the total data distance. To further improve the learning performance, the distance-aware HFL (DAHFL) algorithm is investigated, which optimizes ID association strategy based on dual-distance, and allocates computing and communication resources alternatively. Finally, the working process of the blockchain-enabled HFL system is exhibited by the blockchain simulation platform and the efficiency of the proposed DAHFL algorithm is demonstrated by the simulation results.

Network slicing (NS) provides customized services to users of the Internet of Things (IoT) by creating logical virtual networks, and NS combined with multiaccess edge computing (MEC) can significantly minimize the latency for delay-sensitive service. Therefore, it is important to research how to employ NS to achieve low latency for delay-sensitive service in MEC-enabled IoT. In this article, we propose a paradigm of dynamic cooperative slicing based on the digital twin network (DTN) to achieve low latency for delay-sensitive service. Specifically, we first build a DTN for the MEC-enabled IoT, and build basic models and function models, including prediction and decision making in DTN. Then, we realize dynamic cooperative slicing through the built basic models and function models. Second, with the assistance of the ubiquitous computing resources in MEC-enabled IoT based on DTN, we construct joint optimization problem of communication resources, computing resources, and collaboration proportion with the objective of ensuring low delay of delay-sensitive service while maximizing the long-term utility of operators. Third, considering that the different MEC servers participating in the cooperation in each time slot lead to different action spaces in different time slots, we propose a deep deterministic policy gradient algorithm with variable action space, called VADDPG, which draws on the idea of action masking and introduces the action adjustor to realize the hard control of action space. Finally, a large number of simulations demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of both the long-term utility of operators and the delay obtained by slicing.

As a three-layer association of Internet of Things Equipment (IoTE)–network slicing (NS)–base station (BS) in radio access network (RAN) slicing, handoff control, and resource allocation has become an important but complicated issue. In addition, the centralized controller has a difficult grasping the network situation in real time. In view of this, the problem of handoff control in the RAN slicing is investigated in the digital twin network (DTN), with the goal of maximizing the long-term utility about user satisfaction and handoff cost. Then, an improved algorithm based on the actor–critic framework is suggested, which is called HCRA. Specifically, the actor component contains neural networks for handoff control and an optimizer for resource allocation, and then the critic component evaluates the handoff and resource allocation actions of the actor component to guide the optimization of actions in the actor component. The simulation results show that HCRA can obtain better performance than benchmark algorithms.

The Internet of Things (IoT) enables intelligent services varying with the complex and realtime environment to achieve network benefits, where network function virtualization (NFV) can dynamically provide virtualized network functions (VNFs) for IoT devices. In the NFV-enabled IoT architecture, a service function chain (SFC) consists of an ordered set of VNFs. However, the energy consumption of the VNF migration and SFC reconfiguration is one major issue owing to the dynamic characteristic of the IoT network. In this article, we propose a new paradigm digital twin (DT) to create the virtual twin of physical objects in the IoT network, then, we formalize the problem as a mathematical model, which aims to minimize the energy consumption. To this end, we prove this problem is NP-hard and propose an algorithm bidirectional gated recurrent unit (Bi-GRU) based on federated learning to predict the resource requirement. Further more, according to the prediction result, which utilizing the deep reinforcement learning (DRL) algorithm for decision making of the VNF migration. Simulation results show that our proposed method can effectively reduce the number of VNFs to be migrated and economize the energy consumption of the DT IoT network.

Massive and diversified machine type communication (MTC) service is one of the development trends of MTC in Internet of Things (IoT). Meanwhile, realizing network functions virtualization (NFV) is inseparable from reasonable virtual network function (VNF) scheduling and resource allocation. For VNF scheduling and resource allocation of MTC services, recently, deep reinforcement learning (DRL) has become one of the feasible solutions. However, existing DRL solutions have problems of inapplicability to the environment with both discrete and continuous variables, long training, time and nonequilibrium resource allocation. In this article, we first model the end-to-end (E2E) VNF scheduling and resource allocation of core network nodes, links, and access network subcarriers with different strategies, respectively, and propose a compound variable optimization problem aiming at maximizing the net income of the network provider. Then, we propose the mapping scheme of the absolute value of the signum function (ASgn mapping scheme) to simplify the compound variables into continuous variables of the optimization problem, so that the DRL algorithm is applicable. Moreover, we propose a model paralleling multiagent twin delayed deep deterministic (MPMA-TD3) policy gradient algorithm to handle massive services, reduce training time, and action space of agents. Finally, we improve the MPMA-TD3 algorithm to handle diversified services, solve the problem of nonequilibrium resources allocation, and realize the reasonable resource allocation for each service. Simulation results show that the proposed algorithms are better than other algorithms in reward, delay, cost, and training time for massive services. Further, the Improved MPMA-TD3 algorithm has the best service equilibrating ability.

Personalization is one of the primary emerging trends in Industry 4.0 and Beyond. Highly personalized services will present a significant challenge to the existing algorithms for network slicing (NS) and resource allocation, leading to issues, such as nonequilibratory resource allocation, in which some services are sacrificed for the maximum total reward of the algorithm, excessive cost, and slow algorithm convergence. A digital twin network (DTN) is offered as a novel solution to the challenges listed above. By integrating the DTN and IIoT NS, we propose a DTN-assisted industry Internet of Things NS (DTN-IIoT NS) architecture for personalized IIoT services in Industry 4.0 and Beyond. The DTN-IIoT NS architecture consists of three layers, three modules, and two closed loops. On the basis of the aforementioned architecture, we focus on the resource allocation process in DTN-IIoT NS, model the DT-assisted resource allocation for highly personalized IIoT services, propose the service equilibrium rate, and formulate the optimization problem aiming at maximizing the equilibrium rate weighted net profit of network providers. Then, we propose a dual-channel weighted (DCW) Critic network for service equilibrium in DTN-IIoT NS resource allocation and the matching Improved prioritized experience replay (PER) to enhance convergent speed. In addition, we present a distributed DT-assisted DCW-PER multiagent deep deterministic policy gradient (PER-DCW MADDPG) algorithm for the resource allocation process in DTN-IIoT NS. Simulation results indicate that the PER-DCW MADDPG algorithm can produce a better service equilibrium and accelerate the convergence speed of the algorithm.

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in the user equipment (UE) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirements. Data compression (DC) has been considered as one of the promising techniques to reduce the transmission latency. By compressing the computation tasks before transmitting, the transmission delay is reduced due to the shrink transmitted data size, and the original computing task can be retrieved by employing data decompressing (DD) at the edge nodes or the centre cloud. Nevertheless, the DC and DD incorporate extra processing latency. For the F-RAN system, the latency performance has not been investigated considering the DC and DD processes. Therefore, in this work, the successful data compression probability (SDCP), i.e., the probability of the task execution latency being smaller than a target latency and the signal to interference ratio (SIR) of the received signal being higher than a threshold, is defined to analyse the latency performance of the DC-enabled F-RAN. Moreover, to analyse the impact of compression offloading ratio (COR), which determines the proportion of tasks being compressed at the edge, on the SDCP of the F-RAN, a novel hybrid compression mode is proposed based on the queueing theory. Based on this, the closed-form result of SDCP in the large-scale DC-enabled F-RAN is derived by combining the Matern cluster process and M/G/1 queueing model, and validated by the Monte-Carlo simulation. Based on the derived SDCP results, the effects of COR on the SDCP is analysed numerically. The results show that the SDCP with the optimal COR can be enhanced with a maximum value of 0.3 and 0.55 as compared with the cases of compressing all computing tasks at the edge and at the UE, respectively. Moreover, for the system requiring the minimal latency, the proposed hybrid compression mode can alleviate the requirement on the backhaul capacity.

Unmanned aerial vehicles (UAVs) have been regarded as an efficient approach for collecting data in wireless sensor networks (WSNs), benefited from their mobility and flexibility. In this work, we investigate the data collection problem in UAV-assisted WSNs. In order to improve data collection efficiency, we first propose a multi-scenario parallel data collection scheme which allows data packets being transmitted through various modes/links simultaneously. Then, addressing the importance of completing data collection within a short time duration, we formulate a constrained optimization problem which minimizes the data collection time of the sensor nodes (SNs) by jointly designing UAV flight trajectory, cluster head mode selection, SN clustering strategy and UAV velocity. To resolve the optimization problem, we first consider the data transmission performance between SNs and present an SN clustering scheme based on a modified K-means algorithm. Given the clustering strategy, the optimization problem is then converted into three sub-problems, i.e., CH mode selection, UAV trajectory design, and flight velocity optimization. Firstly, jointly considering the data collection time of the cluster heads in various transmission modes and the spectrum resources of the sink node, we propose a greedy method-based CH mode selection scheme. Then, we map the UAV trajectory optimization problem as a traveling salesman problem and propose a simulated annealing-based algorithm to determine the flight trajectory for the UAV. Finally, by applying discrete time segment scheme, the UAV velocity optimization subproblem is transformed into a sequence of convex flight time minimization problems and a segment optimization-based flight velocity control strategy is presented. Numerical results reveal that the proposed data collection algorithm can achieve$25{\mathrm{\% }}$and$12{\mathrm{\% }}$performance gains comparing to the existing algorithms and the benchmark scheme, respectively.

Virtualized network slicing allows a multitude of logical networks to be created on a common substrate infrastructure to support diverse services. A virtualized network slice is a logical combination of multiple virtual network functions, which run on virtual machines (VMs) as software applications by virtualization techniques. As the performance of network slices hinges on the normal running of VMs, detecting and analyzing anomalies in VMs are critical. Based on the three-tier management framework of virtualized network slicing, we first develop a federated learning (FL) based three-tier distributed VM anomaly detection framework, which enables distributed network slice managers to collaboratively train a global VM anomaly detection model while keeping metrics data locally. The high-dimensional, imbalanced, and distributed data features in virtualized network slicing scenarios invalidate the existing anomaly detection models. Considering the powerful ability of generative adversarial network (GAN) in capturing the distribution from complex data, we design a new multi-discriminator Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) model to learn the normal data distribution from high-dimensional resource metrics datasets that are spread on multiple VM monitors. The multi-discriminator BiWGAN-GP model can be trained over distributed data sources, which avoids high communication and computation overhead caused by the centralized collection and processing of local data. We define an anomaly score as the discriminant criterion to quantify the deviation of new metrics data from the learned normal distribution to detect abnormal behaviors arising in VMs. The efficiency and effectiveness of the proposed collaborative anomaly detection algorithm are validated through extensive experimental evaluation on a real-world dataset.

Network slicing has become an essential technology for the future network. Obviously, it will play an important role in satellite networks as well. To address that quality of service (QoS) may be severely affected by embedding satellite virtual networks (SVNs), we propose a method for SVN admission control that can effectively guarantee the QoS of network slices by admitting SVNs embedded in the physical satellite networks. Specifically, firstly, we propose a two-stage SVN embedding mechanism that decouples short-term resource allocation from long-term admission control and resource leasing. Then, we consider the case of uncertain system capacity due to the highly dynamic nature of the satellite networks topology, and model the admission control problem as a robust optimization problem. The robust problem is transformed into a convex counterpart by using the Bernstein approximation. Finally, we solve the resource allocation problem by converting it into a convex problem. The simulation results show the effectiveness of the proposed method.

The rapid development and wide application of wireless communication technologies advance the integration of heterogeneous access networks. While the heterogeneous networks are expected to offer enhanced data transmission services for mobile terminals (MTs), the heterogeneous characteristics of access technologies, the diverse requirements of user services and various features of user devices pose challenges to network access and handover strategy. In this paper, the network access and handover problem in dynamic heterogeneous network scenarios is investigated. To tackle the variation of performance metrics in different access networks, we define instantaneous quality of service (QoS) metrics. Then, system utility function is introduced to characterize user service experience on access networks. Specifically, by jointly considering network performance, service characteristics and user requirement, system utility function is defined as the weighted sum of user QoS, queue status and handover cost. Aiming to maximize the long-term average utility, network access and handover problem is formulated as a constrained stochastic optimization model. To solve the optimization problem, we regard the network access and handover procedure as a Markov decision process (MDP), and propose a deep Q-network (DQN)-based approach to determine the optimal strategy. Simulation results show that compared to the reference algorithms, the proposed algorithm offers better performance.

In fog network, device-to-device (D2D) sharing is an important way to obtain data. However, due to an untrusted environment, it is difficult for a device to assess the reliability of the received data. What’s more, devices may be reluctant to share data because of selfish, resulting in data supply and demand imbalances. In this regard, a data sharing scheme assisted by blockchain and matching algorithm is proposed. In order to ensure the authenticity of the data, the Bayesian inference model is employed to predict quality of the data, and a multi-factor data evaluation method is presented to make accurate judgments. Furthermore, different utility functions for data requesters and providers are defined, and a two-way matching game is introduced to balance of data supply and demand. To reduce the blockchain consensus delay and ensure the activeness of fog nodes, a practical byzantine fault tolerates (PBFT) consensus mechanism based on the frequency of interaction is investigated. The simulation results verify the effectiveness of the algorithm. The proposed data sharing scheme promotes the interaction of information in the fog computing network.

With the development of satellite communications, satellites have been equipped with edge computing capability and edge caching capability, and these advancements can further drive the development of video transmission mechanisms. In this paper, we propose to utilize in-network caching and computing of software-defined space-air-ground integrated networks to improve the quality of video experience for users. The optimization problem can be viewed as a coupling of three parts, namely, the video resolution adaptation problem, the computing resource scheduling problem, and the bandwidth provision problem. To achieve the solution of the problem effectively in practice, we deploy the alternating direction method of multipliers to decouple the three sets of variables. Numerical results demonstrate the effectiveness of the proposed scheme.

A hybrid or coexisting orthogonal frequency-division multiple access (OFDMA) and nonorthogonal multiple access (NOMA) scheme is a promising approach to greatly enhance network capacity and reduce the interference in machine-to-machine (M2M) communication networks. In this article, we study the power allocation and access mode selection problem of machine-type communication devices (MTCDs), which are allowed to transmit their data packets to the base station (BS) in direct transmission mode (DTM) or cluster head forwarding mode (CHFM). Considering the transmit power optimization and data rate maximization of the MTCDs, we formulate the power allocation and access mode selection problem as a sum-rate maximization problem. Since the original maximization problem is a nonlinear fractional problem that cannot be solved conveniently, we transform the optimization problem into two subproblems, i.e., power allocation subproblem and access mode selection subproblem. The power allocation subproblem is solved for both OFDMA and NOMA schemes by applying the Lagrange dual method. To solve the access mode selection subproblem, we further divide the subproblem into DTM subproblem and CHFM subproblem and solve these subproblems successively. In particular, for the solution of the CHFM subproblem, we first propose a greedy method-based algorithm, and then, to tackle the issue of high computational complexity, we present a low complexity heuristic algorithm. In the end, we present simulation results to demonstrate the effectiveness of the proposed algorithms.

The satellite–terrestrial integrated network (STIN) has been envisioned as an emerging architecture to provide global anytime anywhere network access, and satisfy transmission requirements of high-capacity backhaul data. However, the integration of satellite and terrestrial networks will aggravate the diversity of base stations (BSs) in backhaul delay and capacity, as well as coverage area, which makes it difficult for users with diverse requirements to access the most suitable service BS. As an effort to address the above problems, a distributed user association with grouping (DUAG) mechanism is proposed via the interaction between BSs and users to maximize the sum rate and balance the load of STIN while meeting the user’s demand by user grouping. First, the transmission characteristics of terrestrial and backhaul links are analyzed after constructing a STIN model, which consists of satellite, three types of BSs, and the variety of intelligent terminals. Then, the user association problems are formulated to maximize the sum rate and balance the load of STIN via jointly considering the backhaul capacity of BSs and mobility and delay of users. Meanwhile, the DUAG mechanism is proposed to associate users with the most suitable service BSs. In DUAG, a greedy-based user association algorithm with user grouping is developed for maximizing the sum rate via giving priority to users with high data rate, and a matching algorithm with user grouping is designed for balancing the load by means of performing multiple iterations between users and BSs. Simulation results demonstrate that the proposed DUAG can maximize the sum rate and balance the load of STIN while guaranteeing the delay demand of user with the increase of user density.

Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs. Besides, there are two types of IDs, namely, requested-IDs (R-IDs), which has task offloading requirement, and free-IDs (F-IDs), which could offload tasks for R-IDs with idle computation resources. Two offloading links are considered: 1) the device-to-device (D2D) link and 2) the ground-to-air (G2A) link, which are responsible for both the uplink and downlink offloading procedure. To minimize the total network overhead, we jointly optimize the UAV trajectory, transmission power, and computation offload radios, while satisfying Quality-of-Service (QoS) requirements of R-IDs. The optimization problem is nonconvex, and the UAV-assisted task offloading optimization algorithm is proposed to obtain the local optimal solutions, which decomposes the original problem into two parallel subproblems and solved alternately. Finally, simulation results demonstrate that the proposed algorithm could achieve superior performance in terms of the network overhead compared with algorithms in the literature.

Emerging digital twin (DT) and mobile-edge computing (MEC) are crucial for enabling the rapid development of 6G. However, the existing works ignore the edge collaboration, which can provide the system with additional performance gain. In this article, we study the problem of mobile users (MUs) intelligently offloading tasks to cooperative mobile-edge servers (MESs) with the assistance of DT. Specifically, a DT-assisted task offloading scheme (DTTOS) that consists of the selection of MESs and intelligent task offloading is proposed. Channel state information (CSI) and blockchain are employed to implement the selection of MESs. Then, we present a solution to enable MU’s task offloading that is modeled as a Markov decision process (MDP) in an intelligent way. After this, a mathematical optimization model aiming at decreasing power and time overhead is formulated. In view of the complexity, it is decomposed into two suboptimization models and solved by the decision tree algorithm (DTA) and double deep-$Q$-learning (DDQN), respectively. Simulations are conducted to prove the superiority of the proposed scheme in terms of data security assurance and network performance improvement.

Applications in the Internet of Vehicles (IoV) are usually accompanied by ultralow network response latency requirement. A promising approach to meet this demand is combining the IoV with mobile edge computing and enabling edge devices to share their communication, computation, and caching (3C) resources via edge intelligent cooperation. However, the allocation of 3C resources supported by artificial intelligence (AI) demands a huge number of training data and strong computing ability which is impossible to achieve on resource-limited on board unit (OBU) or road side unit (RSU). In this article, we propose a digital twin (DT) supported edge intelligent cooperation scheme, which empowers the optimal 3C resource allocation and edge intelligent cooperation possible. We focus on the response delay minimization in order to meet the requirement of latency-sensitive applications in the IoV. Specifically, mathematical expressions of the network response time are formulated according to modeling the workflow of the edge server as an M/M/1/N/FCFS queuing process. Especially, we conduct a detailed analysis of the deviations in 3C resource between the physical world and the DT space, based on which we further discuss the impact of these deviations in offloading decision. Furthermore, a mathematical optimization model aiming at minimizing the latency is formulated. In view of its complexity, we apply a deep deterministic policy gradient algorithm to solve it by modeling the cooperation process between edge nodes as a Markov decision process. Finally, we carry out simulations to demonstrate that our algorithm outperforms the existing schemes in terms of network response latency.

Missing item event detection is one of the most important radio-frequency identification (RFID)-enabled functions. Yet it is largely unaddressed how to fast and reliably detect missing item event in multitagged RFID systems where multiple tags are tagged on one item. The canonical methods can only solve tag-level detection problem where each item is associated with one tag, and applying them to detect the missing multitagged items would falsely alarm and is time inefficient. To bridge the gap, this article formulates and analyzes the missing multitagged item detection problem. Our key idea is to search the proper seeds so that the reader only needs to probe a subset of the tags each being selected from different items instead of the entire tag set for the missing item detection. By employing the computation-communication tradeoffs, we design two protocols named M2ID and M2ID+ that classifies the tags before the segmentation compared to the former to improve time efficiency. With the derived optimum parameters, our protocols can achieve up to$4\times$performance gain in terms of time efficiency compared with the state-of-the-art solution.

Network Function Virtualization (NFV) is a promising paradigm that enables the employment of novel service types with lower deployment cost and faster time-to-value, but it introduces new fault management problems and challenges. In NFV environment, anomalies occurred in virtual machines (VMs) can be caused by faulty components of their hosting servers or anomaly propagation from other ones. If the performance of VMs degrades, we need to find out the root causes accurately, i.e., locate the exact faulty components, to recover the networks as soon as possible. In this paper, we first use digital twin to establish a virtual instance of the physical network to capture the real-time anomaly-fault dependency relationship. When the network environment changes, transfer learning is leveraged to utilize the learned knowledge of dependency relationship in historical periods to avoid huge time and computation cost of learning from scratch. Assisted by the learned dependency relationship, a dynamic set-covering (DSC) based root caused analysis problem is formulated and modeled with a set of parallel hidden Markov models to capture the dynamics of component states, which can best explain the sequence of anomalous VMs. We use alternating direction method of multipliers to decompose the DSC problem into a set of independent sub-problems and solve it in a distributed fashion. Since the state variables of each component in the DSC problem are coupled between any two successive time epochs, each sub-problem is solved by the Viterbi decoding and an incremental function is designed to construct the feasible solution that covers all anomalous VMs. Simulation results show the availability and superiority of the digital-twin assisted root cause analysis algorithm for NFV environment.

In Low Earth Orbit (LEO) satellites, which run in polar orbit, the area of overlap among beams becomes wider as the latitude of satellites increases, which leads to intolerable interference and extra energy consumption. To minimize the onboard power with QoS requirements, we propose an energy optimization model with considering power allocation, user association and dynamic beam ON/OFF operation jointly. Moreover, the frequent beam ON/OFF operations lead to the large number of user handovers, so handover cost is also considered in the model. The original problem is decomposed into two levels due to the high coupling of variables and the successive convex approximation is employed. A low complexity greedy ON/OFF iteration is proposed to adapt to dynamic topology of LEO. Simulation results show that the proposed scheme can effectively reduce the system energy consumption.

In this paper, we propose a Digital Twin (DT) Supported Resource Allocation Scheme (DTS-RAS), which empowers the intelligent edge cooperation in the Internet of Vehicles (IoV) environment possible. We focus on the latency minimization under the DT-IoV framework. Specifically, we formulate the mathematical expression for the response time of vehicle offloading tasks to cooperative edge nodes according to modeling the edge server as a M/M/1/N queen. Then, we construct the optimization model aiming at reducing the response time. In view of the complexity, we apply a Double Deep Q-learning Network (DDQN) to training the edge server to get an optimal allocation action by modeling the cooperation process as an MDP. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of execution latency.

As the network slicing is one of the critical enablers in communication networks, one anomalous physical node (PN) in substrate networks that carries multiple virtual network elements can cause significant performance degradation of multiple network slices. To recover the substrate networks from anomaly within a short time, rapid and accurate identification of whether or not the anomaly exists in PNs is vital. Online anomaly detection methods that can analyze system data in real-time are preferred. Besides, as virtual nodes mapped to PNs are scattered in multiple slices, the distributed detection modes are required to preserve the data privacy of different slices. According to those requirements, we propose a distributed online PN anomaly detection algorithm based on a decentralized one-class support vector machine (OCSVM), which is realized through analyzing real-time measurements of virtual nodes mapped to PNs in a distributed manner. Specifically, to decouple the OCSVM objective function, we transform the original problem to a group of decentralized quadratic programming problems by introducing the consensus constraints. The alternating direction method of multipliers is adopted to achieve the solution for the distributed online PN anomaly detection. The simulation results on the real-world network dataset show the effectiveness and superiority of the proposed distributed online anomaly detection algorithm.

Network Function Virtualization (NFV) enables the employment of novel service types with lower deployment cost and faster time-to-value, but it introduces new fault management problems and challenges. Anomalies of virtual machines (VMs) can be caused by faulty components of their located physical servers or anomaly propagation from other ones. If the data patterns of VMs are detected as anomalous, we need to locate the root causes precisely to recover the networks as soon as possible. In this paper, we first introduce digital twin to capture the real-time anomaly-fault dependency matrix for the networks. Assisted by the dependency matrix, a dynamic set-covering (DSC) problem is formulated and modeled with a set of parallel hidden Markov models to find a minimal set of faulty components at each observation epoch, which can cover all anomalous VMs. We introduce alternating direction method of multipliers to decompose the DSC problem into a set of independent sub-problems and solve it in a distributed fashion. Simulation results show the availability and superiority of the proposed digital-twin assisted root cause analysis algorithm for NFV environment.

In this paper, a hybrid satellite-terrestrial relay network (HSTRN) is considered, which is composed of one satellite, a relay ground station (RGS), a destination ground station (DGS) and a number of cellular users (CUs). We model the satellite links from the satellite to the DGS/RGS as shadowed-Rician (SR) fading channel, and the links between any two terrestrial devices as Nakagami-m fading channel. Considering the satellite links share the same spectrum with the terrestrial links, we analyze the transmission performance of both the direct transmission link as well as the relay forward link. Specifically, we derive the statistic characteristics of the signal to interference plus noise ratio (SINR) of the links, derive the outage probability (OP) of both links and the amount of fading (AoF) of the relay link. Simulation results demonstrate the effectiveness of the analysis.

The rapid growth of Internet of Things (IoT) applications poses a great challenge to the computation capability of smart mobile equipments (SMEs). Fog Computing, as a promising technology, provides fast computing services for resource-limited SMEs in various applications. In this paper, we consider a blockchain-based fog computing network consisting of SMEs, fog nodes (FNs) and the cloud server. To optimize the delay and energy consumption of processing computation-intensive tasks, two offloading models are introduced, namely, task offloading to the device-to-device (D2D) cooperation group and to a nearby FN. Additionally, the blockchain technology is enabled to prevent malicious nodes from modifying with transaction information by maintaining a continuous tamper-proof ledger database. To reduce the delay and energy consumption of the traditional consensus mechanism, we propose the voting-based delegated proof of stake consensus mechanism, in which the FNs with the top half of votes will form a verification set, and the FNs will take turns being the manager to generate new blocks. Furthermore, to minimize the network cost, we jointly optimize task offloading decision, transmission rata allocation and computing resource allocation under various constraints. Finally, the effectiveness of the proposed scheme is demonstrated.

Blockchain technology is used to improve the security of users data in the network. However, the traditional blockchain structure is not suitable for the fog network due to the low throughput and scalability limitations. To solve the above issues, in this paper, we propose a sharding blockchain to improve the security of the fog network, while increasing the throughput. Sharding blockchain will divide the network into several shards, and each of them could process the transactions parallelly. In addition, the normalized entropy of the fog network could be calculated by the main opinion and secondary opinion in the consensus result. Then, the main-chain layer could calculate the probability of malicious fog nodes (FN) in the network and the maximum number of shards. Finally, the S-type fog nodes assignment algorithm (S-NA) is proposed to optimize the association between shards and FNs, which combines the greedy algorithm and the max-min fair algorithm. Simulation results verify the efficiency of the proposed S-NA algorithm.

In mobile computing scenarios, federation learning allows users to jointly train global models in a decentralized manner without exposing private data. However, due to the heterogeneity of the network and devices, the traditional global model often fails to fit the user data distribution, which is inconsistent with the primary condition of federation learning, resulting in accuracy decreasing of global models. Besides, the security of federated learning is decreasing with the increase of malicious attacks. To address the aforementioned issues, in this paper, we explore the cosine similarity of model gradients and design a clustered mechanism to improve learning efficiency. Furthermore, we combine the clustered federated learning with the blockchain-supported fog computing networks, which could verify local models uploaded by users and generate the traceable global models to improve the learning efficiency. Finally, we conduct experiments on several frameworks with the real-world dataset FEMNIST, and the experimental results demonstrate the efficiency and robustness of the blockchain-enabled clustered federated learning framework.

In the vehicular ad hoc networks (VANETs), vehicles share content with other vehicles and roadside units (RSU) to improve traffic efficiency. However, the vehicles and RSUs are not always credible. If they have malicious behavior, sharing false information put lives in danger. To address these security challenges, we propose a content sharing management method based on blockchain in VANETs. Specifically, we propose a hybrid trust model to evaluate the credibility of content based on the vehicle entity and interactive data. We deploy practical Byzantine fault tolerates (PBFT) consensus protocol based on the interaction frequency between RSUs and vehicles. The higher the interaction frequency, the more likely the RSU is to gain the right to package the block. In this way, RSUs and vehicles actively participate in the network, and achieve the content sharing honestly and effectively. We conduct extensive experiments, which demonstrate the implementation feasibility of proposed mechanisms.

As a novel network paradigm, software-defined networking (SDN) is capable of simplifying network management and offering flexible support to various user services. In order to meet the rapidly increasing transmission demands of SDN switches, the controller deployment strategy in an SDN scenario should be designed. In this paper, we investigate the capacitated controller deployment problem for SDN. Consider the signaling transmission and processing performance of switches and address the worst-case performance, we define network response time (NRT) as the maximum control plane response time of switches. Then aiming to achieve the tradeoff between NRT and the cost of controllers, we introduce the concept of network cost which is defined as the weighted sum of NRT and controller cost. The capacitated controller deployment problem is formulated as a constrained network cost minimization problem. To solve the optimization problem, we propose a two-stage heuristic algorithm, which first tackles the controller deployment subproblem under the unlimited capacity constraint, and then solves controller-type matching subproblem. Specifically, during the first stage, a minimum eccentricity-based controller deployment algorithm is designed to determine the number and location of controllers as well as the association strategy between controllers and switches. During the second stage, a greedy method-based controller-type matching strategy is proposed to determine the types of deployed controllers. Extensive simulations are performed and the results certify the effectiveness of the proposed algorithm.

In Satellite Internet of Things (SIoT), satellite constellation design is used to satisfy more communication demands with fewer satellites by optimizing satellite orbits in specific areas. However, the diversity of user demands and satellite resource poses a great challenge in evaluating the Quality of Experience (QoE) of the satellite constellation. In this article, a QoE-aware satellite constellation design scheme is proposed to enhance the user satisfaction by constructing QoE factors. First, an SIoT network model is established with low-Earth-orbit (LEO) satellites and ground Internet of Things (IoT) devices, and the problem is formulated to calculate the QoE for different areas after analyzing the dynamic network topology. Then, the QoE factors are defined to assess QoE by taking into account the coverage performance, communication fluency, regional demand capacity, and profitability. Following that, an intelligent optimization algorithm named multilayer tabu search (MLTS) is designed to obtain reasonable satellite orbits with the best QoE, which provides orbit parameters for satellite constellation configuration. The simulation results demonstrate that the designed satellite constellation can effectively improve the users QoE by optimizing the various QoE factors.

With the emergence of a large number of computational resource-intensive applications and various content delivery services, there is an explosion of data growth in the Internet of Vehicles (IoV). To improve the transmission performance of the IoV, caching content on the edge of the network is considered as a potential solution to reduce the content transmission delay. In this article, we investigate the content caching decisions optimization method in the IoV to minimize the content fetching delay for vehicles, which is based on the vehicle-to-vehicle (V2V) collaboration. A delay-aware content caching (DCC) algorithm in the IoV is proposed, which consists of vehicle associations, content caching, and precaching decisions optimization. First, a delay-aware vehicle associations (DVAs) algorithm is proposed to optimize the vehicle associations. Consequently, based on the vehicle associations results, the content caching decisions are optimized in two network scenarios according to the existence of the handover vehicles. Finally, a practical scenario of Shanghai with time-varying traffic flow is used for simulations and the effectiveness of the proposed DCC algorithm is verified.

Recently, software-defined Industrial Internet of Things (SDIIoT), the integration of software-defined networking (SDN) and Industrial Internet of Things (IIoT), has emerged. It is perceived as an effective way to manage IIoT dynamically. Aiming to improve scalability and flexibility of SDIIoT, multi-SDN has been applied to form a physically distributed control plane to handle the large amount of data generated by industrial devices. However, as the core of multi-SDN, reaching consensus among multiple SDN controllers is a thorny issue. To meet the required design principle, this paper proposes a blockchain-enabled distributed architecture with SDIIoT to synchronize local views between distinct SDN controllers and finally reach the consensus of global view. On the other hand, both the cryptographic operations of blockchain and the noncryptographic computational tasks have access to the same computational resource pool of mobile edge cloud (MEC). In order to simultaneously optimize the throughput of blockchain and the energy consumption caused by computing, we adaptively allocate computational resources and the block size by jointly considering the trust features of SDN controllers and the resource requirements of non-cryptographic operations. To implement the truly distributed manner of blockchain, we describe our problem as a partially observable Markov decision process (POMDP) and propose a novel deep recurrent Q-network (DRQN) approach to solve it. In the simulation results, we compare two different protocols of blockchain and show the effectiveness of our scheme in either of them.

Fog computing is an advanced technique to enhance the Quality of Service (QoS), decrease network latency and energy consumption for Internet-of-Things devices (IDs). In this article, to minimize the overhead of the fog computing network, including the task process delay and energy consumption, while ensuring multiply QoS requirements of different types of IDs, we propose a QoS-aware resource allocation scheme, which jointly considers the association between fog nodes (FNs) and IDs, transmission and computing resource allocation to optimize the offloading decisions while minimizing the network overhead. First, an analytic hierarchy process-based evaluation framework is established to find the preference of QoS parameters and the priority of different types of ID tasks. Second, we introduce a resource block (RB) allocation algorithm to allocate RBs to IDs based on the IDs priority, satisfaction degree, and the quality of RBs. Moreover, a QoS-aware bilateral matching game is introduced to optimize the association between FNs and IDs. Finally, the offloading decisions are based on the previous steps to minimize the network overhead. The simulation results demonstrate that the proposed scheme could efficiently ensure the loading balance of the network, improve the RB utilization, and reduce the network overhead.

Recently, a fog computing network that widely deploys fog nodes (FNs) at the edge of the network has been able to provide better communication performance and powerful computation support to the resource-limited Internet-of-Things (IoT) devices. In this article, we analyze the energy-efficient (EE) resource allocation problem in fog computing networks with the candidate FNs mechanism to ensure the network loading balance under the transmission performance constraints. In the scenario, the associated computation capability allocated to IoT devices from FNs is related to the historical energy consumption and the current energy consumption. The FN that reports nonzero computation capability is considered as the candidate FN and included in the candidate set. Moreover, a candidate FN-based EE resource allocation (CF-EE) algorithm is proposed to maximize network EE, which is converted into the Lyapunov optimization for each time slot. The optimal resource allocation can be obtained by minimizing the upper bound of the Lyapunov drift function and the penalty term to guarantee the loading balance and network stability. Finally, the optimization problem is decomposed into two suboptimization problems: 1) transmission resource allocation optimization and 2) power allocation optimization, and solved separately. The simulation results demonstrate that the proposed CF-EE algorithm can achieve a considerable performance improvement compared with algorithms in the literature.

Recently, software-defined Industrial Internet of Things (SDIIoT), the integration of software-defined networking (SDN) and Industrial Internet of Things (IIoT), has emerged. It is perceived as an effective way to manage IIoT dynamically. Aiming to improve the scalability and flexibility of SDIIoT, multi-SDN has been applied to form a physically distributed control plane to handle a large amount of data generated by industrial devices. However, as the core of multi-SDN, reaching consensus among multiple SDN controllers is a thorny issue. To meet the required design principle, this article proposes a blockchain-enabled distributed SDIIoT to synchronize local views between distinct SDN controllers and finally reach the consensus of the global view. On the other hand, both the cryptographic operations of blockchain and the noncryptographic tasks have access to the same computational resource pool of mobile edge cloud (MEC). In order to optimize the system energy efficiency, we adaptively allocate computational resources and the batch size of the block by jointly considering the trust features of SDN controllers and the resource requirements of noncryptographic operations. To implement the truly distributed manner of blockchain, we describe our problem as a partially observable Markov decision process (POMDP) and propose a novel deep reinforcement learning (DRL) approach to solve it. In the simulation results, we compare three different protocols of blockchain and show the effectiveness of our scheme in each of them.

To overcome the drawbacks of traditional Internet architectures, software-defined networking (SDN) technology has been proposed, which is expected to dramatically simplify network control processes and enable the convenient deployment of sophisticated network functions. To achieve highly efficient resource utilization in SDN and offer users with diverse service requirements, virtual network embedding (VNE), which maps various virtual network requests of users to a given substrate network, should be conducted. In this paper, we study the VNE problem in SDN where the substrate SDN switches and links may be subject to malicious attacks. We first propose a hierarchical virtualization-enabled SDN architecture based on which the VNE strategy can be designed. Then, stressing the importance of network load and reliability of the substrate network, we formulate the VNE problem of SDN as a multi-objective optimization problem which jointly minimizes network load and maximizes embedding reliability under the constraints of virtual network requirements and the resource characteristics of substrate network. As the formulated optimization problem is a complicated multi-objective optimization problem which cannot be solved conveniently, we apply the ideal point method. In particular, we first propose virtual node embedding sub-algorithm and virtual link embedding sub-algorithm to determine the locally optimal solution to the two subproblems, i.e., network load minimization subproblem and embedding reliability maximization subproblem. Then, examining the distance between the feasible solutions and the locally optimal solutions, we formulate a single-objective optimization problem and solve the problem to obtain the global VNE strategy by applying discrete particle swarm optimization (DPSO) algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm.

Both mobile edge cloud (MEC) and software-defined networking (SDN) are technologies for next generation mobile networks. In this paper, we propose to simultaneously optimize energy consumption and quality of experience (QoE) metrics in video streaming over software-defined mobile networks (SDMN) combined with MEC. Specifically, we propose a novel mechanism to jointly consider buffer dynamics, video quality adaption, edge caching, video transcoding and transmission. First, we assume that the time-varying channel is a discrete-time Markov chain (DTMC). Then, based on this assumption, we formulate two optimization problems which can be depicted as a constrained Markov decision process (CMDP) and a Markov decision process (MDP). Then, we transform the CMDP problem into regular MDP by deploying Lyapunov technique. We utilize asynchronous advantage actor-critic (A3C) algorithm, one of the model-free deep reinforcement learning (DRL) methods, to solve the corresponding MDP issues. Simulation results are presented to show that the proposed scheme can achieve the goal of energy saving and QoE enhancement with the corresponding constraints satisfied.

A vehicular cloud (VC) can reduce latency and improve resource utilization of the Internet of vehicles by effectively using the underutilized computing resources of nearby vehicles. Although the task offloading of the VC enhances road safety and traffic management on the Internet of vehicles and meets the low-latency requirements for driving safety services on the Internet of vehicles business, there are still some key challenges such as the resource allocation mechanism of differentiated services (DiffServ) and task offloading mechanism of improving user experience. To address these issues, we study the task offloading and resource allocation strategy of the VC system where tasks generated by vehicles can be offloaded and executed cooperatively by vehicles in VC. Specifically, the computing task is further divided into independent subtasks and executed in different vehicles in VC to maximize the offloading utility. Considering the mobility of vehicles, the deadline of tasks, and the limited computing resources, we propose the optimization problem of task offloading in the VC system in the cause of improved user experience. To characterize the difference in service requirements resulting from the diversity of tasks, a DiffServ model focusing on the pricing of a task is utilized. The initial pricing of a task is tailored by the characteristics of the task and the uniqueness of the network status. In this model, tasks are sorted and processed in order according to task pricing, so as to optimize resource allocation. Numerical results show that the proposed scheme can effectively increase the resource utilization and task completion ratio.

Both mobile edge cloud (MEC) and software-defined networking (SDN) are technologies for next generation mobile networks. In this paper, we simultaneously optimize energy consumption and quality of experience (QoE) in video streaming over software-defined mobile networks (SDMN) with MEC. Specifically, we propose to jointly consider buffer dynamics, video quality adaption, edge caching, video transcoding and transmission. We formulate two optimization problems which can be depicted as a constrained Markov decision process (CMDP) and a Markov decision process (MDP). Then we transform the CMDP problem into regular MDP by deploying Lyapunov technique. We utilize asynchronous advantage actor-critic (A3C) algorithm, one of the deep reinforcement learning (DRL) methods, to solve the corresponding MDP problems. Simulation results are presented to show that the proposed scheme can achieve the goal of energy saving and QoE enhancement with the corresponding constraints satisfied.

Nowadays, the shortage of the licensed spectrum and the rapid development of mobile communication technologies have brought new challenges on the spectrum utilization. LTE in the unlicensed band, namely LTE-U, under the carrier aggregation technology has been widely concerned. In the multiple LTE-U base station scenario, the main problem is to design a proper access mechanism in the unlicensed band to avoid mutual interference. Meanwhile, LTE-U can not continuously transmit in the unlicensed band, which would affect the transmission performance of delay-sensitive users. In this paper, we proposes a delay-based priority resource scheduling scheme for multiple LTE-Us that could ensure the fairness resource allocation among LTE-U, the transmission performance of WiFi, as well as the transmission quality of delay-sensitive LTE-U users. The optimization problem is solved by two steps: delay-based priority resource scheduling and price-based resource allocation scheme. Simulation results demonstrate the effectiveness of the proposed algorithm.

Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users who without licensed spectrum in the network can suffer from long executing time, which is not acceptable for latency-sensitive and computation- intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user, i.e., the quality of service of the PU can be guaranteed, and within the available computation resources of the MEC server, the primary user and secondary users with different channel gains both can offload tasks to the MEC server through non-orthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the non-convexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near-optimal solution. Simulation results show that the proposed scheme can effectively reduce the overall computing latency for the secondary network.

Summary In this paper, we study the full‐duplex relay selection strategy based on a potential game in a cognitive cooperative network under the interference power constraint from secondary users to the primary receivers, the total available transmission power constraint for the secondary system, and the self‐interference constraint at each secondary relay. The relay selection problem is modeled as a non‐cooperative game where the total rate of a cognitive cooperative network has common utility. Then, we prove that the game is a potential game that has at least a pure strategy Nash equilibrium (NE), and the optimal strategy set that able to maximize cognitive cooperative system rate is also a pure strategy NE of the proposed game model. On the premise of having no information of infeasible strategy sets, we solve the feasibility conditions of the pure NE in the proposed game. Furthermore, we propose a cognitive full‐duplex relay iterative algorithm that can achieve a pure strategy NE, and the complexity and the convergence of the proposed algorithm are studied. Simulation results show that the proposed algorithm can achieve optimal or near optimal rate performance with low complexity and offers significant performance gain compared with the traditional half‐duplex mode.

In the two-layer space communication network (TLSCN), communications can be performed to obtain higher throughput and lower latency by introducing various types of spatial nodes. However, the mobility of spatial nodes and the connectivity of spatial links result in the time-varying network topology, and intermittent link connection. This further leads to the lack of continuous contact, unreliable transmission, and high transmission cost. In this article, contact plan design (CPD) is employed to address the above problems by increasing the contact reliability while decreasing the contact cost. First, a directional space-time graph (DSTG) is constructed by considering the motion trajectory of spatial nodes and the time evolution nature of TLSCN. Afterwards, based on DSTG, we propose three CPD methods with greedy algorithm by considering the limited computing power of nodes. The three methods can optimize the objective functions of the total contact reliability, total contact cost, and invalid probability cost, respectively. The simulation results show that the proposed CPD methods can effectively improve the contact reliability, reduce the contact cost of TLSCN, and perform well with the increase of network density.

The phenomenal growth of mobile data has brought new challenges on the limited spectrum resource. Deploying LTE on unlicensed bands has been introduced, namely LTE-U, which could provide a higher transmission data rate, spectrum efficiency as well as seamless mobile user experience by taking advantage of the Carrier Aggregation technology. In this paper, we first introduce the coexistence model between LTE-U and WiFi in the multi-operator scenario. To avoid long colliding time among operators due to the same backing off window size, a full duplex-based listen and talk access scheme is introduced. The proposed listen-and-talk based imperfect sensing power adaption (LAT-ISPA) scheme could avoid the interference among multi-LTE-U operators, maximize the effective throughput of LTE-U while ensuring the transmission performance of WiFi by optimizing the backing off window as well as the transmission power in different scenarios. Due to the residual self-interference by the LAT scheme, the imperfect sensing is taken into consideration. Simulation results show that the proposed algorithm could achieve a considerable performance improvement with respect to the schemes in literatures.

In this paper, we consider a cellular device-to-device (D2D) system which consists of one base station (BS) deployed with a mobile edge computing (MEC) server, and a number of users. By defining task execution cost as the weighted sum of execution latency and energy consumption, the joint computation offloading and resource allocation problem is formulated as a task execution cost minimization problem under the constraints of task requirement, computation offloading, resource allocation and task partition, etc. As the formulated optimization problem is a mixed integer nonlinear problem, which cannot be solved conveniently, we decompose it into two subproblems, i.e., computation offloading subproblem and resource allocation subproblem, and solve the two subproblems by applying Kuhn-Munkres algorithm and Lagrange dual method, respectively. Numerical results demonstrate the effectiveness of the proposed scheme.

In this paper, we investigate the optimal transmit antenna placement for short-range indoor line-of-sight (LOS) 2 × 2 multiple-input multiple-output (MIMO) channels in three-dimensional (3D) space. The corresponding channel with arbitrary antenna orientations in 3D space is first established by using the spherical-wave model (SWM). On this basis, we propose a scheme of the transmit antenna placement optimization based on the ant colony algorithm (ACA) with the mobile station (MS) moving in a certain area. Simulation results demonstrate that the proposed scheme effectively improves the average channel capacity in a region.

We consider a holistic approach for dual‐access cognitive small cell (DACS) networks, which uses the LTE air interface in both licensed and unlicensed bands. In the licensed band, we consider a sensing‐based power allocation scheme to maximize the sum data rate of DACSs by jointly optimizing the cell selection, the sensing operation, and the power allocation under the interference constraint to macrocell users. Due to intercell interference and the integer nature of the cell selection, the resulting optimization problems lead to a nonconvex integer programming. We reformulate the problem to a nonconvex power allocation game and find the relaxed equilibria, quasi‐Nash equilibrium. Furthermore, in order to guarantee the fairness of the whole system, we propose a dynamic satisfaction‐based dual‐band traffic balancing (SDTB) algorithm over licensed and unlicensed bands for DACSs which aims at maximizing the overall satisfaction of the system. We obtain the optimal transmission time in the unlicensed band to ensure the proportional fair coexistence with WiFi while guaranteeing the traffic balancing of DACSs. Simulation results demonstrate that the SDTB algorithm could achieve a considerable performance improvement relative to the schemes in literature, while providing a tradeoff between maximizing the total data rate and achieving better fairness among networks.

Mobile edge computing (MEC) has risen as a promising technology to augment computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues, we formulate an optimization problem in wireless cellular networks with mobile edge computing, taking into consideration computation offloading decision, physical spectrum resource allocation, MEC computation resource allocation, and content caching strategy. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers (ADMM) based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, the MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using graph coloring. The outcomes of the offloading decision and PRB allocation are then used to allocate the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

How to improve system capacity and spectral efficiency is a key issue for next generation wireless communication. Heterogeneous network (HetNet) has been considered as a new promising technique for enhancing the quality of service and spectrum efficiency due to different radio access technology and network structures. However, conventional resource allocation algorithms in HetNets are achieved under the assumption of perfect parameter information which may be invalid in practical systems. In this paper, a robust rate maximization resource allocation problem for multiuser cognitive HetNets is formulated to flexibly use network resource and improve overall capacity where robust cross-tier interference constraint and maximum transmit power constraint of base station are simultaneously considered. The semi-infinite programming problem is converted into a geometric programming problem by using relaxation approaches. Simulation results show that the proposed algorithm can guarantee transmission performance of macrocell users and microcell users under channel uncertainties.

Vehicular ad-hoc networks (VANETs) have received considerable attention from both academia and industry in recent years. In VANETs, source vehicles (SVs) are allowed to connect roadside units, such as the access points (APs) of wireless access networks and conduct information interaction. However, due to the high-speed mobile characteristics of VANETs and the dynamic random characteristics of wireless channels, the direct connection between SVs and APs might be inaccessible. In this case, some neighbor vehicles referred to as relay vehicles (RVs) can be selected as relay nodes and help to forward data packets for the SVs. In this paper, we propose an analytical model for evaluating the transmission performance of RVs in VANETs. In particular, we apply network calculus theory to formulate the arrival curve of SVs and the service model of RVs, respectively, and evaluate the effective throughput of the RVs when forwarding data packets for various SVs. We then propose a joint effective throughput optimization based RV selection algorithm. The optimization problem is formulated and transformed into an optimal matching problem in a bipartite graph, which can then be solved based on Kuhn-Munkres (K-M) algorithm. Numerical results demonstrate that compared to previous algorithms, the proposed algorithm offers better transmission performance.

With the development of the fifth generation communication technology, how to improve system capacity and spectral efficiency is a key issue, which has attracted more and more attention from industry and academia. Heterogeneous network has been considered as a new promising technique for enhancing the quality of service, energy and spectrum efficiency as well as coverage of network due to different radio access technology (RAT) and different network structures. In this paper, the rate maximization resource allocation problem for multiuser cognitive heterogeneous networks is formulated to flexibly use network resource and improve the overall capacity, which simultaneously considers cross-tier interference constraint and maximum transmit power of cognitive microcell base station. The non-convex optimization problem is converted into a geometric programming problem which can be solved by Lagrange dual method in a distributed way. Simulation results are given to show the performance of the proposed algorithm in terms of the achievable system capacity and the interference to the macrocell network.

Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.

In recent years, heterogeneous radio access technologies have experienced rapid development and gradually achieved effective coordination and integration, resulting in heterogeneous networks (HetNets). In this paper, we consider the downlink secure transmission of HetNets where the information transmission from base stations (BSs) to legitimate users is subject to the interception of eavesdroppers. In particular, we stress the problem of joint user association and power allocation of the BSs. To achieve data transmission in a secure and energy efficient manner, we introduce the concept of secrecy energy efficiency which is defined as the ratio of the secrecy transmission rate and power consumption of the BSs and formulate the problem of joint user association and power allocation as an optimization problem which maximizes the joint secrecy energy efficiency of all the BSs under the power constraint of the BSs and the minimum data rate constraint of user equipment (UE). By equivalently transforming the optimization problem into two subproblems, that is, power allocation subproblem and user association subproblem of the BSs, and applying iterative method and Kuhn-Munkres (K-M) algorithm to solve the two subproblems, respectively, the optimal user association and power allocation strategies can be obtained. Numerical results demonstrate that the proposed algorithm outperforms previously proposed algorithms.

We consider a sensing-based power allocation scheme in a cognitive small cell network to maximize the sum rate of each small cell by jointly optimizing both the cell selection, the sensing operation and the power allocation over channels, under the condition of interference to primary users below a certain value. Due to intercell interference and the integer nature of the cell selection, the resulting optimization problems lead to a non-convex integer programming which is NP-hard. In order to deal with the non-convexity, we reformulate the problem to a non-convex power allocation game and use the relaxed equilibria concept, namely, quasi-Nash equilibrium. A sensing-based power allocation optimization algorithm that converges to a quasi-Nash equilibrium is also discussed in this paper. Simulation results show that the proposed approach achieves substantial performance gains with respect to a deterministic approach.

Cognitive radio networks (CRNs) are expected to improve spectrum utilization significantly by allowing secondary users (SUs) to opportunistically access the licensed spectrum of primary users (PUs). In a CRN scenario consisting of multiple heterogeneously integrated CRNs, the SUs with multiple interfaces may have to conduct spectrum handoff to target subchannels. Jointly considering the interruption delay and the transmission performance of the handoff SUs on target subchannels, the subchannel selection and resource allocation optimization problem which maximizes the energy efficiency of all the interrupted SUs under the quality of service (QoS) constraints is formulated and solved through applying iterative algorithm and Lagrange dual method. Numerical results demonstrate the efficiency of the proposed scheme.

In cognitive radio networks, a critical issue is to exploit the spectrum holes based on spectrum sensing while avoiding interference to the primary users. However, the reliability of sensing is uncertain. In order to increase the probability to access the channel, cognitive users may report false detection results and become malicious users (MUs), which could significantly degrade the performance of spectrum sensing. In this paper, we proposed an energy efficient MUs detection algorithm which is able to perform coalition-based cooperative detection and spatial correlation with Geary'C theory to maximize the probability of MUs detection. The problem is reformulated as a coalition game with the theoretical certification of its stability. Simulation results show that our algorithm is able to achieve a significant improvement while saving the energy in the MUs detection process compared with other algorithms in the literature.

Device-to-device (D2D) communication has recently emerged as a promising technology to improve the capacity and coverage of cellular systems. Coordinating interference between D2D and cellular users plays a crucial role in realizing D2D communications underlaying cellular networks successfully. While most of prior mechanisms for D2D have focused on mitigating interference within a single-cell system, they fail to address intercell interference with multiple cell settings. In this paper, we investigate the intercell interference issue in a cellular network where a D2D link reuses the available spectrum resources of multiple cells. We propose a game-theoretic resource allocation scheme, termed GALLERY, to address this problem. Unlike existing works that typically treat D2D users as players, we characterize base stations (BSs) as players competing for resources allocation quota of D2D demand, and define the utility of each player as the payoff gained from both cellular and D2D. We also propose a resource allocation protocol based on the equilibrium derivation. Extensive simulations are conducted to verify the proposed scheme and the results show that it can considerably enhance the system performance in terms of sum rate and sum rate gain. It is expected that GALLERY provides systematical insights into resource configurations of multiple cells for D2D communications.

In this paper, a new algebraic method to decode the (71, 36, 11) QR code up to five errors is proposed. It completely avoids computing the unknown syndromes, and uses the previous scheme of decoding this QR code up to three errors, but corrects four and five errors with a new different method. In the four-error case, the new algorithm directly determines the coefficients of the error-locator polynomial by eliminating unknown syndromes in Newton identities. Subsequently, the shift-search algorithm can be utilized to decode the fifth error and the concept of bit reliability is also introduced to accelerate the decoding process. In other words, a weight-five-error pattern can be decoded in terms of the four-error case by inverting an incorrect bit of the received word in ascending order of reliability. Particularly, a threshold parameter γ can be preset to limit the number of inverting bits one by one, and a corresponding upper bound of the probability that decoding fails is derived. Finally, simulation and analysis show that the proposed new decoding algorithm for the abovementioned QR code not only significantly reduces the decoding complexity in terms of CPU time but also saves a lot of memory while maintaining the same error-rate performance. Additionally, the introduction of γ achieves a better tradeoff between the decoding performance and the computational complexity.

In this paper1, a method to search the subsets I and J needed in computing the unknown syndromes for the (73, 37, 13) quadratic residue (QR) code is proposed. According to the resulting I and J, one computes the unknown syndromes, and thus finds the corresponding error-locator polynomial by using an inverse-free BM algorithm. Based on the modified Chase-II algorithm, the performance of soft-decision decoding for the (73, 37, 13) QR code is given. This result is never seen in the literature, to our knowledge. Moreover, the error-rate performance of linear programming (LP) decoding for the (73, 37, 13) QR code is also investigated, and LP-based decoding is shown to be significantly superior in performance to the algebraic soft-decision decoding while requiring almost the same computational complexity.

In this paper, a method to search the set of syndromes' indices needed in computing the unknown syndromes for the (73, 37, 13) quadratic residue (QR) code is proposed. According to the resulting index sets, one computes the unknown syndromes and thus finds the corresponding error-locator polynomial by using an inverse-free Berlekamp-Massey (BM) algorithm. Based on the modified Chase-II algorithm, the performance of soft-decision decoding for the (73, 37, 13) QR code is given. This result is new. Moreover, the error-rate performance of linear programming (LP) decoding for the (73, 37, 13) QR code is also investigated, and LP-based decoding is shown to be significantly superior in performance to the algebraic soft-decision decoding while requiring almost the same computational complexity. In fact, the algebraic hard-decision and soft-decision decoding of the (89, 45, 17) QR code outperforms that of the (73, 37, 13) QR code because the former has a larger minimal distance. However, experimental results indicate that the (73, 37, 13) QR code outperforms the (89, 45, 17) QR code with much fewer arithmetic operations when using the LP-based decoding algorithms. The pseudocodewords analysis partially explains this seemingly strange phenomenon.

The next generation wireless communication system is expected to provide users with high-speed communication services through integrating multiple wireless access technologies, including cellular system, wireless local area network, worldwide interoperability for microwave access, and ad hoc network, etc. In a heterogeneous wireless network scenario, mobile nodes with multiple interfaces will be capable of choosing different access networks for service accessing and performing vertical handoff among various networks. However, the heterogeneity and incompatibility of wireless access technologies and the varieties of user service types pose difficulties and challenges to efficient vertical handoff scheme design. In this paper, the joint access network utility is modeled based on quadratic utility function and a vertical handoff decision algorithm that achieves the maximization of joint network utility under certain constraints of network load status and user handoff number is proposed. Numerical results demonstrate the efficiency of the proposed algorithm.

User selection for access network is considered to be one of the distinct features of heterogeneous wireless systems, in which users with multi-network interface terminals can freely select access network for better quality of service with lower expense. On the other hand, service providers (SPs) will have to face more intense competition for attracting more subscribers and increasing their profits, which can be achieved through either non-cooperative or cooperative strategies. In this study, the authors propose a unified quantification model for evaluating the access service of heterogeneous systems. The relation between competitive SPs and users is described by different game models, based on general assumptions and practical application scenarios. A novel network selection scheme for maximising user performance–cost ratio (PCR) is proposed. Numerical results demonstrate that all SPs can achieve Nash equilibrium price under non-cooperative game framework and coalition price for cooperative game case to maximise their absolute profits, and the maximal PCR criterion for user network selection scheme is analysed under different scenarios.

While throughput maximization and service differentiation are two critical issues in wireless local area networks (WLANs), both are separately investigated in most existing work. This paper, from a different angle, addresses how to maximize saturation throughput of a WLAN conditioned that bandwidth differentiation is supported too. A novel model is established for this problem assuming IEEE 802.11e is used. We calculate the optimal values of minimum contention window for stations to maximize the saturation throughput and provide differentiated service as well. The simulation results validate our new model.

While most existing work focuses separately on how to improve WLAN saturation throughout and how to provide differentiated service, few attention is put to study their relationship. In this paper, we investigate the impact of service differentiation on saturation throughput maximization in IEEE 802.11e WLANs and theoretically prove that it is contradictory and impossible to achieve both of them simultaneously. In other words, saturation throughput is maximized without service differentiation or service differentiation reduces the maximal achievable saturation throughput more or less.

To be adaptive to the traffic variations in some real-time sensor applications, AC-MAC is proposed by Jin Ai et al. Based on Sensor Medium Access Control (S-MAC), AC-MAC introduces an adaptive duty cycle scheme within the framework of S-MAC. However, frequent transceiver state switches can lead to the increasing consumption of energy. In order to solve this problem, we focus our research on how to reduce the number of transceiver state switch. By combining AC-MAC with the Dynamic Power Management, it brings in a new protocol, an Adaptive Coordinated MAC Protocol based on Dynamic Power Management for Wireless Sensor Networks, AC-MAC/DPM, which not only guarantees low delay or high throughput, but also reduces the potential energy consumption when the traffic load is high.

In this paper, we explore how to make RWBO+BEB support service differentiation. An analytical model is proposed to analyze how to choose the minimum contention windows according to the bandwidth ratios of stations. Based on the analysis, a novel service differentiated backoff algorithm for IEEE 802.11 DCF, named DS-RWBO, is proposed. The simulation results indicate that DS-RWBO can allocate the wireless bandwidth according to the bandwidth ratio of each station.