Mobile devices (MDs) are increasingly incorporating deep neural network (DNN) inference into their systems due to the rapid growth of intelligent applications. Mobile edge computing-based distributed DNN collaborative inference has gained popularity due to limited on-device computation and energy budgets. However, the resource competition among MDs, along with the coupling of collaborative inference tasks across MDs and servers, creates significant challenges for efficient resource management. This issue is further exacerbated by the complexity of directed acyclic graph (DAG)-structured DNNs. Most prior studies do not jointly address the dual challenges of partitioning complex-structured DNNs and leveraging advanced optimization for collaborative inference, and their resilience to channel condition fluctuations remains underexplored. To address these challenges, we propose a novel task-aware collaborative inference framework. First, we devise a fine-grained partitioning point search algorithm based on a bidirectional graph linked list, which enables one-dimensional and flexible partitioning of DAG-structured DNNs. We then reformulate the problem of minimizing collaborative inference energy consumption and latency as a task-aware Markov decision process (MDP), which partitions each user's inference task queue into consecutive task windows for resource allocation. Building on this, we propose an Embedded Multi-Agent Hybrid Proximal Policy Optimization (EMH-PPO) algorithm to learn effective policies. Extensive experiments conducted across diverse network scenarios reveal that, compared to local DNN inference on MDs, our proposed method reduces inference latency by up to 64% and energy consumption by up to 46%.

As the in-network computing (INC) paradigm evolves, efficient scheduling of dependent tasks within complex network systems becomes increasingly crucial. The network needs to handle high-level resource demands while adhering to strict latency requirements. Deterministic delay constraints are particularly critical in applications that rely on directed acyclic graphs (DAGs). To address this challenge, we first propose a deterministic delay-aware task scheduling optimization problem over INC to maximize resource utilization and ensure task acceptance. We accurately establish the complex deterministic delay constraint through traffic arrival and service curves and utilize network calculus for conversion to facilitate solving. Then, we further transform the task optimization problem into MDP and develop a deep reinforcement learning (DRL) algorithm that combines graph neural network (GNN) and delay-aware proximal policy optimization (DPPO) to solve it, called the Deterministic Delay-aware Task Scheduling (DDTS) scheme. It utilizes multilayer GNN to handle task dependencies and applies the DPPO algorithm to introduce deterministic delay penalty factors to evaluate policy operations, achieving optimal task scheduling. The simulation results demonstrate the significant advantages of the DDTS scheme over existing algorithms and task scheduling schemes in terms of task acceptance rate and resource utilization.

Aerial base station (AeBS) is a promising technology for providing wireless coverage to ground user equipment. Traditional methods of optimizing AeBS networks often rely on pre-known distribution models of ground user equipment. However, in practical scenarios such as natural disasters or temporary large-scale public events, the distribution of user clusters is often unknown, posing challenges for the deployment and application of AeBS. To adapt to complex and unknown user environments, this paper studies a method of estimating information from local to global and proposes a multi-agent AeBSs autonomous deployment algorithm based on deep reinforcement learning (DRL). This method attempts to dynamically deploy AeBS to autonomously identify hotspots by sensing user equipment signals without network-side assistance, providing a more comprehensive and intelligent solution for AeBS deployment. Simulation results indicate that our method effectively guides the autonomous deployment of AeBS in emergency scenarios, addressing the challenge of the lack of network-side assistance.

With the significant advances in generative AI (GAI) and the proliferation of mobile devices, providing high-quality AI-generated content (AIGC) services via wireless networks is becoming the future direction. However, the primary challenges of AIGC service delivery in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. In this paper, we employ semantic communication (SemCom) in diffusion-based GAI models to propose a resource-aware workload-adjustable transceiver (ROUTE) for AIGC delivery in dynamic wireless networks. Specifically, to relieve the communication resource bottleneck, SemCom is utilized to prioritize semantic information of the generated content. Then, to improve computational resource utilization in both edge and local and reduce AIGC semantic distortion in transmission, modified diffusion-based models are applied to adjust the computing workload and semantic density in cooperative content generation. Simulations verify the superiority of our proposed ROUTE in terms of latency and content quality compared to conventional AIGC approaches.

Digital twin (DT), as a key enabling technology for 6G edge intelligence, can establish real-time connections between digital twin objects and physical devices, ensuring real-time synchronization and thereby enhancing the service performance and stability of edge networks. This paper considers the high dynamics of edge networks and combines digital twins with edge networks to construct a three-layer network architecture. Based on the demands for low-latency services and system energy efficiency, we design a network metric: system overhead, to minimize service latency and system energy consumption. To achieve these system objectives, we integrate digital twin technology with multiagent deep reinforcement learning (MADRL), proposing a digital twin-driven multi-agent scheme for green and reliable resource allocation. This approach effectively minimizes system overhead and can adapt well to dynamic changes in terminal devices. Compared with baseline algorithms, it reduces system overhead by at least 7 % while maintaining significant reliable.

Integrating Reconfigurable Intelligent Surfaces (RIS) into Cell-Free networks is a key direction for the future evolution of mobile networks, offering substantial potential for energyefficient communication architectures. This paper proposes an innovative segmented RIS-assisted Cell-Free network model and studies the weighted sum rate (WSR) maximization problem with complex segmentation pairing. Owing to the nonconvexity of the problem, we decouple the active beamforming, passive beamforming, and segmentation matrix into three subproblems using an alternation optimization framework. Specifically, fractional programming, successive convex approximation, and the swap-matching-based algorithm are adopted to solve the variables in an iterative way, respectively. The simulation results illustrate that the proposed strategy approaches 97.37 % of the WSR performance of the unsegmented RIS-assisted Cell-Free network with lower beamforming complexity through segmentation optimization.

The task of converting Hanyu Pinyin abbreviations to Chinese characters is a significant branch within the domain of Chinese Spelling Correction (CSC). It plays an important role in many downstream applications such as named entity recognition and sentiment analysis. This task typically involves text-length alignment and seems easy to solve; however, due to the limited information content in pinyin abbreviations, achieving accurate conversion is challenging. In this paper, we treat this as a fill-mask task and propose CNMBERT, which stands for zh-CN Pinyin Multi-mask BERT Model, as a solution to this issue. By introducing a multi-mask strategy and Mixture of Experts (MoE) layers, CNMBERT outperforms fine-tuned large language models (LLMs) and ChatGPT-4o with a 61.53% MRR score and 51.86% accuracy on a 10,373-sample test dataset.

The development of B5G and 6G technologies has led to an explosive growth in device connectivity density in Industrial Internet of Things (IIoT) systems. However, the limited spectrum resources in industrial wireless networks pose significant challenges for large-scale access and communication rates, especially for factory automation applications that are sensitive to control stability and latency. In this article, we investigate an uplink nonorthogonal multiple access (NOMA) transmission for ultrareliable and low-latency communication services in IIoT systems, where sensors in NOMA clusters transmit collected data to the base station to meet the high communication rate and control stability requirements of controlled devices. The dynamic control convergence constraint is theoretically transformed into an optimal control condition in each communication round based on the decoding error probability. Additionally, we formulate an optimization problem to maximize the effective throughput of the considered system in the finite blocklength regime by jointly optimizing blocklength allocation, power allocation, and decoding error probability. To solve this mixed integer nonlinear programming problem, we decompose it into two subproblems and propose an efficient optimization framework based on generative AI. Specifically, we apply successive convex approximation to solve the blocklength allocation subproblem, and use a diffusion model to address the joint power control and decoding error probability subproblem. Finally, extensive simulation results demonstrate the effectiveness of this approach.

Wireless networked control systems (WNCSs) have emerged as a new paradigm in industrial Internet of Things (IIoT), where base station (BS) transmits control commands generated by the remote controller to actuators of multiple control subsystems through shared wireless channels. This paper investigates a novel rate splitting multiple access (RSMA) enabled ultra-reliable and low-latency (URLLC) transmission design for industrial control applications in WNCSs, where control commands are splitted and transmitted with finite blocklength regime. This design aims to maximize the system sum rate (SR) by optimizing beamforming at BS, rate control for each control subsystem, and user pairing between control subsystems and subcarriers, while ensuring the control stability requirements for all control subsystems. We first derive the control convergence constraint into a communication reliability constraint expressed in terms of outage probability. Then we propose a nested iterative algorithm adopting alternating optimization (AO). During the inner iteration, we propose a resource allocation method leveraging successive convex approximation (SCA) to jointly optimize beamforming and rate control, while during the outer iteration, a hypergraph game-theoretic based matching method is provided to obtain the optimal pairing result between control subsystems and subcarriers. Simulation results demonstrate that the proposed transmission design outperforms existing schemes in terms of communication rate and control cost.

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature.

In the Metaverse, real-time, concurrent services such as virtual classrooms and immersive gaming require local graphic rendering to maintain low latency. However, the limited processing power and battery capacity of user devices make it challenging to balance Quality of Experience (QoE) and terminal energy consumption. In this paper, we investigate a multi-objective optimization problem (MOP) regarding power control and rendering capacity allocation by formulating it as a multi-objective optimization problem. This problem aims to minimize energy consumption while maximizing Meta-Immersion (MI), a metric that integrates objective network performance with subjective user perception. To solve this problem, we propose a Multi-Objective Multi-Agent Evolutionary Reinforcement Learning with User-Object-Attention (M2ERL-UOA) algorithm. The algorithm employs a prediction-driven evolutionary learning mechanism for multi-agents, coupled with optimized rendering capacity decisions for virtual objects. The algorithm can yield a superior Pareto front that attains the Nash equilibrium. Simulation results demonstrate that the proposed algorithm can generate Pareto fronts, effectively adapts to dynamic user preferences, and significantly reduces decision-making time compared to several benchmarks.

Explainable ensemble learning combines explainable artificial intelligence (XAI) and ensemble learning (EL) to solve the closed-box problem of EL and provide a clear and transparent explanation of the decision-making process in the model. As a distributed machine learning architecture, EL deploys base learners trained with local data at edge node and infers on target tasks, then combines the inference results of the participating base learners. However, selecting all base learners into EL may result in wasting more computing resources and not obtain better performance. To address this issue, we put forward the definition of confidence level (ConfLevel) on the basis of XAI and verify its effectiveness as the metric of selecting the base learner. Then, we take the joint optimization model of considering high ConfLevel and low computing power to determine the participating base learners for selective ensemble learning (SEL). Due to the non-convex and combinatorial nature of the problem, we propose a node selection and power control algorithm on the premise of Benders’ Decomposition (referred to BD-NSPC) to obtain the global optimal solution efficiently. In addition, simulation results show that BD-NSPC consumes about 30% less energy per EN on average and improves accuracy by 1-2% compared to other SEL algorithms. Besides, compared with federated learning (FL) framework, BD-NSPC reduces the energy consumption by about 25% and the latency by about 28%, achieving comparable accuracy in the edge computing system.

As an important research topic in the field of intelligent network management, network traffic prediction has received extensive attention in recent years. However, the existing research mainly focuses on short-term network traffic prediction and does not consider the influence of external factors and the effective embedding of heterogeneous data sources. Effective longterm traffic prediction has become a challenging problem. To address these challenges, this paper proposes a knowledge-driven deep learning method KGASTN for spatio-temporal graphical convolutional networks for long-term traffic flow prediction with multiple factors. In the method, our innovative idea is to design a knowledge-data dual-driven feature extraction scheme that fuses the knowledge representation of external factors into a spatio-temporal graph convolutional network. The method utilizes knowledge inference and sequence similarity algorithms to realize the extraction of explicit knowledge in log data and the construction of similarity graphs between traffic data sequences; and fuses explicit knowledge and similarity relationships based on the knowledge-data dual-drive model, and ultimately constructs spatio-temporal graph convolutional networks based on the attention mechanism. We evaluate KGASTN with a heterogeneous dataset containing log data, and use several sequence prediction datasets from other application domains for additional comparison. Experimental results show that our method outperforms several state-of-the-art baselines.

Multipath communication is a technique that utilizes multipath transmission to improve network transmission efficiency. Multipath transport protocols like MPTCP usually require complex signaling control and lack adaptability to instantaneous network changes. The advent of programmable switches and INT has addressed these issues to some extent. In this paper, we propose DINT-Based DWRR (Decentralised INT-Based Dynamic Weight Round Robin), a dynamic weight round-robin packet scheduler based on non-centralized telemetry technology. It aims to collect telemetry information and update path weights with millisecond granularity, and efficiently achieve load balancing while reducing telemetry overhead. The core idea of DINT-Based DWRR is to leverage data-plane programmability to achieve the convergence of the forwarding node and the computing node. The forwarding nodes forward the packets using the DWRR (Dynamic Weight Round Robin) method and periodically generate telemetry messages. The computing nodes are dispersed across the forwarding nodes and efficiently update weights to the forwarding nodes. After testing in various experimental scenarios, it is proven that DINT-Based DWRR can provide better scheduling policies, reduce the link packet loss rate, and increase link bandwidth utilization.

A new network monitoring technology called in-band network telemetry (INT) offers users the ability to gather precise, real-time data about the entire network. While several studies have used in-band network telemetry for network-wide monitoring, it is insufficient for the growing number of massive networks that are resource-constrained. Finding the most valuable information with the least amount of resources is challenging. In this paper, We formalize the problem of in-band network-wide telemetry with low resource overheads. This formalized problem aims to achieve high freshness of network performance data using fewer resources and reducing deployment and maintenance costs for O&M personnel. We propose a heuristic method based on path reorganization to address this issue. The heuristic algorithm for planning paths starts from greedy path planning results and finds a more appropriate planning scheme by merging and reorganizing paths. Additionally, probabilistic insertion is considered to reduce the impact of intrusiveness of in-band network telemetry. Simulation results show that our approach is effective in improving resource utilization and reducing the cost of monitoring the network compared to similar studies.

Accurately predicting traffic in a cellular network is challenging since the traffic time series integrated by various wireless services is non-stationary and reveals concealed spatial correlation among different cells. Due to that, the presence of bias in a single forecast model often hinders the ability to generalise under numerous circumstances in wireless traffic data, no particular approach stands out as clearly superior to the others. In this paper, we propose an adaptive ensemble learning paradigm that can benefit from centralizing individual forecast base models. It stacks the prediction outputs of several base learners due to the traffic dynamics characteristic. An improved convolutional neural network (CNN)-based representation learning method is designed to extract the high-order spatial-temporal features in the traffic data and obtain the adaptive weights of participating base learner models for the ensemble. The experimental results verify that the proposed ensemble approach can fully utilize spatial-temporal features and outperform individual statistical and machine-learning models regarding prediction accuracy. Furthermore, the ensemble method via stacking base models with fewer parameters is capable of generating predictions close to the large-parametric spatial-temporal transformer (ST-Tran) model produced.

To support the escalating traffic demands in the 6G era, the novel computing paradigm of in-network computing (INC), where tasks can be processed on the forwarding path, is emerging with enhanced network performance and improved service quality. Considering the large network scale with high dynamics, effective task scheduling in INC paradigm becomes imperative but challenging. In this work, we investigate the task scheduling in INC paradigm to minimize both the task delay and network energy consumption, while considering constraints on task latency, traffic dynamics, and available communication and computing resources. We first construct a novel computing and communication model considering the traffic variation in network nodes on the transmission path. To solve the task scheduling problem, we propose an algorithm, named as NBFNDRL, which is a deep reinforcement learning (DRL) algorithm based on Neural Bellman Ford networks (NBFNet). NBFNet can learn high-dimensional correlated graph structural information, utilize message-passing mechanisms to represent changes in traffic between adjacent nodes, predict scheduling paths, and provide a basis for DRL decision-making. The DRL agent trains and updates NBFNet through interaction with the environment. Finally, we present simulation results to demonstrate the effectiveness of our proposed approach in comparison to benchmark algorithms and various computing paradigms.

The reconfigurable intelligent surface (RIS) is a green and promising technology that provides passive beamforming through a large amount of low-power reflecting elements, to realizes expected coverage extension and interference signal suppression. In this paper, we investigate a self-sustainable RIS-empowered D2D communication network, where the RIS first harvests energy from the D2D signals, and then uses energy collected to sustain its passive beamforming operation. We aim to characterize the energy efficiency (EE) maximization under imperfect channel state information conditions by jointly optimizing the transmit precoding in both two stages, RIS passive beamforming design, and energy harvesting time allocation. An efficient alternating optimization algorithm is proposed to deal with the difficult non-convex optimization problem. Specifically, transmit precoding is optimized by using the Dinkelbach's method, Lagrangian dual transform, quadratic transform and S-procedure. The penalty convex-concave procedure is adopted to solve the optimal phase shift of RIS. A closed-form expression for the optimal energy harvesting duration is derived. The simulation results show that the proposed scheme further enhances the EE compared with the active RIS and no RIS schemes in various scenarios.

The intelligent reflecting surface (IRS) serves to manipulate information and modulate signals, facilitating space-time coding in wireless communication. This study presents an innovative model for implementing space-time block coding (STBC), where the base station transmits multiple symbols to a user using multiple IRSs. These IRSs manipulate phase shifts to establish robust links. Initially, statistical characterizations are derived for the sum of independent generalized gamma (GG) random variables, providing expressions for the end-to-end signal-to-noise ratio (SNR). Subsequently, a closed-form expression for symbol error rate (SER) is developed to evaluate reliability performance. Additionally, an asymptotic expression for SER at high transmission power is supplied to outline performance limits. Simulation results reveal that the STBC system assisted by multiple IRSs attains comparable or superior performance when contrasted with the STBC system that employs multiple amplify-and-forward (AF) relays, utilizing a restricted number of reflecting units.

Split federated learning (SFL) allows clients with limited resources to engage in distributed machine learning, yet it grapples with issues related to energy usage and the efficiency of training. We propose low-carbon hierarchical multiple SFL (HMSFL) to address these issues and advance sustainable computing. HMSFL partitions the client model into several segments, enabling local aggregation among clients. This process amplifies energy efficiency and diminishes the carbon footprint. We formulate the training cost minimization problem and solve it using a generalized task allocation algorithm. Evaluation across real-world tasks demonstrates that HMSFL achieves a 36% reduction in training time and a 33% decrease in energy consumption compared to baseline methods, showcasing its potential for sustainable distributed machine learning.

Emerging use cases within the realm of industrial automation have underscored the importance of predictable wireless networked control when wireless networks bridge both real-time (RT) and time-sensitive (TS) services concurrently. However, the interplay between the varying fading channels, stochastic arrival tasks, and queuing states makes it challenging to guarantee completely the jitter-bounded deterministic latency of TS services and the throughput of RT services. To address this issue, we develop a predictable radio resource scheduling scheme based on Lyapunov-guided proximal policy optimization (LyPPO) for maximizing the transmission rate of RT services while adhering to the jitter-bounded deterministic delay constraint of TS services. The stochastic network calculus (SNC) is innovatively used to deduce the delay violation probability (DVP) and delay-constrained arrival rate bounds for RT services, which guides LyPPO by transforming the invisible jitter-bounded deterministic delay constraints of TS services into visible adaptive bandwidth limits. The simulation results verify that compared to alternative scheduling strategies, the proposed scheme adeptly mitigates the challenges posed by system dynamics and inherent uncertainties and provides predictable performance, encompassing both the foreseeability of TS services latency and the tolerability of RT services latency. Furthermore, the scheme exhibits superior performance in terms of packet loss probability and resource utilization efficiency.

Expanding industrial components and network density raise challenges in the domain of mobility management of multiagent systems (MASs), such as multirobot cooperative transportation. This article investigates the heterogeneous network (HetNet) handover problem in the industrial context involves jointly optimizing data rate, block rate, and handover frequency among large-scale mobile user Terminals. Specifically, by introducing user-centric conditional handover features, we leverage Pareto-efficient solutions to address the multiobjective optimization problem of balancing data rate and block probability. The optimization problem is reformulated into a multiagent learning-based Markov cooperative game to cope with dynamic context conditions, introducing a handover penalty factor to enhance service continuity. Furthermore, we develop a Pareto-efficient multiagent transformer with efficient advantage decomposition, leveraging sequential modeling, and distributed computing power of MAS. Extensive simulations demonstrate the superiority of the proposed algorithm, implementing user-centric optimal handover decisions, while also obtaining an additional fairness gain.

Virtual reality (VR) technology has recently achieved notable success and been widely expected to interplay with more mobile multimedia services. To further enhance real-time immersive experience for VR applications, exploiting cooperative offloading among capable terminal devices should be emerged as an effective means. However, faced with diverse and surging mobile VR user requests, terminal-assisted offloading needs to support comprehensive cached content, ultra-low latency delivery, and continuous energy provisioning, to guarantee stringent quality of service requirements, which poses a critical challenge for resource-constrained terminals. Hence, this paper proposes a Cooperative Offloading framework for Terminal Clusters (named CO-TC), in which VR terminal clusters form several cooperation groups for sharing cached field of view (FoV) tiles and available computing resources to cooperatively perform FoV rendering and content delivery. To maximize energy efficiency in CO-TC, an optimization problem is formulated to jointly decide the task offloading and computing resource utilization. An intelligent offloading scheme is designed based on multi-agent reinforcement learning (MARL) specially using agent relation feature graph embeddings. Moreover, we theoretically prove the permutation invariance and convergence of the proposed algorithm and derive the optimal observation range of the agent to balance the performance gain and interaction overhead in the distributed MARL frame. Finally, simulation results show that the proposed offloading scheme outperforms other baselines in terms of VR service performance, including latency, energy consumption, and energy efficiency.

As a critical and prevalent service in future mobile networks, virtual reality (VR) is latency-sensitive and power-hungry, bringing out the optimization problem of trade-off among power saving, delay, and resource utilization. Content caching and render offloading are deemed as promising solutions to meet the stringent requirements of VR on data transmission speed and end-to-end latency. In this article, we propose a novel distributed computing framework based on multi-agent deep deterministic policy gradient (MADDPG) for joint optimizing terminal-cooperative caching and offloading for VR tasks. Since the individual VR user can hardly reach the optimal actions based on its limited local observed states and samples, MADDPG with centralized training and distributed execution is exploited to solve the above challenge. In addition, the generative adversarial network (GAN) is introduced to obtain experience-enhanced agents in the offline training phase and to achieve an optimal allocation to minimize energy consumption in the online inferring phase. The Nash equilibrium is proven in the case that the distribution of finite real VR data samples is well imitated and complemented by GAN. Numerical results demonstrate that our algorithm has significant superiorities in terms of convergence performance and energy consumption over other benchmarks.

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S4in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.

Faultless and cost-saving route calculation plays a fundamental role in the network traffic engineering. The existing route calculation methods mostly are data-driven and lack the interpretability, which make less use of complex network information. Knowledge graph has the ability to convert complex network data into interrelated knowledge, so the interpretability can be enhanced. In this paper, we propose a novel network route calculation solution based on the knowledge graph, which utilizes the link prediction to handle the high-dimensional features of network. By improving the path-based link prediction framework NBFNet, the defects caused by lack of interpretability are made up. The simulation results show that the proposed method outperforms Bellman-Ford and TransH in terms of packet loss and delay and maintains the network load balance with various network topologies.

The estimation accuracy of first-arrival-path (FAP) delay plays a vital role in positioning performance. We investigate the limitations of traditional cross-correlation (CC) algorithms in delay estimation. Our work proposes a FAP delay estimation mechanism using conditional tabular generative adversarial network (CTGAN) assisted convolutional neural network (CNN). The mechanism uses the CC algorithm to extract the delay feature in the wireless signal as input and finally outputs the FAP delay. For communication scenarios where it is difficult to obtain a large amount of training data, we use CTGAN to assist CNN training to improve the accuracy of FAP delay estimation. A series of simulation experiments were presented to evaluate the performance of CTGAN-assisted CNN and compare it with traditional high-resolution delay estimation algorithms. The results show that CNN performs well in weak LOS signals and dense multipath situations. It can still maintain high precision in the case of insufficient data.

The high-precision clock synchronization is pursued with the consideration of the balance for time-domain resource utilization, when 5G technologies are expected to carry the timing services. Firstly, a clock synchronization model is established in the case that the system exists the observed value loss. The probability distribution of the loss of the observed value is evaluated by a newly proposed delay deterministic confidence method. Then, the error boundness of the clock synchronization is investigated by the Kalman filter algorithm, and the optimization problem for 5G time-domain resource configuration is formulated with guaranteeing the precision of clock synchronization; Finally, the proposed optimization problem is solved by dueling double deep Q-learning-based Lyapunov optimization. The experimental results verify the effectiveness and superiority of the proposed method in terms of the joint optimization of clock synchronization error covariance and throughput.

As a new communication paradigm, neural network-driven semantic communication (SemCom) has demonstrated considerable promise in enhancing resource efficiency by transmitting the semantics rather than all bits of source information. Using a large semantic coding model can accurately distil semantics, and significantly save the required bandwidth. However, this consumes a large amount of computing resources, which are also precious in the network. In this paper, we investigate the joint computing resources and bandwidth allocation for SemCom networks. We first introduce the computing latency model in SemCom, and formulate the joint computing resources and bandwidth allocation optimization problem with the objective of maximizing semantic accuracy. Then, we transform this problem into a deep reinforcement learning framework and exploit a multi-agent proximal policy optimization to solve it. Numerical results show that the proposed method significantly improves the average semantic accuracy in the resource-constrained cases, compared with the two baselines.

6G edge networks strive to offer ubiquitous intelligent services, requiring a greater emphasis on network stability and reliability. However, current networks present a low automation degree of the operation, administration and maintenance process. Consequently, active service migration away from abnormal network nodes and links, as well as automatic and transparent service recovery from sudden anomalies, become challenging tasks. These conditions underscore the urgency for an innovative service self-healing mechanism for 6G edge networks. Digital twin (DT) technology uses modeling to represent physical entities, thereby facilitating lifecycle management. However, the application of DT technology in networks is still a burgeoning field of study. In this paper, we explore the DT-driven service self-healing mechanism in 6G edge networks. Initially, we design a DT-based architecture for service self-healing. Subsequently, we construct a performance prediction mechanism leveraging graph neural networks (GNNs) to devise an efficient prediction model, which aims to accurately infer network performance and promptly detect abnormal network conditions. To maintain fine-grained service stability amidst potential network anomalies, we propose a DT-driven service redeployment mechanism enhanced by GNNs. Comprehensive experimental results reveal that our proposed mechanism can accurately predict flow-level delays and identify abnormal links and nodes. Furthermore, the DT-driven service redeployment mechanism effectively reduces service delay and enhances network load balance.

Fine-grained service offloading in collaborative edge computing can make full use of the limited resource of edge nodes to achieve efficient parallel computing. It is imperative to select appropriate edge nodes for the subtask offloading in order to ensure the network’s load balance. However, there is a lack of research on computing offloading of end-to-end fine-grained services, and existing node selection algorithms can only be used in small-scale scenarios or networks with a fixed number of nodes. In this paper, we construct an end-to-end fine-grained computing offloading model, with load balancing as the optimization goal. Especially, a deep graph matching method, based on graph neural networks, is used for offloading node selection. It can be applied to dynamic and large-scale scenarios with strong generalization capability and fast execution speed. Compared with baseline algorithms, it greatly reduces the network load imbalance degree while ensuring a high acceptance ratio of services and meeting delay, location and resource constraints.

Knowledge graph completion (KGC) aims to perform link prediction to fill lost relations between entities by knowledge graph embedding (KGE). Translating embedding, as an efficient embedding method in KGE, is widely applied in numerous recent KGC models. However, these translating models may lack the ability to express various relation patterns and mapping properties for knowledge graphs (KGs). In this paper, a simple and well-performed translating model named TransC is proposed to express different relations. A multi-channel mechanism is defined firstly to constrain translating embeddings. Then a relation-aware transfer function is designed to break the expressive restriction and map triplets involving the same relation into a corresponding plane. We also mathematically prove that TransC is capable of expressing four popular relation patterns and all mapping properties. Finally, experimental results illustrate that TransC can efficiently represent the different relation patterns and properties and achieve better performance than state-of-the-art translating models.

In the industrial scenario, the requirements for network quality and data security of industrial Time Sensitive Network (TSN) services are becoming more and more stringent. 5G technology has the characteristics of wide bandwidth, low latency, and massive connections. Mobile Edge Computing (MEC) can improve the utilization of resources and the security of data, while future reducing the transmission delay. However, for industrial TSN services, a new network deployment architecture is still needed to provide reliable, flexible, and secure services for the industrial Internet. This paper proposes a local deployment architecture and adopts a deployment optimization scheme of Distributed Unit/ Centralized Unit/ User Plane Function/ TSN Translator (DU/CU/UPF/TT) convergence integration, highly integrated network elements, build DU/CU/UPF/TT compact integrated equipment, realize the idea of the separation of control plane and user plane, and realize efficient data forwarding. Meanwhile, some technologies such as Multiple-transmission/Reception Point (Multi-TRP) and New Radio Dual Connectivity (NR-DC) are used to meet the requirements of the low latency of industrial TSN services, and ensure data security and improve network quality.

With the development of the smart power grid, the requirements for intelligent, vivid, and real-time power system inspection services are getting higher. 5G, artificial intelligence, edge computing, big data, virtual reality, and other information technologies bring breakthroughs for power system inspection services evolution, but in the meanwhile, making the services more complex and draining large amounts of computing resources. In the 5G networks with collaborative computing enabled in cloud-edge-end nodes, how to reasonably decompose a complex power system inspection service into multiple task clusters becomes a prerequisite for the efficient distributed deployment of the service and its rapid execution. This paper proposes an approach for the decomposition of intelligent power system inspection services. It considers the computing resource requirements of the functional components in a power system inspection service as well as the interactions between them, perceives the computing capabilities of heterogeneous nodes, such as cloud, edge, and collaborative end terminal in 5G networks, and decomposes the service into multiple task clusters which retain the original logical structure. Simulation results show that the approach can produce suitable results, which can reduce the expected service execution time and improve the utilization of network resources.

With the development of mobile communication, network technology, and the continuous emergence of intelligent network applications, users' demand for network computing power has increased explosively, which promoted the formation of a multi-level computing power system composed of the end devices, mobile network edge cloud, and center clouds. The terminal and edge computing power resources are limited. The cloud computing power is rich, but the delay is high, so the computing power at all levels needs effective cooperation to meet the quality of service requirements of various ubiquitous computing services. In this trend, cloud computing and edge computing begin to evolve into networked collaborative computing. In this paper, a task scheduling heuristic algorithm based on task cost minimization is proposed for network computing services with a large amount of communication and computation and high delay cost. This method divides the computing tasks of network applications into multiple granularities and schedules the divided sub-tasks, which can improve the utilization of the distributed computing resources and enhance the collaborative scheduling capability of computing and network resources.

Vehicle-to-everything communication is an indispensable component of 6G networks that could help to facilitate future transportation systems. However, massive vehicles and unstable vehicle-to-vehicle (V2V) links may become bottlenecks for the low-latency delivery of contents, such as safety-critical emergency messages and multimedia. Instead of resolving the problem in a centralized way, we propose a massive vehicular Internet-of-Things system and investigate the approach that would enable each vehicle to decide the transmission mode from three modes, i.e., vehicle-to-network, vehicle-to-infrastructure and V2V sidelinks, and wireless resources. Specifically, a multiagent deep reinforcement learning (RL) framework is formulated by combining the multiagent RL approach, WoLF-PHC, with the techniques from deep$Q$-learning (DQN) to gain the formulated framework with the capability of capturing the effects of interaction between learning agents and states of complex environment. The framework is set to maximize the throughput of vehicles while maintaining the latency and reliability constraints of the vehicle communication links. However, it could be easily extended to other objectives. The simulation results demonstrate that the proposed approach outperforms the compared ones in total traffic capacity and satisfaction rate of the vehicles in communication.

With the development of mobile communication and network technology, smart network applications are experiencing explosive growth. These applications may consume different types of resources extensively, thus calling for the resource contribution from multiple nodes available in probably different network domains to meet the service quality requirements. Task decomposition is to set the functional components in an application in several groups to form subtasks, which can then be processed in different nodes. This paper focuses on the models and methods that decompose network applications composed of interdependent components into subtasks in different granularity. The proposed model characterizes factors that have important effects on the decomposition, such as dependency level, expected traffic, bandwidth, transmission delay between components, as well as node resources required by the components, and a density peak clustering (DPC) -based decomposition algorithm is proposed to achieve the multi-granularity decomposition. Simulation results validate the effect of the proposed approach on reducing the expected execution delay and balancing the computing resource demands of subtasks.

As an emerging distributed machine learning (ML) method, federated learning (FL) can protect data privacy through collaborative learning of artificial intelligence (AI) models across a large number of devices. However, inefficiency and vulnerability to poisoning attacks have slowed FL performance. Therefore, a blockchain-based asynchronous federated learning (BAFL) framework is proposed to ensure the security and efficiency required by FL. The blockchain ensures that the model data cannot be tampered with while asynchronous learning speeds up global aggregation. A novel entropy weight method is used to evaluate the participating rank and proportion of the local model trained in BAFL of the devices. The energy consumption and local model update efficiency are balanced by adjusting the local training and communication delay and optimizing the block generation rate. The extensive evaluation results show that the proposed BAFL framework has higher efficiency and higher performance for preventing poisoning attacks than other distributed ML methods.

With the emergence of industrial Internet of Things (IIoT), intensive computation workload will be imposed to industrial end units (IEUs). By leveraging mobile edge computing (MEC), the local computational tasks can be offloaded to servers deployed in mobile edge networks with low latency. This article proposes the intelligent cost-and-energy-effective task offloading in the 5G and Wi-Fi 6 coexisting heterogeneous IIoT networks. The novel joint task scheduling and resource allocation approach comprises the following two parts: a Lyapunov optimization-based component to decide local task scheduling and computing power and an online multiagent reinforcement learning component together with a game theory-based algorithm to select offloading link and decide transmit power, respectively. Simulation results demonstrate the proposed approach holds obvious advantage over the compared “intuition” and “cost optimal” approaches in the efficiency of making comprehensive decision that improves energy efficiency and cost while controlling task delay in the multi-IEU and multiaccess-node MEC systems.

Device-to-Device (D2D) is a very promising technology which can significantly improved the spectral efficiency, while the communication distance is often limited by resource constraints. The reconfigurable intelligent surface (RIS) can optimize the passive phase shift of each element to enhance the signal strength at D2D devices and expand the communication coverage. Regardless of the above advantages, the RIS-aided D2D communication will introduce severe interference to user equipments (UE) or D2D device itself with irrational resource allocation schemes. In this paper, based on hypergraph-with-weight model, a RIS-aided D2D communication network is constructed where the weight of each hyperedge represents the interference severity. Moreover, A greedy coloring algorithm is proposed to solve the complex resource allocation problem in a simple and effective manner. Simulation results show that the proposed greedy coloring algorithm can greatly reduce interference and improve network capacity.

Intelligent reflecting surface (IRS) is advanced as an effective technology to meet the high requirements of frequency spectrum and energy efficiency (EE) in future wireless communication systems, which can dynamically adjust its reflecting elements to control the incident signal and change the signal transmission path, thus improve the channel transmission environment. The prior works on IRS mostly considered the uplink scenarios with single cell, which however, did not address the issue of co-channel interference between different cells. This paper investigates an uplink wireless communication system with single IRS serving multiple cells with multiple users (UEs). The transmit power of users and the phase shifts of IRS are jointly optimized for maximizing the system throughput. The resulting non-convex optimization problem is solved by a heuristic algorithm, in which we exploit a non-cooperative game algorithm to solve the co-channel interference dilemma. Presented simulation results illustrate that the proposed scheme achieves a better performance in both system throughput and EE than other baseline algorithms.

With the development of 5G, the distribution of base stations tends to be dense. Compared with the traditional network architecture, Cloud Radio Access Networks(C-RAN) architecture can satisfy the current requirements of high bandwidth, low latency and low energy consumption. Currently most energy-saving scheme for C-RAN is complex with time cost computing, which may not be suitable for large-scale region. For the problem of energy-efficient resource allocation for dense distribution of Remote Radio Heads(RRHs) in C-RAN, we use K-means clustering algorithm to simplify the network topology and reduce the complexity under a distributed manner. Aiming at the problem of network resource allocation in C-RAN, we use A3C algorithm to allocate network transmission power, and compare the total energy consumption, system energy efficiency and Signal to Interference plus Noise Ratio(SINR) value of terminal devices through simulation experiments. The experimental results show that in the same network environment, A3C algorithm has the highest energy efficiency, and can keep the SINR value of terminal devices in a reasonable range, which proves the effectiveness of A3C algorithm.

Deploying aerial base stations (AeBSs) has been regarded as an effective solution to wireless network capacity enhancement in specific areas with excessive traffic burden but insufficient capacity. Since the traffic distributions in wireless networks tend to be ever-changing, the deployed AeBS need to adjust its position to rapidly and continuously adapt to the drifting capacity enhancement demands, which is difficult to handle with traditional optimization methods due to high computational complexity and poor scalability. In this paper, we design a multi-agent deep reinforcement learning-based 3D AeBS deployment algorithm with the goal of maximizing the system throughput, which is able to make decisions in dynamic environments and conducts in a distributed manner. Additionally, in order to address the interference issue between multiple AeBSs, we adopt the Coordinated Multiple Points Transmission (CoMP) in the air-to-ground communication and propose a clustering algorithm to form groups of AeBSs for cooperative communication based on the network interference characteristics. Simulation results demonstrate that the proposed approach has significant throughput gains over conventional schemes without CoMP, and that the proposed multi-agent deep Q network (MADQN) is more efficient than centralized DQN in deriving the solution.

Aiming at the ultra-low latency service demand of power emergency Internet of Things (PEIoT), a multi-slice network architecture for ultra-low delay transmission of emergency Internet of Things was designed, and a PEIoT slice resource reservation and multi-heterogeneous slice resource sharing framework was proposed. The proposed framework adopts the deep reinforcement learning method to realize the automatic prediction and allocation of real-time resource requirements among heterogeneous slices. Simulation results show that the method based on resource reservation enables PEIoT slice to explicitly retain resources and provides a better level of security isolation. Deep reinforcement learning can ensure the accurate and real-time update of resource reservation and effectively consider the resource utilization rate and the differentiated service quality requirements of slices. The comparison with two existing algorithms shows that Dueling DQN has better performance advantages.

In recent years, Industrial Internet of Things (IIoT) has been at a high-speed development stage. However, the diversity of services and technical standards in the Industrial Internet of Things and vertical industries makes it impossible for the existing communication technologies to use the same physical network to meet different needs of services. To tackle this problem, Network Slicing (NS) is proposed and has become the key enabling technology of the fifth generation mobile communication (5G) network. Network Slicing is a logical network running on the physical or virtual infrastructure, which can divide the network into multiple logical networks with different configurations according to different requirements. Each slice is isolated from each other and does not affect each other, which can meet the needs of different application scenarios in IIoT. In this paper, a delay optimization oriented Service Function Chain (SFC) deployment model is proposed and a two-stage collaborative deployment algorithm called DSFC is designed. Simulations validate that the proposed method can reduce the delay while ensuring the reliability of SFC deployment.

In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.

In the face of rapidly emerging and explosion IP services, 5G IP+optical communication network architecture will become an important mode of communication for smart grid communication network. Under the control of SDN, management and maintenance of IP+optical networks can be realized effectively. In order to improve the collaborative ability and resource utilization of 5G IP+optical networks, this paper combines the characteristics of IP services. Firstly, risk equilibrium index is designed according to the bearing characteristics of IP network and optical network. Then, combined with network delay, bandwidth, website level difference and similarity of primary and alternate routes, a reasonable primary and alternate routes allocation model is designed. Finally, a co-allocation algorithm of service routing in 5G IP+optical networks based on deep reinforcement learning is proposed. The simulation results and comparative analysis show that the method not only fully utilize the resources of IP+optical networks, but also guarantee the average service delay and reduce the network risk. Otherwise, this method effectively improves the convergence speed, which provides demonstration and theoretical guidance for the construction of the future power communication network.

Due to the limited energy in massive machine type communication (mMTC), improving energy efficiency is necessary for cellular IoT transmission. This paper considered the edge IoT devices to access relay nodes and used NOMA scheme to transmit information to improve system energy efficiency and reliability. Firstly, we proposed a multi-hop uplink NOMA assisted IoT transmission model with rely cooperation. The transmission model uses NOMA technology and needs to control the power. Secondly, for the channel matching problem, traditional algorithm models such as KM and Hungarian algorithms are only applicable to two-hop system. The paper proposed a multi-hop channel allocation model to improve transmission reliability. Then we used an intelligent optimization algorithm to solve optimization problem. This paper improved the GSO algorithm and proposed the GCM algorithm. GCM can solve the problem of local optimization of traditional GSO algorithm. Finally, in the simulation part, we compared the GCM algorithm with GSO, ACO and random matching. The GCM algorithm has higher reliability, which is 8% and 12% higher than GSO and ACO. It is clear that energy efficiency achieved by GCM is 10% and 18% than GSO and ACO, respectively.

The cloud computing market is dynamic, distributed, and lacks central authorization. In this environment, cloud resource providers are vulnerable to deception and cloud resources may be abused. How to implement efficient and feasible trusted negotiations with users to expand Benefits is an urgent issue. Based on SLA (Service Level Agreement), this paper proposes a trusted negotiation method to optimize cloud resource allocation from the perspective of cloud resource providers. In a nutshell, it firstly quantifies each indicator based on the total amount of cloud resources requested by the user and the corresponding price, the user's comprehensive credit, and the total amount of resources corresponding to each SLA level, then filters the users who meet the requirements. Next knapsack algorithm and the greedy algorithm based on dynamic programming are used to predict the allocation of cloud resources respectively. Finally, the allocated users are negotiated to reach a transaction. This article takes the resource allocation price, negotiated price, and negotiated success rate as the evaluation index. The simulation results show that compared with the greedy algorithm, the algorithm in this paper has higher resource allocation price, negotiated price and negotiated success rate under different numbers of users, and can effectively realize the optimal allocation of cloud resources.

To meet the immensely diverse service requirements, heterogeneous cloud radio access network (H-CRAN) architecture and D2D communication is embraced. Consequently, the resource allocation between D2D pairs and current users is a challenge. In this paper, a joint power control and sub-channel allocation scheme is proposed. The original mixed-integer nonlinear programming problem is decomposed into power and sub-channel allocation. Geometric Vertex Search approach and 3-dimensional (3-D) matching method are used to solve them. Finally, numerical results verify the proposed scheme has about 35% and 60% improvement in total throughput comparing with other approaches.

To satisfy ultra-high data volume and traffic density transmission requirements, millimeter wave (mmWave) and device-to-device (D2D) communication technology will be widely used in 5G mobile communication networks. In scenarios where mmWave small cell and D2D transmission coexist, D2D links mostly reuse frequency resources of the small cell to obtain higher spectral efficiency. However, this will make D2D impose great interference to mmWave small cell. This paper designs a Stackelberg game based interference control scheme with full frequency reuse in the context of D2D underlaying mmWave small cell network. The scheme aims to optimize the transmit power of D2D links, alleviate the interference caused by D2D communication to the mmWave small cell and take full advantage of the bandwidth of the millimeter band. Simulation results show that the proposed scheme converges rapidly, keeps signal to interference plus noise ratio (SINR) in a high range and achieves excellent throughput performance.

With the development of modern network, the demand of users has increased dramatically, and more data and services are required. This has caused tremendous pressure on the Macro-cellular network of infrastructure. Air access has become a new solution for the development of communications. Unmanned aerial vehicle (UAV) is used as an air node to improve coverage and capacity. Based on deep Q-Network (DQN) algorithm and considering the different quality of service requirements of different users, this paper proposes an optimal 3D location planning algorithm. The results show that the use of multiple UAVs can not only provide capacity enhancement, but also meet the different QoS requirements of different users. The average spectral efficiency of the system is increased by 15.5%, and user coverage to meet QoS requirements increased by 25.5%.

As one of the key technologies of 5G, massive IoT environments provide the ubiquitous IoT services. Compared with 4G, its structure is more complex, and it has a large number of deployed nodes. If a failure occurs and can't be alleviated its effect in time, it will lead to a significant drop in network performance. Therefore, the cell outage compensation (COC) problem in massive IoT environments is very important. Although deep reinforcement learning (DRL) has been applied to many scenarios related to the self-organizing network (SON), there are fewer applications for cell outage compensation. In this paper, aiming at the cell outage scenario in massive IoT environments with the goal of maximizing the connectivity of base stations while meeting service quality demands of each compensation user, we present a framework based on DRL to solve it. Specifically, we first allocate compensation users to adjacent BSs by using the K-means clustering algorithm, then use DQN to find the antenna downtilt and the power allocated to compensation users. The simulation result shows that the algorithm converges quickly and tends to be stable, and reach 95% of the maximum target value. It verifies the efficiency of the DRL-based framework and its effectiveness in meeting user requirements and handling cell outage compensation.

As an effective way of utilizing spectrum resources, the application of cognitive radio technology in smart grid has been widely studied, especially in the wireless Neighborhood Area Networks(NAN). The traditional static allocation method does not consider the dynamic changes of the spectrum environment, and cannot effectively utilize the spectrum resources. This paper proposes a dynamic spectrum allocation method in the NAN scenario, to ensure that spectrum resources can be fully allocated and utilized under the constraints of the different requirements for quality of services (QOS). Firstly, a service arrival model is established considering the uncertainty of the arrival of the primary user (PU), so that the number of reserved channels can be dynamically determined based on this arrival probability model. Secondly, we propose a novel dynamic spectrum allocation method using spectrum reservation to improve the reliability of some services by sacrificing the real time of other services. Finally, the optimal number of leased channels can be determined by the stationary distribution in Markov model we proposed, in which we use blocking rate and dropping as the indicators. Numerical results show that the proposed dynamic spectrum allocation method is superior to comparison method, and some meaningful conclusions are drawn after the observation of our experiments and simulations.

Mobile edge computing in 5G network is emerging as a very promising computation architecture by pushing computation and storage closer to end users with both strategically deployed and opportunistic processing and storage resources. Baidu cloud provides network services which can be deployed in 5G network recently. The network services such as weather forecast service and city road map service are typical applications for smart city. We analysis Baidu website data in this paper by our data mining method and related software. Clustering, outlier detection, prediction, and statistical methods are used to evaluate these smart city services, and the analysis result give suggestions to improve design and development of our 5G services (API website).

Energy efficiency is essential for massive machine-type communication (mMTC), because of the limited energy in internet of things (IoT) devices. We consider a two-hop amplify-and-forward (AF) relay communication, and allow IoT devices with inferior channel conditions to connect relays by using device-to-device (D2D) technology. This paper proposes to jointly optimize relay selection, channel allocation and power control, so that the total energy efficiency is maximized while guaranteeing the signal to interference plus noise ratio (SINR) requirements of relays and BSs. The formulated joint optimization problem involves a nonlinear fractional programming (NFP) problem and a user-relay-channel matching problem which is NP-hard. Therefore, we propose a two-stage approach composed of the Dinkelbach method and a hypergraph-based 3D matching (HGM). Simulation results show that the total energy efficiency under the HGM is 8.41% and 59.85% higher than the iterative Hungarian method (IHM) and the minimum zero surface prioritized allocation (MZPA), respectively.

The satellite is a primary means for providing emergency communication backhaul in disaster areas, where large bandwidth is demanded to support communication services in a wide affected area. Millimeter-wave (mmWave) communication with sufficient spectral resources promises significant enhancement to satellite-terrestrial link capacity. However, the alignment delay and mutual interference caused by directional communications with narrow beams severely limit the capacity of mmWave communication. To this end, we optimize the beamwidth to reduce the impact of beam alignment overhead on capacity. Then, considering the multi-user interference between beams, we propose a transmission scheduling scheme based on beam sharing, namely users with strong mutual interference when served simultaneously by independent beams, share the same beam. A heuristic algorithm is proposed to derive the groups of users sharing beams, and their beamwidth. Simulation results show that the proposed scheme achieves considerable capacity enhancement compared to the one-to-one beam occupation scheme (OB) and fixed beam scheme (FB), thus improving the spectrum efficiency of mmWave satellite-terrestrial communication.

Wireless-based neighborhood area network (NAN) plays an increasingly important role in smart grid (SG) since the rapidly emerging smart services and rising number of terminals in grid put forward higher demand for NAN. Considering the differential business demands in NAN, we focus on the wirelessly uplink resource allocation, which allows a trade-off between network throughput and service fairness. For more flexible and coordinated allocation, this paper introduces the cloud-radio access network infrastructure into NAN with orthogonal frequency division multiplexing passive optical network (OFDM-PON) as the fronthaul link, and proposes a corresponding uplink resource allocation method that balances the network throughput and allocation fairness. By utilizing a hybrid intelligent optimization algorithm, composed by adaptive genetic algorithm and binary particle swarm optimization, the optimal throughput-fairness trade-off solution can be obtained with a good convergence ability. Simulation results demonstrate the advantages of our proposed method in both improving network throughput and achieving the trade-off between throughput and fairness.

In cyber-physical power system (CPPS), the communication transmission links carry key services. However, some existing routing approaches cause service routes to be too centralized in the links, which leads to increase the risk of service and network. In order to reduce the risk impact of communication transmission links interruption on the service, this paper proposes a service routing reconstruction approach based on risk balance. Firstly, we analyze the risk transmission of cross-space in CPPS. Then this paper establishes a risk assessment model by characterizing the node risks with the station load pressure, expressing the link risks with the service average communication delay and the service risk balance degree. Further, the improved genetic algorithm is adopted to solve the service routing reconstruction. Finally, based on part of power grid topology from a Chinese province, the simulation results show that the proposed approach can find a route allocation scheme with lower risk value than the original Dijkstra algorithm and genetic algorithm using "roulette wheel" selection strategy, as well as ensuring the stable operation of the power system.

Recently, a new opportunity for on-grid energy saving is enabled by the green network infrastructure sharing. This paper mainly investigates the collaboration between multiple operators to improve the energy utilization in this scenario. Then, an energy-saving management mechanism is proposed to reduce energy consumption and optimize energy utilization. We decompose the problem into two sub problems for base station sleeping and green energy allocation. And the BS sleeping algorithm and the green energy centralized allocation algorithm are respectively proposed to solve them. Comparing with other mechanisms, simulation results show that the proposed energy-saving management mechanism can effectively reduce 65% on-grid energy consumption while guaranteeing the quality of service (QoS) to the user equipment device (UE).

Cognitive-radio-based smart grid networks have been studied recently as an efficient way to overcome radio spectrum shortages, especially in wireless Neighborhood Area Network (NAN). In this paper, we propose the optimal spectrum allocation strategy of cognitive radio NAN Gateway (NGW), which also acts as a spectrum collector by radio sensing and leasing from the providers for a fee. Since the service terminals in grid are heterogeneous based on different QoS requirements and willingness to pay, this paper uses differential pricing and admission control for different terminals to improve the benefits of NGW. The decision-making process for spectrum collection and allocation is modeled as a 4-stage Stackelberg gaming, where the optimal decision of radio sensing, spectrum leasing, admission control and differential pricing are deducted through a reverse derivation. A novel corresponding algorithm is also given to solve these optimal solutions efficiently. The numerical results verify the theoretical work sufficiently, meanwhile some obvious meaningful conclusions are drawn from the observation of numerical experiments.

An attractive architecture called heterogeneous cloud radio access networks (H‐CRAN) becomes one of the important components of 5G networks, which can provide ubiquitous high‐bandwidth services with flexible network construction. However, massive access nodes increase the risk of cell outages, leading to negative impact on user‐perceived QoS (Quality of Service) and QoE (Quality of Experience). Thus, cell outage management (COM) became a key function proposed in SON (Self‐Organized Networks) use cases. Based on COM, cell outage detection (COD) will be resolved before cell outage compensation (COC). Currently few studies concentrate on COD for 5G H‐CRAN, and we propose self‐organized COD architecture and approach for it. We firstly summarize current COD solutions for LTE/LTE‐A HetNets and then introduce self‐organized architecture and approach suitable for H‐CRAN, which includes COD architecture and procedures, and corresponding key technologies for it. Based on the architecture, we take a use case with handover data analysis using modified LOF (Local Outlier Factor) detection approach to detect outage for different kinds of cells in H‐CRAN. Results show that the proposed approach can identify the outage cell effectively.

Hybrid cognitive radio (CR) relays serve cellular users in a two-hop fashion, which jointly utilize both licensed and unlicensed radio spectrums to significantly increase the system capacity. User equipments (UEs) need to be actively associated with the macro-cell BS or CR relays having a more lightly loaded spectrum if the quality of services (QoS) can be guaranteed. To this end, this paper investigates optimal user association for load balancing problem in cellular network with hybrid cognitive radio relays. Firstly, we propose a multi-objective user association optimization model to balance the loads among different tiers while reducing the total resource occupancy. Then, this multiobjective problem is converted into a single one by the linear weighing-sum method and a genetic algorithm is introduced to solve it. The numerical simulation results show that our proposed scheme can obtain more balanced resources occupation, better throughput performance, and lower blocking rate compared with the heuristic and max-power strategies.

The increasing traffic puts high demands on capacity for the next generation mobile networks. The millimeter-Wave (mmWave) communication system offers new opportunities to meet this requirement due to the tremendous amount of avail- able spectrum. However, the massive non-line-of-sight (NLOS) transmissions and the site constraints in urban environment are severely challenging the conventional way of deploying terrestrial low power nodes (LPNs). To address these problems, we introduce the mmWave aerial base station (mAeBS) in next generation mobile networks, which can be quickly and flexibly deployed to enhance the capacity in data traffic bursting areas. To maximize the enhancing effects, an ergodic capacity analytical model of mAeBS is proposed, considering both user distribution and environment conditions. Then an mAeBS 3D placement method based on the model is given. Simulation results show that the proposed method can achieve considerable capacity enhancement and supplement regional coverage as well.

Recently, densified small cell deployment with overlay coverage through Heterogeneous Networks (HetNets) has emerged as a viable solution for 5G mobile networks. Cell Outage Detection (COD) which is the essential functionality in Self-Organizing Network (SON) is designed to autonomously deal with unexpected faults. Typical methods for detecting cell outage are usually based on Manual Drive Tests (MDT). However, it is difficult to detect small cell outage by MDT measurements in HetNets, because the User Equipment (UE) served by these small cells can switch to the macro cell and keep the Reference Signal Received Power (RSRP) and Signal to Interference plus Noise Ratio (SINR) values normal. To resolve this issue, we propose a COD architecture based on the handover statistics. Our model concentrates on cell outage detection in a two-tier heterogeneous network. We process sequential handover statistics spatially and temporally in conjunction with data mining methods. Also, an improved LOF algorithm (M-LOF) is proposed to enhance the detection performance based on handover statistics. To evaluate the system performance, a set of tests has been carried out using some reasonable assumptions and network simulator we designed. The results of simulation show that our system is more effective to detect cell outage in comparison to the architecture using MDT measurements.

LTE-based Public Safety Network (PSN) is a wireless communication network which can provide efficient and reliable communication in disasters or emergencies for disaster relief and public protection. Therefore, ensuring that network congestion will not happen in PSN during an emergency is becoming increasingly important. LTE-based PSN is easy to be congested because part of spectrum resources is compressed to guarantee priority requirements of public safety users. In this paper, we develop a new method namely Selective Admission Control (SAC) mechanism to manage the radio bearers access to the commercial radio for Public Safety (PS) in LTE-based PSN. In the case of emergency, we select the traffic bearer with minimum estimated load increment accessing to the LTE-based PSN. The channel quality of new bearers should be taken into account, which means that in congestion, users who arrive earlier with poor channel quality will be rejected to reserve sufficient resources for users who arrive later with good channel quality. The simulation results show that the SAC mechanism can improve throughput by 36% and lower the rejection rate by 73% at most than reference method based on non-selective access control model, as a result effectively avoiding the network congestion and improving the utilization of spectrum resources for public safety communication.

This paper proposes a novel Gain-Aware Uplink-Downlink(GAUD) jointly resource allocation scheme to maximize the Device-to-Device(D2D) throughput while guaranteeing Quality of Service (QoS) of cellular users. We formulate the global optimization problem as a mixed integer nonlinear programming problem and decompose it into three sub-problems. Firstly, a method of jointly uplink and downlink reuse mode selection is proposed. Based on the throughout gain, each D2D pair is appropriately assigned by either downlink or uplink frequency resource to reuse. Then a heuristic scheduling is designed for fairness channel allocation in order to form D2D users as much as possible. At last, the Lagrangian dual algorithm is developed to solve the optimal power allocation. The simulation results show that our proposed jointly downlink-uplink resource reusing scheme can make the system throughput increased by about 35% and 50% higher than the scheme based on Only Downlink and Only Uplink resource reusing.

To realise load optimisation in cellular and public wireless local area network (WLAN) interworking network, a fairness preferred throughput maximisation (FPTM) optimisation model and a particular algorithm for it named joint UE‐AN association and resource allocation optimisation based on generalised benders decomposition are proposed in the study. The derived solution will give guidance on UE's access selection and resource allocation in cellular network to optimise the overall performance of the interworking network. Simulation results validate the performance on optimising access load in the interworking networks of FPTM model, which can practically enhance the effect of offloading from cellular network to WLAN and improve the total throughput.

This paper proposes a novel indoor-outdoor caching relay system (CRS) and develops the corresponding caching mechanism, which can improve the utilization of wireless resources. It operates in two phases periodically. In Phase I, spectrum resources of the established links between MBS and user equipment (UE) are extracted to support data caching. In Phase II, caching relay system can directly serve indoor users and the fronthaul resources are released to serve other users. Simulations verify that compared with the conventional relay system (RS) the proposed CRS can improve the system throughput by at most 142% in the reusable data caching cases, only at the cost of temporarily suppressing the traffic rate to establish caching links.

As power communication network is more and more important in smart grid, to decrease the failure risk of power system caused by the interruption of communication service, this paper propose a novel routing optimization method based on risk predication for communication services. Firstly, we analyze the probability of failure of communication link and node which is induced by external factors, like winds and snows, equipment failures, and etc. Then based on importance of services, links and nodes, we calculate the risk of failure of communication link and node. Further, we propose three service risk indicators and corresponding improved Dijkstra algorithms to optimize service routing, thus to decrease the network failure probability. Finally, based on part of power grid topology structure from a Chinese province, the simulation results show that the service risk o and the risk of the whole network are also reduced.

Reducing the energy consumption (EC) of base station (BS) is one of the major concerns in wireless cellular networks. Additionally, turning off some underutilized BSs during off-peak period and performing effective compensation without delay are the most efficient way to save energy. However, large-scale energy conservation yet remains to be investigated at macro level. In this paper, to solve the problem that long convergence time and poor convergence precision in the large-scale network, we propose a BS topology-aware based energy-saving (ES) model, whose core is cell adjacency graph (CAG) with vertexes and links representing eNodeBs (eNBs) and their neighboring relationship. In addition, we introduce new metrics, predicted energy efficiency (PEE) and quality of compensation (QoC), as the weights of nodes and links respectively. Consequently, the model transforms the ES problem into average weights maximization in CAG. In view of the model presented, centralized and hybrid algorithms are put forward to solve the problem. Compared with classic distributed algorithm, simulation results claim that our hybrid approach achieves the maximization of ES with guaranteed QoC while our centralized approach maximize the PEE.

Load balancing is one of the key target of LTE Self-Optimization Network (SON). In this paper, we propose a load balancing method for LTE downlink network, namely Load Vector Minimization based Load Balancing (LVMLB) method. Load Vector (LV) is a vector whose elements are the load values of cells and sorted in descending order. The order of LVs is defined by the lexicographical order. The smaller the LV is, the higher the balance degree of cells load will be. As the LV has a lower bound with total load fixed, the balance degree of cells load would reach a local optimal. On this basis, we design the LVMLB algorithm, trying to get the optimal solutions to load balancing problems, the proof of being optimal will also be given in this paper. Simulation scenarios are set in a square part of Macro-Pico mixed HetNets. Simulation results show that LVMLB outperforms the Cell Region Expansion (or Bias) scheme, increasing the capacities of Macro and Pico tiers at the same time, and improving balance degree of cells load, only sacrificing a little QoS performance.

The current power consumption of intelligent terminals are over burden for their battery capacities, which directly restrict the hours used. In order to realize the energy saving of terminals in the LTE-A system, this paper puts forward the concept of virtual cells and a related uplink energy saving mechanism. Virtual Cell's resources and the outage probability of terminals are proposed by this mechanism as constraint conditions. The first step is sectioning off energy saving area in virtual cell. Secondly, we use the uplink CoMP (Coordinated Multiple Points Transmission/Reception) technology to provide diversity gain for the terminals of energy saving area. The third step depends on uplink power control which could adjust the mobile terminals' transmission power for energy saving. The simulation results show that the energy consumption of total terminals will decrease nearly 50% in the virtual cell while its capacity is lower than the 50% of maximum.