In non-terrestrial networks (NTN), passive Internet of Things (IoT) systems typically face challenges including unstable energy supply, limited signal coverage, and complex channel environments. This paper proposes an innovative optimization framework that jointly designs the trajectory and phase configuration of unmanned aerial vehicle (UAV) mounted reconfigurable intelligent surfaces (RIS) based on trust region policy optimization (TRPO) algorithms. The framework encompasses two key technical innovations: first, a transmission protocol based on orthogonal time-slot allocation that maximizes backscatter communication performance while ensuring efficient energy harvesting; second, a position-phase decoupled optimization strategy that employs a weighted geometric center method to determine UAV position, followed by TRPO optimization of RIS phase configuration, effectively overcoming learning convergence difficulties caused by position-dependent channel variations. Simulation results demonstrate that our scheme exhibits excellent performance across various scenarios, providing an efficient and practical solution for passive IoT applications in dynamic NTN.

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.

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.

Due to network congestion, the uplink communication of local models is slow and unpredictable in cloud-based Federated Learning (FL), which will make it difficult to achieve the goal of Hyper Reliable Low Latency Communication (HRLLC) in the 6G era. To minimize communication latency and also to achieve a larger range of user participation, Hierarchical Federated Learning (HFL) has been proposed in academia. Nevertheless, HFL still faces many challenges, such as time-varying channels, user mobility, and data heterogeneity. To address these difficulties, we design a dynamic cell association scheme for multi-cell over-the-air computation-based HFL (MC-AirCompFL). This dynamic strategy innovatively integrates the channel state information (CSI) driving mechanism with the data distribution distance sensing technique to achieve dual-dimensional cooperative optimization. Firstly, we analyze the convergence behavior of MC-AirCompFL at different global communication rounds. Secondly, to relieve the pressure from imbalanced data and nonideal wireless channels, we minimize the optimality gap and data distributed distance by jointly optimizing the cell association and transmission power at user equipment (UE) and the de-noising factors at base stations(BSs). Finally, numerical results based on the MNIST datasets validate the superiority of the proposed scheme over the traditional cell association strategy in the multi-cell FL.

In the high-density data transmission and multi-terminal device environment of 6G edge networks, an efficient routing strategy is crucial. Existing routing methods lack adaptability in the face of network dynamics, which can result in service delays and connection interruptions. To address this issue, this paper proposes an innovative fault-tolerant traffic routing (FTTR) mechanism. Leveraging Message Passing Neural Networks (MPNNs) and Deep Q-Network (DQN), FTTR can deeply explore the interdependencies between links and make precise routing decisions. Extensive experiments demonstrate that FTTR mechanism achieves an average 27.72% increase in network load capacity compared to mainstream routing strategies. Its generalization and robustness significantly outperform Proximal Policy Optimization (PPO), clearly demonstrating its adaptability and stability to network dynamics.

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.

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.

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.

With the continuous functional enhancement of network services, a service usually presents a directed acyclic graphic (DAG) structure. This paper models the DAG task scheduling problem as a multi-objective optimization problem to balance the task execution efficiency, network traffic, and system load balance in componentized task deployment. To produce an instant decision, we propose the Cloud-edge Collaborative Task Scheduling (CCTS) Algorithm based on hybrid reward architecture deep reinforcement learning (DRL). Specifically, to reduce the redundancy of the state space of the Markov decision process, we use directed graph convolution networks and graph convolution networks (GCN) to embed the directed task graph and undirected network graph, respectively. Simulation results show that the proposed method outperforms the compared convolutional neural networks and GCN-based DRL schemes in reducing the system latency, energy cost, network traffic, and load balance.

In the upcoming B5G/6G era, devices will generate a amount of heterogeneous data at the network edge. As a paradigm for implementing distributed and privacy-preserving machine learning (ML), Federated Learning (FL) has drawn great attention to secure data sharing in edge networks. However, FL takes too much time and communication resources to train and transmit model parameters, which is unaffordable for edge devices with limited capabilities. To achieve a trade-off between resource and efficiency, it is crucial to select appropriate training nodes. While existing works about node selection focus on the resources allocation and pay less attention to the node mobility and seamless service. In this paper, we considering mobility, computation capability, and transmission power of training nodes to minimize the FL system cost. We propose an algorithm and mechanism respectively for different scenarios of node speed. An algorithm based on Deep Reinforcement Learning (DRL) matches with stationary and low-speed training nodes. A heuristic mechanism is used for nodes with high mobility. Simulation results show that the proposed schemes select appropriate training nodes effectively, and reduce the system cost by up to 20%.

To both take the advantage of wired and wireless networks, the burgeoning mobile edge computing (MEC) technology is integrated into fiber-wireless (FiWi) network to support the cost-effective deployment of Internet of Things (IoT). However, the trusted model training, efficient task computing, reasonable comprehensive energy consumption and different quality of services, are still the key problems to be solved. Thus, we introduce the federated reinforcement learning (FRL) to the framework to jointly optimize the accessing mode selection, computation offloading decision and transmission power allocation without the leakage of users’ privacy. Then, we further design a twolayer FRL algorithm based on reputation value to respectively realize the protection of user privacy and efficient optimization of the global model. The simulation results demonstrate that our proposed method outperforms others in balancing energy consumption, reducing service delay, as well as providing differentiated services.

To provide intelligent and accurate assistant driving services in smart city, as well as ensure the security and tamper proof of vehicle data, this paper build a deep reinforcement learning (DRL) and main-side blockchain empowered service framework. By storing driving data and vehicle information on the sidechain, while deploying index information on the mainchain, the main-side blockchain structure can enhance the scalability of blockchain, decrease the communication overhead, improve consensus efficiency, and avoid the leakage of data between different sidechains. However, the resource limited vehicles on sidechain cannot process numerous computation-intensive mining tasks in time, resulting in high service delays. Thus, this paper integrate mobile edge computing with blockchain system to design a double-layer mining service offloading mechanism, allowing the edge nodes and neighboring vehicles to form a cooperative mining network and collaboratively participate in mining process with specific offloading rates. The first layer uses Asynchronous Advantage Actor-critic (A3C) algorithm to efficiently offload partial mining task from the task vehicle to the road side unit (RSU), and the second layer applies double auction to specifically obtain the offloading rates from RSU to multiple service vehicles. Simulation results demonstrate that, our proposed mechanism outperforms other compared algorithms in the average profit and consensus delay.

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.

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.

Network delay is a crucial indicator for realizing delay-sensitive task offloading, network management, and optimization in B5G/6G edge computing networks. However, the delay prediction for edge networks becomes complicated due to diverse access strategies and heterogeneous services’ storage, computing, and communication resource requirements. Current GNN-based delay prediction models such as RouteNet and PLNet lack the ability to express the complex associations between links and paths, so the predicted delay is not accurate. In this paper, we propose a novel end-to-end delay prediction model named MixerNet for edge computing, which is based on the mixed multi-layer perceptron (MLP). In this model, a mixed MLP architecture is applied to represent the association between links in the network topology and various paths. Observing that each link may have different effects on various paths, a weight matrix is then defined and multiplied by the path matrix to express it. Thus, a complete mapping frame from network characteristics (e.g., traffic intensity and routing schemes) to delay indicator is constructed. Finally, we perform extensive experiments on NSFNET and GEANT2 datasets and regard RouteNet as the baseline model. Experimental results show that MixerNet can accurately predict end-to-end delay results on various network topologies and the mean absolute error is merely about 0.36%. MixerNet also outperforms the baseline model in most evaluation indicators, especially the mean square error has a 3-fold decrease in NSFNET.

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.

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.

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.

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.

With the continuous development of IoT, smart grid receives widespread attention. Aiming at the problem of poor performance of the Rake receiver in a wideband micro-power wireless communication system under a low signal-to-noise ratio, the non-stationarity of the Chirp signal used in the system and the adaptability of the non-stationary signal of the denoising method are proposed. A complementary empirical mode decomposition (CEEMD) combined with wavelet threshold denoising algorithm to improve the receiver's signal-to-noise ratio. The CEEMD algorithm can not only handle non-stationary signals well, but also overcome the modal aliasing phenomenon. However, using only the CEEMD algorithm, some effective information will be lost when removing high-noise high-frequency IMF components. Therefore, this paper combines CEEMD decomposition with wavelet threshold denoising, and performs wavelet threshold denoising processing on high-frequency IMF components decomposed by CEEMD to extract useful information from high-frequency components. Through matlab software simulation, the signal-to-noise ratio has been improved by about 1dB.

IoT is widely used and plays an critical role in transforming traditional industries, leading emerging industries, improving people's lives, and providing national security. From the physical layer of communication, it is particularly important to ensure the reliability of transmission in the IoT. Polar code has outstanding performance, but its coding structure determines that the length of polar codes must be the power of 2. The structure of polar code is more flexible with the existing puncturing schemes, but the decoding performance of these schemes varies with the number of puncturing bits. The decoding performance of the puncturing scheme in C0 mode drops sharply when the number of puncturing bits is large, and the decoding performance of the puncturing scheme in C1 mode is poor when the number of puncturing bits is small. In this paper, an improved polar code puncturing scheme is proposed based on the forward sequential puncturing scheme and the bit reversal puncturing scheme in the C0 puncturing mode. Compared with the traditional puncturing scheme, the simulation results indicated that the proposed scheme solves the problem of decoding performance degradation when the number of puncturing bits is too large in C0 mode; compared with the scheme in C1 mode, the scheme in this paper has a decoding performance gain of about 0.2dB when the block error rate reaches 10-3, which can also be achieved in high or low code rate.

The beam coverage directivity of mmWave communication brings great challenges to the application research of high-speed Internet of things (IoT) terminal devices, such as unmanned vehicles and unmanned aerial vehicles (UAV). Most of the existing millimeter wave beam tracking algorithms aim at AOA /AOD (Angles of Arrival/Angles of Departure) for continuous tracking estimation. However, the estimation error will increase rapidly with the increase of AOA/AOD change speed. Inspired by Auxiliary Beam Pair (ABP) algorithm, a robust two-stage beam tracking algorithm is proposed in this paper. First, AOA/AOD is estimated by the Unscented Kalman filter (UKF) algorithm, and then the AOA/AOD is modified by the improved ABP algorithm. The simulation results show that when AOA/AOD changes at a speed greater than 0.25 degrees per time slot, the proposed two-step beam tracking algorithm significantly reduces the estimation error and effectively increases the robustness of the same type of algorithm.

In order to resolve the issue of high-reliability communication over long distances, the latest Internet of Things (IoT) technology plays a key role. Effective channel estimation is the key to the overall system implementation. Aiming at the problem that the Least Square (LS) estimation algorithm in IoT is affected by noise and estimation accuracy is relatively poor. To solve this problem, an improved LS estimation algorithm in view of wavelet denoising is proposed. At first, the improved algorithm uses the LS algorithm to perform the initial estimation of the channel, and then shifts to the wavelet domain for threshold denoising. By improving the denoising threshold function, the noise is better eliminated and the estimation accuracy is improved. The bit error rate (BER)and the mean squared error (MSE) of the algorithm were simulated by MATLAB. The simulation consequent indicates that the performance of channel estimation algorithm in the paper is notably better than LS estimation algorithm, LS based on DFT denoising, and soft threshold wavelet denoising algorithm. And compared with the soft threshold wavelet denoising algorithm, the SNR of the improved algorithm is increased by about 2 dB for the same BER.

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 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.

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.

With the proliferation of bandwidth-demanding mobile applications in the era of 5G, the aggregation of a few users may lead to extremely high load in cellular base stations, producing traffic hotspot in wireless networks. Therefore the higher requirement is imposed on the flexibility of a 5G network, namely the capability of performing rapid capacity enhancement in hotspot area, which makes hotspot localization and critical prediction functions. In this paper, we proposed to localize hotspots with Gaussian Random Field (GRF)-based spatial traffic density model deduced from load data of base stations, together with the prediction with Holt-Winters. We measured the spatial traffic in a specific area within a short time span and forecasted the spatial traffic density distribution. Numeric results show the proposed approach can localize hotspot efficiently, and during traffic peak hours, hotspot prediction is of high success rate.

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 power SCADA system is designed to ensure the safe operation of the power system. The SCADA communication network as an information exchange carrier between remote terminal units and master stations, is the key part of the SCADA system, and it has a high requirement for security. However, due to the wide distribution of the network and the interconnected network structure, it is susceptible to risks. So there is an urgent need for accurate and real-time risk prediction. In this paper, we propose a risk prediction model based on entropy-gray model, where the gray model is used to predict the values of the network risk indexes, and the entropy method is to determine the weight of those risk indexes. Finally, the overall risk value of the network is decided with analytic hierarchy process. Simulation results show that the proposed entropy-gray method can achieve accurate and timely risk prediction.

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.

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.

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.