This paper presents FDC-Ground, a reinforcement learning framework that addresses the high-cost, low-signal challenge of GUI grounding training. The framework introduces two core contributions: (1) the Exponentially Decayed Distance Reward (EDDR), which provides resolution-robust and continuous feedback for position predictions, and (2) the Fact-Aligned Dynamic Completions Pruning (FDC-Pruning) strategy, which selectively retains completions whose advantage signs align with factual correctness, thereby reducing computational overhead while enhancing gradient quality and training stability. Using only 3.2K training samples and a single epoch, our 7B-parameter model achieves 88.3% and 91.0% accuracy on ScreenSpot and ScreenSpot-v2, outperforming several RL-based models such as UIShift and SE-GUI. Our 3B-parameter model based on Qwen2.5-VL-3B surpasses its original performance by +26.6%, demonstrating the effectiveness of our reward design and pruning strategy under low-resource conditions. Furthermore, the proposed FDC-Pruning strategy achieves a 1.18× training speedup and a +5.9% accuracy improvement over standard GRPO, and expanding the exploration space to 4× yields an additional +10.5% gain, confirming both the scalability and the training efficiency of our approach. These findings highlight that combining EDDR with FDC-Pruning offers a practical path toward scalable and efficient RL-based GUI grounding, even in low-resource settings.

Communication overhead represents a primary bottleneck in distributed deep learning, impeding training scalability. Although existing gradient sparsification techniques reduce network traffic, they introduce critical limitations: they fail to optimize intra-node data paths and are incompatible with efficient, decentralized Allreduce operations. To address these issues, we propose D-DOSA, a DPU-based communication offloading framework. D-DOSA incorporates two key innovations: 1) D-DO, an architecture that establishes a direct GPU-DPU data path to offload data loading and intra-node communication from the host CPU; and 2) D-SA, a novel sparse Allreduce algorithm that, for the first time, enables compatibility between sparse tensors and high-performance, ring-based communication. We evaluated D-DOSA on a 8-node, DPU-enabled cluster using representative models including VGG, LSTM, and BERT. Experimental results demonstrate that our framework accelerates training by up to 1.32x compared to the state-of-the-art sparse training baseline, without compromising accuracy. Ultimately, D-DOSA shows that co-designing data-flow architectures and communication algorithms on the DPU resolves key bottlenecks in sparse training and presents a viable path toward scalable performance in larger systems.

At present, most edge servers adopt popularity-based caching strategies, prioritizing the caching of content with the highest overall popularity on user-side edge servers. However, in social network scenarios, user interests and preferences are highly personalized and dynamically changing. This results in existing caching strategies often failing to adjust the cache placement of content in real time according to individual user preferences, leading to suboptimal edge cache hit rates, increased user request response latency, and a decline in quality of service (QoS) for the user experience. To address this issue, we propose a new caching strategy tailored for large-scale social content based on knowledge graph neural network (KGNNC). First, an entity-relation KG is constructed from users' triple data$(\boldsymbol{h}, \boldsymbol{r}, \boldsymbol{t})$on social platforms. Next, a graph convolutional neural network is employed to iteratively aggregate feature information from neighboring nodes and learn vector representations of the nodes. Finally, a reinforcement learning-based algorithm is utilized to determine the optimal caching location for content. Experimental results on multiple public datasets demonstrate, compared with several existing baseline algorithms (least recently used, least frequently used, neural network-based collaborative filtering, KG-DQN, and CAFR), the algorithm proposed in this article achieves a reduction in the average response latency of requests by 38.26%, 34.46%, 13.56%, 6.49%, 4.19% on MovieLens 1M dataset and 30.31%, 27.59%, 14.11%, 5.83%, 4.78% on last FM dataset, respectively. Meanwhile, experiment results demonstrate that the caching hit rate is increased by 24.2%, 25.1%, 14.4%, 6.5%, 3.53% on MovieLens 1M and 39.51%, 39.05%, 23.40%, 10.66%, 8.67% on last FM compared with the four existing baseline algorithms, respectively. These results verify the effectiveness of our algorithm in reducing response latency of user requests and improving caching hit rate of edge servers in social networks.

The evolution of 6G networks introduces heterogeneous services with stringent computing and latency demands. However, constrained edge resources, intricate task dependencies, and dynamic network fluctuations intensify resource contention, increasing the risk of node faults and service interruption. Current fault-tolerant methodologies lack the necessary adaptability to handle the coupled complexity of task interdependencies and volatile resource states, leading to sub-optimal decisions or excessive system overhead. To address these challenges, this paper innovatively proposes TransDiffuse—an intelligent preemptive service migration framework for 6G edge networks. First, the framework employs a Transformer-GAT hybrid model to capture long-range temporal load dynamics and spatial topological constraints, enabling accurate failure prediction. Second, to navigate the trade-off between migration overhead and service robustness, we devise a diffusion-based decision module. This module efficiently explores the discrete combinatorial solution space to synthesize near-optimal service orchestration. Furthermore, a comprehensive evaluation system is constructed to validate the effectiveness of TransDiffuse. Experiments demonstrate that TransDiffuse reduces energy consumption by 32.4%, decreases task completion time by 25.6%, and improves resource balance by 18.7%, while keeping service violations below 5%. This work achieves joint optimization of energy, delay, and resource efficiency, offering a robust solution for resilient service orchestration in 6G edge networks.

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.

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.

To resolve the problem of power allocation (PA) in cell-free massive MIMO systems, GNN stands out among various machine learning methods due to its utilization of network topology and strong generalization capabilities. In cell-free systems, there is a competitive relationship among UEs served by the same AP, a cooperative relationship among APs serving the same UE, and an interference relationship among different communication links involving different APs and UEs. However, existing GNN methods do not fully utilize this knowledge. In this paper, we model the channels as nodes, with the competition, cooperation, and interference relationships represented as three types of heterogeneous edges. The proposed HAtt3EGNN employs mechanisms of hierarchical attention and edge updating. The hierarchical attention mechanism takes into account both the importance of neighbors along the same meta path and the importance between different meta paths; The edge updating mechanism accelerates the propagation of heterogeneous edge information as we have defined. Benefit from the robust representational capacity of this modeling approach, our proposed graph neural network model demonstrates excellent stability and convergence when employed in an unsupervised learning context. Simulation results indicate that our proposed method outperforms existing GNN methods on the sum-SE performance and approaches the performance of optimal mathematical algorithms.

We propose a method that utilizes Generative Diffusion Model (GDM), a sequence encoding algorithm, and an image decoding algorithm to jointly assist Convolutional Neural Network (CNN) in estimating the First Arrival Path (FAP) delay. In the task of utilizing CNN for FAP delay estimation, there is a challenge of insufficient dataset. To mitigate this limitation, we augment the dataset for CNN training using GDM. Specifically, we employ the Cross-Correlation (CC) algorithm to extract delay features from wireless signals, thereby generating Cross-Correlation Function (CCF) sequences that serve as the input data. To capitalize on the capabilities of GDM in processing image data, we use the sequence encoding algorithm to encode CCF sequences into images, which are then used as dataset for training the GDM. After training, GDM generates new image data. The image decoding algorithm can decode the generated image data back into sequences, which are used as augmented data for CNN training. The results of the simulation experiments demonstrate that the sequence encoding algorithm and the image decoding algorithm can effectively assist GDM in augmenting the data for CNN training in the task FAP delay estimation.

In cell-free multiple-input multiple-output (MIMO) systems, beam alignment is critical to achieving high spectral efficiency and reliable communication. However, traditional optimization-based methods often suffer from high computational complexity and sensitivity to dynamic channel conditions, especially in decentralized architectures with distributed access points (APs) and mobile users. To address these challenges, a novel scheme for beam alignment optimization that leverages generative diffusion models (GDMs) is proposed in this paper. By learning the underlying distribution of optimal beamforming configurations from historical channel state information (CSI) and user positioning data, the proposed approach generates high-quality precoding and combining matrices that minimize beam alignment errors while maximizing signal-to-noise ratio (SNR). The system model integrates a conditional diffusion process, where CSI and user locations serve as input to guide the generation of beamforming solutions. The framework operates in two phases: an offline training stage that learns the latent distribution of optimal beam alignment, and an online inference stage that rapidly adapts to real-time channel variations. To ensure practicality, the design incorporates power constraints and feedback mechanisms to dynamically refine beam configurations. Simulations demonstrate significant improvements in beam alignment accuracy and communication performance, particularly in high-mobility scenarios.

With the advancement of industrial intelligence, control systems are increasingly demanding higher real-time performance and accuracy, particularly in the face of growing network data volumes. Semantic communication has shown considerable promise in improving transmission efficiency and minimizing latency, making it a promising approach for future communication systems. However, due to the lack of a unified and comprehensive theoretical framework, the relationship between semantic communication and control performance remains unexplored, as well as semantic-aware resource allocation. To deal with these challenges, we first analyze and derive the relationship between communication delay and control stability and define the transmission delay in semantic communication systems using the DeepSC model as an example. Then, we formulate a semantic-aware resource allocation problem aimed at maximizing semantic similarity through joint optimization of channel assignment, power allocation, and semantic compression ratio across multiple devices. To overcome the inefficiencies of traditional mathematical methods and deep reinforcement learning (DRL) algorithms in solving complex optimization problems, we propose a two-stage Generative Diffusion Model (GDM)empowered DRL algorithm framework which decouples the solution space by leveraging GDM to generate resource allocation scheme and optimal semantic compression parameter is solved subsequently with exhausted searching. The simulation results confirm the effectiveness of the proposed two-stage approach and highlight the advantages of the two-stage GDM-enhanced DRL algorithm over both the single-stage approach and the standalone DRL algorithm.

With the development of immersive applications, eXtended Reality (XR) has emerged as a key application in scenarios such as Industrial Internet of Things (IIoT) and smart healthcare, where the demand for low-latency and high-bandwidth network performance is particularly urgent. To meet the low-latency requirements of multiple users in multiple XR services, service caching and wireless resource scheduling have become essential approaches. However, most existing studies focus on optimizing only one of these two aspects, ignoring the coupling relationship between the two, which makes it difficult to comprehensively improve the quality of XR services. To address this challenge, we propose a joint service caching and wireless resource scheduling method for low-latency XR delivery. By comprehensively considering user requests and service features, we formulate a joint optimization model with the objective of minimizing end-to-end latency. To solve this model, we design a Transformer-Enabled Multi-Agent ActorCritic (TMAC) algorithm. Specifically, we first introduce Graph Neural Network (GNN) to capture network features. Then, we model each XR service as an agent and design a Transformer-based actor network to make the service caching decision, while incorporating a global actor network based on Hypergraph Neural Network (HGNN) to generate resource scheduling decisions. This method achieves collaborative optimization of service caching and wireless resource scheduling while enhancing system flexibility and decision-making efficiency. Compared to various baseline methods, the proposed algorithm reduces the average service latency by approximately$\mathbf{1 5. 8 8} \boldsymbol{\%} \mathbf{- 3 1. 2 9} \boldsymbol{\%}, \mathbf{2 6. 7 3} \boldsymbol{\%} \mathbf{4 5. 0 9 \%,} \mathbf{2 9. 9 5 \%} \boldsymbol{-} \mathbf{4 9. 4 8 \%}$respectively, significantly improving the QoS (Quality of Service) and low-latency guarantee capability of XR service delivery in multi-user, multi-service scenarios.

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.

In recent years, the rapid development of Internet of Things (IoT) technology has led to a significant increase in the amount of data stored in the cloud. However, traditional IoT systems rely primarily on cloud data centers for information storage and user access control services . This practice creates the risk of privacy breaches on IoT data sharing platforms, including issues such as data tampering and data breaches. To address these concerns, blockchain technology, with its inherent properties such as tamper-proof and decentralization, has emerged as a promising solution that enables trusted sharing of IoT data. Still, there are challenges to implementing encrypted data search in this context. This paper proposes a novel searchable attribute cryptographic access control mechanism that facilitates trusted cloud data sharing. Users can use keywords To efficiently search for specific data and decrypt content keys when their properties are consistent with access policies. In this way, cloud service providers will not be able to access any data privacy-related information, ensuring the security and trustworthiness of data sharing, as well as the protection of user data privacy. Our simulation results show that our approach outperforms existing studies in terms of time overhead. Compared to traditional access control schemes ,our approach reduces data encryption time by 33%, decryption time by 5%, and search time by 75%.

In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis.

Anomaly detection in blockchain transactions faces several challenges, the most prominent being the imbalance between positive and negative samples. Most transaction data are normal, with only a small fraction of anomalous data. Additionally, blockchain transaction datasets tend to be small and often incomplete, which complicates the process of anomaly detection. When using simple AI models, selecting the appropriate model and tuning parameters becomes difficult, resulting in poor performance. To address these issues, this paper proposes GANAnomaly, an anomaly detection model based on Generative Adversarial Networks (GANs) and Autoencoders. The model consists of three components: a data generation model, an encoding model, and a detection model. Firstly, the Wasserstein GAN (WGAN) is employed as the data generation model. The generated data is then used to train an encoding model that performs feature extraction and dimensionality reduction. Finally, the trained encoder serves as the feature extractor for the detection model. This approach leverages GANs to mitigate the challenges of low data volume and data imbalance, while the encoder extracts relevant features and reduces dimensionality. Experimental results demonstrate that the proposed anomaly detection model outperforms traditional methods by more accurately identifying anomalous blockchain transactions, reducing the false positive rate, and improving both accuracy and efficiency.

Recently, with the advancement of technologies such as virtual reality and the metaverse, remote 3D reconstruction has gained increasing attention. However, achieving fast and high-quality 3D reconstruction in complex communication environments remains a major challenge. To address this, we propose R-SVRSC, a semantic communication system for single-view 3D reconstruction based on 3D Gaussian Splatting (3D GS). Specifically, the transmitter extracts and transmits semantic information from a single image, instead of transmitting complex 3D data. At the receiver, a Semantic-to-Gaussian Mapper (SGM) directly maps the received deep semantic features into 3D Gaussians, which are then rapidly rendered using a Gaussian rasterizer. Furthermore, to combat the performance degradation caused by multipath fading and channel instability, we introduce a RIS-assisted enhancement to the proposed semantic communication system. A deep learning (DL)-based RIS phase shift prediction module is integrated into the transmitter and jointly trained with the entire system in an end-to-end manner, enabling intelligent reconstruction of the wireless channel. Experimental results show that the proposed system achieves efficient rendering and consistently high-quality 3D reconstruction under low signal-to-noise ratio (SNR) conditions across various real-world channel scenarios.

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.

The rapid development of autonomous vehicles and smart city has led to an exponential increase in data generation within Intelligent Transportation Systems (ITS). However, comprehensive extraction and utilization of these data are severely hindered by communication and energy constraints, security and privacy concerns, vehicle mobility limitations, and spatial distribution challenges. Using 6G and Digital Twin (DT) technologies offers a promising solution to these problems. In this paper, we propose a DT-based model training architecture for vehicular networks and introduce Federated Learning (FL) to preserve data privacy. While distributed model training and parameter transmission introduce challenges in delay and energy consumption, which conflict with real-time service requirements in ITS. In addition, the quality of the data and the processing capability of each vehicle varies widely, which will affect the efficiency of data sharing and model accuracy. Therefore, it is vital to select appropriate training nodes and optimize resource allocation under the constraints of task delay and energy consumption. We formulate an optimization model to improve the selection of FL participating nodes and energy management strategies, aiming to maximize accuracy while minimizing energy consumption. We then develop a DT-assisted deep reinforcement learning (DRL) method. Experiments show that our scheme achieves higher training accuracy and energy efficiency compared to the benchmark.

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.

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets.

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.

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.

Augmented Reality (AR) applications are latency-sensitive and contain significant heterogeneous content, such as mixed static objects and interactive data. Relying solely on real-time edge caching makes it difficult to meet the latency requirements of AR, disrupting user’s immersive experience. In addition, the operator can motivate terminal caching foreground content and reduce transmission costs through device-to-device (D2D). Therefore, we proposes a cloud-edge-terminal collaborative proactive caching and differentiated delivery mechanism of heterogeneous content, which reduces service response latency and improves comprehensive revenue through efficient edge collaboration methods, accurate heterogeneous content pre-caching strategies, and differentiated delivery mechanisms. Firstly, we synthetically considers AR user’s service response latency and operator’s comprehensive revenue, proposing a user behavior and resource-aware edge collaborative service domain construction method to improve the collaborative service capability of edge nodes. Then, it proposes a pre-caching algorithm for heterogeneous content based on foreground/background content separation, user preference prediction, and storage space partitioning to improve cache utilization in the edge network. In particular, a D2D-assisted differentiated delivery strategy is designed to improve service response speed and overall revenue. The numerical results show that the proposed mechanisms are better than other solutions and can improve cache hit rates and operator’s comprehensive revenue.

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.

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.

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%.

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.

To deal with the explosion connections and data volume for emergency communication or hot spot capacity enhancement with massive Internet of Things (IoT) devices, deploying aerial base stations (AeBSs) on unmanned aerial vehicles (UAVs) to generate heterogeneous space–air–ground networks is considered to be a quite effective method. However, the flying AeBSs and back-hauling to existing heterogeneous networks (HetNets) lead to network energy consumption a key point. To ensure the energy-efficient operation of space–air–ground networks for smart IoT applications, we put forward the cluster-based HetNets energy-efficient resource allocation mechanism (CHERA). The scheme first divides the entire network into multiple independent BS clusters with the K-means++ algorithm for distributed energy efficiency (EE) optimization. Then, we propose a greedy BS sleeping strategy and a Lagrangian-dual-based optimal power allocation algorithm for the maximization of EE in each BS cluster. The EE optimization of space–air–ground IoT networks is implemented under the self-organizing network framework to make sure of the efficient and reliable operation of the network. Simulation results indicate that energy consumption is effectively decreased with the mechanism. It boosts the EE of space–air–ground networks by 23.8% compared with a baseline algorithm in which BSs are all in active mode with no power optimization. The result is expected to be useful for achieving future green space–air–ground networks IoT applications.

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.

With the development of 5G/6G networks, the number of wireless users is growing exponentially, and the application scenarios are increasingly diversified. Using unmanned aerial vehicles as base stations (UAV-BSs) to serve ground users has become a trend for wide area coverage and capacity enhancement for rapid access of service in 6G networks. However, as UAV-BSs have limited energy or battery storage, solutions to optimize energy efficiency while providing high-quality services are necessary. Therefore, this paper mainly concentrates on the energy-efficient deployment of coverage-aimed UAV-BSs (Co-UAV-BSs) and capacity-aimed UAV-BSs (Ca-UAV-BSs) for the coverage and capacity enhancement of ground communication under disaster areas or burst data traffic. First, Co-UAV-BSs are deployed with DQN algorithm to to get the UAV-BSs’ optimal flight paths, which mainly adopted to detect out of service users in such areas. Then the users are completely clustered based on the detection results. After that, Co-UAV-BSs and Ca-UAV-BSs are deployed hierarchically based on the user distribution and sought to optimize the energy efficiency with acceptable user services. Still, DQN algorithm and the A3C algorithm are used for obtaining all the UAV-BSs’ location deployment and users’ best connections. The simulation results show that the dynamic flying path requires less energy than the fixed path for user detecting. For the coverage and capacity enhancement, it reveals the solution we proposed could provide high-quality service for users with high energy efficiency comparing to traditional algorithms.

Network function virtualization (NFV) is an emerging technology that uses virtualization technology to provide various services in enterprise networks and reduce costs. However, in cloud edge networks, effective virtual network function (VNF) configuration is particularly difficult, and the system design needs to consider the reliability and energy-saving while meeting the requirements of Quality of Service (QoS). This paper uses the binary integer programming (BIP) model to study the service function chain (SFC) orchestration problem, and designs a federated deep reinforcement learning SFC orchestration algorithm (FDOA). With this method, energy consumption can be reduced and the QoS of users can be improved. In addition, considering the limitations of local deep reinforcement learning (DRL) model training, this paper proposes a federated DRL algorithm to help obtain a more robust model, and simultaneously improve the convergence speed of the model. Among them, we introduce reputation theory during model training to evaluate the reliability of the nodes carrying the DRL model, avoiding the influence of unreliable models on the training effect. Finally, the simulation results show that FDOA has better performance in training time and end-to-end delay compared with other existing algorithms.

5G and beyond network will support vertical industry applications, and the resource requirements of each service vary widely. The introduction of network slices provides great flexibility to the network, which can realize the differentiated customization requirements of service. However, while determining how to intelligently orchestrate the network slices is an important challenge, current solutions rarely treat multiple customized requirements of delay, bandwidth, load balancing, and slice isolation. In this article, network slice orchestration is considered from the perspective of slice isolation and cloud-edge collaboration. First, differentiated isolation level requirements are restricted to constraints, the customized isolation is realized. Second, bandwidth is saved and network latency is reduced via the collaboration of cloud and edge data centers. In addition, exclusive orchestration optimization objectives that match various service needs are proposed to distinguish the specific requirements of different slices. Finally, two deep reinforcement learning-based algorithms are proposed. The experimental results demonstrate that the proposed algorithms can optimize the objectives while ensuring differentiated isolation levels. For typical slices, the proposed algorithms respectively reduce bandwidth consumption by about 29% and 64%, reduce slice delay by about 14% and 70%, and optimize load balancing by about 17% and 23%.

The deep flow inspection (DFI) can identify abnormal traffic to avoid the data congestion caused by the sudden increase of traffic, which can maintain the stability of 6G network. However, the traditional DFI cannot protect the privacy of clients. This paper proposes a deep flow inspection method based on the horizontal federated learning to achieve the traffic identification locally, which reduces the risk of data leakage. Besides, a lightweight CNN model, Simplified-MobileNet, is proposed to realize the effective traffic identification under the limited hardware environment. Federated aggregation algorithm FedAvg is also applied to promote the communication efficiency during model training. The experimental results demonstrate that the Simplified-MobileNet decreases the training time per round by about 15%, and compared with the standalone mode, the FedAvg algorithm can achieve a higher training accuracy with a limited communication time.

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.

The demand for satellite relay service is increasing, while the satellite network resources are limited and unevenly distributed, which pose a great challenge to task scheduling of tracking and data relay satellites. Most existing relay scheduling models are based on static antenna setup time, which has limitations in practical applications and leads to ineffective utilization of satellite resources. This paper models the task scheduling problem based on dynamic antenna setup time and splittable tasks, which maximizes the total scheduled task number and minimizes the total antenna setup time. We also propose a two-stage insertion heuristic to solve the problem. The experimental results show that the proposed algorithm can significantly improve the total scheduled task number, total antenna setup time and effective time window utilization compared with traditional methods.

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.

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.

Multiaccess edge computing (MEC) provides users with better Quality of Experience (QoE) via offloading tasks to the nearby edge. However, the emergence of new Internet of Things applications with multiple tasks and repeated requests brings redundant computation and transmission to the edge. Meanwhile, the current offloading method based on deep reinforcement learning (DRL) has low sampling efficiency and slow convergence issues for training in a changing environment. Therefore, improving QoE of computation offloading services is still the ultimate challenge. In this article, we devise a collaboration of computing and cache resources among multiple edge nodes, which could reduce redundant computation and transmission. Specifically, we formulate a cache-assisted computation offloading process as a QoE-aware utility maximization problem based on multidimensional indicators. Then, we propose a cache-assisted collaborative task offloading and resource allocation strategy to solve it. This strategy is decomposed into two subproblems. First, to determine and obtain task cache state, we propose a collaborative task caching algorithm, which can improve the hit rate of tasks while balancing network overhead. Second, to acquire offloading and resource allocation decisions efficiently, we propose a metareinforcement learning-based cache-assisted computation offloading method (MCCOM), which can achieve rapid offloading decisions with a few gradient updates and samples. The optimization problem was transformed into multiple Markov decision processes (multiple MDPs). The improved learning process includes metapolicy learning that adapts to multiple Markov decision processes (MDPs) and policy learning for a specific MDP based on metapolicy. Simulation results show that our proposed method outperforms baselines in terms of QoE indicators while achieving rapid convergence and decisions.

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 development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement.

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

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.

5G enables many industries, and each industry's application requirements vary greatly. Today's “one-size-fits-all” network architecture approach no longer meets the needs at the same time. The introduction of network slicing brings great flexibility to the network, so that the network can be customized, deployed and dynamically guaranteed. However, how to orchestrate the functions of network slices according to the needs of business scenarios is an important challenge for slice operation. In order to solve the problem that the existing methods can hardly distinguish the differential requirements of different slices for delay, bandwidth and node load balancing, this paper proposes an exclusive orchestration optimization goal to match the business requirements, and establishes a business demand-oriented network slice orchestration problem model. Then, aiming at the typical slices, a business-oriented slicing algorithm based on DQN (BOSAD) is proposed. In this algorithm, we propose the strategy of cooperating the core data center and the edge data center, which can effectively save bandwidth and reduce network delay. The experimental results show that the proposed BOSAD saves the consumption of bandwidth resources, reduces the average slice delay and optimizes node load balancing.

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.

With the development of information technology, the network consists of various proprietary hardware devices, and the use of these devices brings problems. To solve problems, network function virtualization is proposed, which decouples the software and hardware in the network, and deploys the existing network function devices to a common physical platform. However, network virtualization needs will inevitably face reliability problems during resource virtualization and service function chain deployment. This article proposes a service function chain reliability evaluation method and reliability optimization algorithm. The composition relationship and reliability influencing factors of service function chain were analyzed, including resource preemption, common cause failure, fault recovery and redundant backup. The service function chain was modeled as a Petri net model, and reliability evaluation results related to execution time were obtained. Based on the reliability assessment results, a VNF migration strategy is designed, with reliability as the optimization goal while considering costs. Simulation results show that, compared with the reliability optimization strategy based on backup, our algorithm costs less and reduces the impact of resource preemption on service reliability.

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.

NFV separates network functions from hardware-dependent middle boxes, which can significantly reduce costs and improve network management flexibility. It has been widely used in operator networks. However, due to traffic fluctuation in the network, using virtual network functions to provide flexible services is still challenging. In addition, most VNF scaling methods are passive in nature, which may cause high latency and fail to meet the QoS requirements of services. Therefore, this paper first proposes a GRU-based traffic prediction model and scales in/out VNF instances in advance based on the prediction result. Then we design a VNF buffering mechanism to avoid frequently releasing and creating VNF instances. Furthermore, based on the scaling results of VNF, we apply a DRL algorithm called A3C to train the agent and then obtain the optimal strategy of deploying new instances. Simulation results show that compared with other methods, the proposed proactive method can respond to traffic fluctuation in advance and reduce the total operating costs.

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.

Network Function Virtualization (NFV) can support flexible services provisioning in form of Service Function Chains (SFCs) consisting of ordered Virtual Network Functions (VNFs). The end-to-end (E2E) delay of flow traversing SFC has been an important indicator, especially for delay-sensitive E2E services, but there does not exist an analytical model that can accurately evaluate it. Moreover, the complicated network and stochastic request arrival are hard to predict and model. Therefore, quantitative delay model and dynamic NFV resource allocation method are needed. In this paper, an adaptive allocation method is designed to meet E2E delay requirements. Firstly, we devise an NFV resource allocation framework based on deep reinforcement learning (DRL) that can adapt to network changes by interacting with the network. Then a dynamic queuing model is established to determine average E2E packet delay. Based on the delay, we define the network utility function and propose a minimizing delay (MD) problem. According to the continuity of the problem, we use unsupervised reinforcement and auxiliary learning (UNREAL) to obtain the optimal allocation policy. At last, extensive simulation results show that UNREAL-MD has better performance compared to state-of-the-art methods in terms of delay, throughput and network utility.

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.

With the rapid development of communication technologies such as 5G, vehicular information and applications are exploding. Mobile edge computing (MEC) as a new technology can transfer the information more quickly and accurately. Providing differentiated services for the information can affect the performance of the applications. In this study, based on 802.11p EDCA protocol, we propose a new differentiated service scheme called DD-EDCA (Differentiating Density Enhanced Distributed Channel Access). Firstly, we use MEC Server to estimate the road density. Secondly, different solutions have been designed according to different vehicle density environment requirements. For example, the displacement trend is considered at a low density, and the multi-hop broadcast information is pre-processed at a high density. And the schemes for dynamically adjusting EDCA parameters are designed. Simulation results show that our method reduces latency and packet loss rate, and improves the throughput.

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.

Mobile edge computing (MEC) is envisioned as re- expanded cloud compared to fog computing. As making mobile services computing sank to the edge of network further, performance improvement can be got on time aspect. Therefore, MEC is seemed as a potential technology for delay-sensitive applications. Based on that, a reasonable computation offloading strategy would reduce system consumption for MEC further and release its computational capability. In view of resource shortage, especially bandwidth competition problem among Small Cells and edge computational requirements of HetNet, we focus on distributed uplink offloading for MEC in macro-micro coordination scene. It mainly contains two steps. First, based on Lyapunov to solve offloading decision-making problem for users in each Small Cell. Second, complete offloading update order- making of Small Cells in Macro Cell with proposed deviation update decision algorithm (DUDA). Our strategy makes up for the lack of system stability and uplink analysis in existing research. Numerical results demonstrate the effectiveness of our strategy.

Satellite communication network (SCN) has the capability to provide long-distance and high-quality communication services. It could play a significant role in the future networks for its high reliability and large capacity. However, SCN still needs more efficient resources allocation and dynamical traffic scheduling. As a new design paradigm, network functions virtualization (NFV) is potential to facilitate the performance of traditional networks, including SCN. Therefore, the applicability of NFV in SCN has attracted many people's attention, especially the study on service function chains (SFC). In this paper, we try to explain the problem of SFC deployment in NFV-enabled SCN and deal with it. Our main goal is to minimize the end-to-end service delay and then achieve flexible service orchestration. Based on the general NFV-enabled architectures, we build a time-varying SCN model and novel forms of SFC requests. Then we formulize this problem and propose an effective approach named SFC deployment in satellite network (SDSN). The solution is conducive to promoting the development of SCN. The simulation results show that SDSN could not only take much shorter execution time and minimize the total delay, but also has a good performance in the resource utilization and acceptance ratio.

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.

System Protection Communication Network (SPCN) is a new type of high-speed, real-time, secure and reliable communication network proposed in China supporting services such as AC/DC control, pumped storage control etc. In order to reduce the impact of SPCN failure on electric power system, this paper proposes a risk modeling and optimization approach. Firstly, we build a risk model to analyze the dynamic link and service risk from aspects of failure probability and its impact value. Then, we construct a risk optimization problem aiming at minimizing the link risk balance degree with service quality and risk constraints, and propose improved genetic algorithm to solve it. Based on part of network topology from a Chinese province, simulation results show that the proposed approach can make SPCN more reliable comparing to other methods when link failure occurs.

The existing network risk control mechanisms are lack of scientific and normative decision-making and rely too much on subjective judgments, which brings a great uncertainty on network risk management. In this paper, a risk control decision-making mechanism of power data network based on grey relation is put forward, and puts emphasis on the prior risk control based on the prediction results. This mechanism first constructs a matrix of positive and negative ideal measures according to the risk control objective. Then, the grey relation coefficient matrix between the candidate and ideal measures is calculated to evaluate the similarity between measures. Finally, we define the grey relation projection coefficient to evaluate the degree of closeness between the candidate measure and the positive ideal measure and the degree of deviation between the candidate measure and the negative ideal measure. Simulation results show that this mechanism can make timely and accurate decision-making of network risk control measures.

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).

Software-defined networking (SDN) develops a logically centralized control plane from the data plane, which makes the network management more intelligent. As the network becomes larger, the control plane with one controller can no longer manage the network efficiently. Therefore, multiple controllers are needed to manage the network. However, the survivability has been a key challenge in multi-controller SDN, which is sensitive to the controller failures. Once the controller breaks down, the switches will lose connections to the controller. This leads to severe consequences. In this regard, we propose an approach to improve fault tolerance of network in face of controller failures. In our work, we also attach great importance to the survivability of connections between controller and switch under random-link failures. Propagation delay is considered in our approach. Simulation results show that our approach guarantees that the controller failures can be effectively recovered. Moreover, after the controller failure is recovered, the survivability of multi-controller SDN in face of random-link failures can be improved.

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.

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.

Network function virtualization (NFV) is a powerful emerging technique with widespread applicability. It provides Network Functions (NFs) through software virtualization techniques that decouple software and hardware. Some connected network functions constitute a service function chain (SFC). Therefore, the deployment of SFCs is much agile and simple. However, this leads to more service failure. The service failure includes service availability failure and service quality degradation. Aiming at the problem that the existing service function chain detection methods have high detection cost and cannot locate the failure accurately. This paper presents a method based on minimum detection cost. The method consists of failure detection and failure localization. In failure detection, we calculate detection paths according to the topology of network functions to avoid duplicate probing of links between network functions. In failure localization, we locate service availability failure and service quality degradation respectively and add timestamp fields to network service header to analyze locations of service quality degradation. Experiments show that the method reduces active detection cost and improves the recall and false-positive of service failure localization.

Software-defined networking (SDN) develops a logically centralized control plane by abstracting the underlying network forwarding devices, which makes the control of network traffic more flexible and more intelligent. In SDN, a switch can only work according to the rule of the flow tables received from its controller. Once the controller breaks down, the switch cannot transmit the incoming data packet which cannot be matched in the flow table. The SDN network can be severely affected by the controller failure. In this regard, we are committed to design a proper backup approach for SDN controllers to reduce the loss brought by controller failures. Besides, we attach great importance to the survivability of the control network under network failures. In this paper, we first formulate the survivability of control network. Then we propose a backup approach for controllers based on the survivability model. The network delay is considered in the backup approach. Simulation is conducted to verify the validity and efficiency of our approach. Results show our backup approach guarantees that the controller failures can be effectively recovered. Comparison results between our approach and other existing approaches prove that the approach can effectively reduce the link loss brought by network failures when the backup controller replaces the failed controller to manage the network.

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.

The delivery of services typically requires packets to be steered through a sequence of middleboxes to improve network security and performance. One constraint on the deployment of services is that middleboxes are tightly coupled to the physical network topology. As a result, ensuring successful deployment requires error-prone and complex low-level configurations. Software-Defined Networking (SDN) can eliminate the need to configure network devices manually to deploy services. However, in terms of steering middlebox-specific traffic in data plane, applying the existing capabilities supported by OpenFlow protocol may lead to incorrect forwarding decisions when there is a loop in the route used to steer traffic. In this paper, we present an implementation using tagging to discriminate different instances of the same packet arriving at the same ingress port on the same switch (i.e. the existence of the loop). Moreover, we propose an algorithm to judge the existence of the loop in a physical sequence of switches and decide which switches are responsible for adding tags. The experimental result demonstrates that our implementation can properly steer traffic through a specific sequence of middleboxes even when there are loops in forwarding path.

Vulnerability assessment and optimization for wide area monitoring, protection and control system (WAMPAC) can enhance the robustness and sustainability of network. However, current assessment methods are incomplete and optimization methods ignore dynamic process. A comprehensive vulnerability assessment and optimization method is proposed. Firstly, for assessment, a comprehensive vulnerability indicator is designed to assess vulnerability of nodes and edges in the network integrating static and dynamic aspects. And then, to relieve unbalanced vulnerability distribution in the network, a routing optimization method is proposed by reconfiguring service routes on the edge with high vulnerability. Finally, the simulation is taken under a real system. Vulnerability assessment with the defined indicator is executed, and its correctness is proved as well. Then with the optimization method, the network vulnerability can be balanced, which takes on effective theoretical and practical significance.

Software-defined networking (SDN) uses a centralized control plane to manage the whole network. If the scale of the network is large, it is necessary to divide it into multiple domains. Since the network scale becomes larger, the probability of failure occurrences is higher. Therefore, it is important to guarantee the control plane resilience in multi-domain SDN. However, the existing approaches cannot store the network state in real time, and do not consider the backup controllers placement problem in multi-domain SDN. In order to ensure the resilience of the control plane in multi-domain SDN, we propose a sharing data store and backup controllers based approach. Sharing data store is used to ensure that each master controller has a view of the whole network and data store can save the network state during the failure time. The sharing backup controllers are used to guarantee the resilience of control plane with minimum cost. Simulations show that our approach can use as less backup controllers as possible to ensure the resilience of control plane.

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.

This paper proposes a risk assessment model of key service and optimization methods to reduce service risk in smart grid communication network. Firstly, we analyze the probability of failure of communication link and node which is induced by external factors, like natural disaster, human attack and system disturbances. Then using importance of services, links and nodes, we build the risk model of failure for key services. Further, we propose optimization methods based on Dijkstra algorithms to reduce the risk of key services. Finally, based on part of smart grid communication network topology structure from a Chinese province, the simulation results show that the risk of key services and whole network are reduced.

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.

Current research in base station(BS) energy consumption area is mostly devoted to the study of the static energy consumption or dynamic factor. But it lacks the research on the energy storage efficiency of the BS. In this paper, we propose a power modeling of BSs based on energy storage monitoring. Firstly, the system architecture and networking scheme of energy storage monitoring are presented, and the mathematical model of BS energy consumption is analyzed. Then, based on the current network's data, a large number of real data are collected through the energy consumption analysis system. We analyzed the effect of charge and discharge time on energy storage efficiency of power grid. Based on the monitoring data, we fit the model of energy consumption and get the model of overall power consumption. Finally, based on the use control template, the optimization scheme of reducing the power consumption of authority network is proposed, which has substantial economic and green value.

HDFS is designed for storing large files, but it suffered performance penalty when storing large amount of small files such as the space occupied by the metadata cause high consumption of NameNode and low efficiency of file reading. Currently, there are many approaches implemented to solve the small file problem. In this paper we use additional hardware named SFS (Small File Server) between users and HDFS to solve the small file problem. The proposed approach includes a file merging algorithm based on temporal continuity, an index structure to retrieve small files and a prefetching mechanism to improve the performance of file reading and writing. The experimental results show that the proposed approach efficiently optimizes small files storing in HDFS with reducing the overload of NameNode and improving the performance of file accessing.

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.

Container transportation has become the main transportation form for international freight. In this paper, the energy saving and reliability tactics are considered and we design a new E-ZBR routing algorithm based on the original ZBR routing protocol. Firstly we propose the score criterion for estimating a path, and then based on nodes' connectivity, we select two existed better routing paths through improved FCM clustering algorithm. Through clustering, we get rid of those nodes with possible failure and construct a fault tolerance routing path with more reliability and robustness. We demonstrate that E-ZBR routing protocol has higher the energy efficiency and fault tolerance.

Currently, the requirements of service quality in the electric power data network are getting higher and higher, and traffic prediction is an important premise to promote service quality. In order to accurately predict the total traffic of communication channels, a Multi-Applications Comprehensive Traffic Prediction (MACTP) model is proposed in this paper. Differing from F-ARIMA and S-ARIMA models which are used to predict the traffic of single application, the proposed MACTP model is used to predict the traffic of multi-applications conveyed in the channels. Simulation results show that MACTP model has higher accuracy and efficiency than classical prediction models, and it is suitable for electrical power data network.

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.

With the rapid development of cellular network systems, the operators need more experience to deal with complicated network management system and wide range of Key Performance Indicators (KPIs). There are many indicators related to each other due to the definition or communication process. But several implicit associations still exist among these KPIs. This paper proposes an approach to figure out the implicit linear relationship among indicators clearly in which a new clustering technique is used for distinguishing different relationships. Data analysis using real network data shows that the approach can well divide data into clusters, and each cluster can effectively reflect the relationship between indicators.

Energy efficiency in future green cellular wireless networks poses a challenge to operators and researchers. An effective method of providing energy savings (ES) in base stations (BSs) is to switch off idle BSs or put them into sleep mode and subsequently migrate the traffic loads to active BSs in their neighborhood. The intrinsic issue related to such methods applied to heterogeneous networks (HetNets) is that the optimal selections of various types of BSs during both energy-saving and coverage-compensating (CC) processes are difficult to determine. In an attempt to resolve the insufficiency of existing strategies, we propose a novel traffic-aware self-organizing ES mechanism that enables efficient resource allocation and interference management in multi-level networks. To further develop optimal energy conservation procedures for BSs, a new constraint model is proposed. We employ coverage gaps and over-provisioning as the optimization objectives and analyze the effects of their weights. The performance in terms of energy savings is evaluated in an urban Long-Term Evolution (LTE) scenario with different types of BSs. The simulation results show that the proposed mechanism can maximize energy efficiency in heterogeneous cellular networks while guaranteeing the quality of service. This algorithm is autonomous in terms of decision making and execution.

Software defined network (SDN) develops a centralized control plane to manage the whole network. If the scale of the network is large, it is necessary to deploy multiple distributed controllers. In SDN, a switch can only work by relying on flow tables received from its controller. Therefore, controller placement is an important problem to keep the switches working efficiently and improve the reliability of the control network, which consists of controllers, switches and the communication paths between them. However, the existing controller placement approaches are not effective or do not consider the network reliability and the required delay between switches and controllers at the same time. In order to ensure the reliability of the control network and meet the required propagation delay, a min-cover based controller placement approach is proposed. Two metrics are proposed to measure the reliability of a control network, and the definitions of neighborhood and min-cover are provided, based on which the approach try to use less controllers to achieve the reliability and low delay of the control network while guaranteeing the manageability of the network. Simulations show that min-cover based approach can use as less controllers as possible to ensure the reliability of control network and satisfy the required delay at the same time. Moreover, the approach has steadily good performance in networks of different scales and connectivity.

This paper discusses how to assess video Quality of Experience (QoE) with image feature information which includes texture and saliency information. In order to compress and transmit the feature information, wavelet transform is conducted and the high-frequency component histograms are fitted using generalized Gaussian distribution. At end user side, the video distortion is measured by using Kullback-Leibler Divergence (KLD) and therefore MOS is evaluated using neural network fitting. The LIVE Video Quality Database is used for testing the performance of proposed method. result confirms that the proposed method is competitive and suitable for assessing the QoE of real-time video service.

Power models are crucial to assess the power consumption of base stations (BSs) without quantitive description. Currently available models seldom consider the dynamic factors such as indoor and outdoor temperature. As power model will affect the energy-saving gains of different green resolutions, in this paper we provide such power models for mobile communication BSs relying on practical data collected from several BSs with focus on dynamic factors, e.g., traffic load, indoor and outdoor temperature. The quantitative power models for communication equipment and air conditioning are defined and validated combined with the mathematical method of linear regression. With application of the models we develop an energy saving method which can save at least 10% of the power consumption per year through the simulation and analysis. Still, the method does not affect the normal operation of communication BSs, which takes on strong economy and green significance.

Network virtualization has been considered as an enabling technology for future network, through which multiple heterogeneous virtual networks can run on a shared infrastructure. In order to study and test the network management mechanism of future network, we develop and implement a network operation simulation platform of the network virtualization environment. The platform mainly simulates the double-layer network topology and the virtual network embedding in the network virtualization environment. In addition, the running status and the fault of networks can also be simulated. The validation results show that our platform can effectively emulate the network virtualization environment. It has three advantages: (i) Simulating Double-layer network model. (ii) Running virtual network embedding experiments graphically and supporting comparison among different embedding algorithms. (iii) Simulating the faults of both substrate and virtual networks and emulating the detection results based on the simulated faults.

Multi-domain virtual network embedding (MVNE) aims to embed a virtual network (VN) across multiple physical domains while minimizing the embedding cost. A key phrase of MVNE is VN partitioning which partitions a VN into multiple physical domains. Since the MVNE problem is NP-hard, we provide a heuristic VN partitioning approach named VNP-PSO based on the Particle Swarm Optimization (PSO) to increase the efficiency of VN partitioning. The VNP-PSO algorithm generates a near-optimal solution of VN partitioning through the evolution process of the particles. The simulation results show that our proposal can increase the efficiency of VN partitioning and decrease the embedding cost of MVNE.

Survivable virtual network mapping (SVNM) has been extensively investigated to guarantee that the mapped virtual network (VN) works normally against substrate failures. The existing studies of SVNM mainly focus on single node or single link failure. Since natural disasters usually cause severe substrate failures in geographic regions, some work addressing SVNM against regional failures has been studied. However, the current approaches only solve the mapping problem against single regional failure. When there are multiple regional failures aroused by natural disasters, such approaches are not effective. In this paper, we first design a regional failure model with the knowledge of risk assessment. Then we propose two effective mapping algorithms based on the disaster-prediction scheme with the regional failure model. One is the minimum link risk prior selection algorithm and the other is the asymmetric parallel flow allocation algorithm. Simulation results show that both approaches can reduce the capacity loss of virtual networks caused by regional failures and can effectively increase the average VN acceptance ratio.

Network virtualization has been regarded as a core attribute of Future Internet. To improve the quality of virtual network, it is important to diagnose the faulty components quickly and accurately. Recently more and more researches focus on end-user fault diagnosis, which can fit incomplete knowledge and dynamic challenges. In this paper, we present a fault diagnosis system called DiaEO in virtual network. It improves the present end-user fault diagnosis methods by screening evidences before analyzing to reduce the time-consuming. Besides that, DiaEO also improves the anti-noise ability of the system. The simulation results show that the proposed method can keep high accuracy and ameliorate time performance.

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.

It's a challenge to effectively assess Quality of Experience (QoE) for TCP video streaming with network performance parameters, to resolve this problem, an objective hierarchical Evaluation for Transmission Control Protocol (TCP) video streaming is proposed under video playback scenarios. QoE assessment for TCP video streaming is resolved into two sub-steps. In the first place, in consideration of video playback performance parameters affecting QoE, the authors demonstrate three novel application-layer metrics. Further, impact of network status on video playback performance is investigated and the authors propose high level network-layer parameters. Then the correlation between the network-layer parameters and application-layer metrics is characterized through analysis and inference. In the secondly place, subjective tests are conducted to evaluate QoE from application-layer metrics. Ultimately, the authors validate analysis and model by simulations and experiments in real networks. The experimental study shows that the proposed method performs well in assessing QoE of TCP video streaming.

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 user's Quality of Experience (QoE) is an assessment of the human experience. It's not only influenced by Quality of Service (QoS) parameters but also influenced by user's preference for video content. This article studies user's preference for video content and how user's QoE changes on account of their biases. Through two experiments, it is concluded that the higher the user's preference score is, the higher the user rate MOS and the less MOS reduces when the resolution of video decrease. That is, the more user likes the video content, the more tolerant they will be to quality reduction of videos. Then a network resource management strategy is proposed based on the conclusion, and a web site platform is established for the test. Eventually we get the result that 87.0% of the participants have their MOS value increased after using the strategy.

In order to assess the quality of experience (QoE) of HTTP video streaming, the model of three levels of quality of service (QoS): network QoS, application QoS and QoE, is employed in this paper. We mainly study the effects of pause position, and therefore propose two new application performance metrics: location of each pause and time interval of pauses. We first focus on the buffer behaviors of the video player, and correlate the application QoS with the network QoS, based on the analytical model and mathematical model. Then the subjective tests and experiments are carried out to assess how application performance metrics affect the QoE, and the Back Propagation Neural Net (BPNN) is established to map the application QoS to the QoE. This paper reveals that the pauses in the front part of the video, as well as the shorter time interval of pauses, have a higher negative effect on QoE of HTTP video streaming.

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.

The performance and reliability of services relies on the network virtualization environment's capabilities to effectively detect and diagnose faults in both substrate and virtual network. However, Network Virtualization Environment (NVE) brings to fault diagnosis new challenges such as inaccessible substrate network information and multi-layer faults. To solve the above issues, a Multi-layer Fault Diagnosis Method (MFDM) is proposed. A layer-by-layer strategy is used to resolve the problem of inaccessible substrate network information. And a filtering algorithm is proposed to distinguish the multi-layer faults in the network virtualization environment. At last, a contribution-based hypothesis selection algorithm is proposed to infer the most possible faults. Simulations and experimental results show that MFDM has a higher performance in the accuracy ratio, false-positive ratio.

Survivable virtual network embedding (SVNE) aims at embedding a virtual network (VN) in a way, that after being affected by substrate failures, the VN is still operating. Based on the single node failure assumption, that at any time there can be at most one failed substrate node, the existing studies for the SVNE against substrate node failures back up VNs with a maximum resource sharing. However, multiple node failures do happen in reality, thus those methods are not always effective. In this paper, we propose a topology-aware VN embedding approach to enhancing the survivability against multiple node failures. We make use of the topology attributes to provide each substrate node with multiple potential failover choices, based on which a recoverability-based VN embedding algorithm and a profit-driven VN remapping algorithm are presented. Simulation results show that the proposed approach can achieve rational resource allocation and effectively increase the long term business profit to the infrastructure provider.

Network virtualization has been regarded as a core attribute of the Future Internet. In a Network Virtualization Environment (NVE), heterogeneous virtual networks can share the same physical infrastructure regardless of their different topologies, demands, protocols and so on. In this case, the Survivable Virtual Network Embedding (SVNE) problem becomes increasingly critical to overcome the failure of physical infrastructure. Backup resources needed to provide survivability of virtual network undoubtedly increase the challenge of resources efficiency of SVNE. In this paper, we study the SVNE problem and propose a method of allocating bandwidth resources based on load balancing of the physical resources and a strategy of reconfiguring backup resources. Simulation experiments show that load balancing based method has a higher performance in the long term acceptance ratio, revenues and utilization of substrate links. And the reconfiguration of backup resources is cost-efficient and also helpful to increase the acceptance ratio.

In this paper, we discuss how to assess video Quality of Experience (QoE) with image saliency and texture information extracted from original and distorted video sequences. Based on this information, we proposed two categories of full-reference quality metrics. The first category of metrics considers spatial distortions measured by MSE and SSIM in terms of saliency weighted images, saliency maps and texture maps. The second category of metrics are constructed by considering temporal distortions, which also includes the above three aspects. Then the temporal MSE between original and distorted video sequences is computed frame-by-frame. Thus altogether 9 metrics are obtained. With these metrics and subjective MOS from both the LIVE dataset and our own dataset, we conduct Neural Net fitting to measure the performance. Finally the detailed comparisons with mainstream models verify the effectiveness of the proposed model.

Adaptive active diagnosis method is widely adopted for fault diagnosis in networks. In active diagnosis, appropriate probes are selected sequentially and fault diagnosis is made by inference from results of selected probes. It is very important to select active probes with low cost and less impact on network performance. However, the selection of the most informative set of probes with limited cost is an NP-hard problem. The computational complexities of existing probe selection algorithms are still too high for large scale networks. In this paper, a lemma about mutual information provided by probes is proved based on the property of conditional entropy. Then an approximate method derived from this lemma is introduced to compute mutual information of probe. With this approximate method an efficient probe selection algorithm for active diagnosis is proposed. At last, the efficiency and effectiveness of the proposed algorithm is verified through simulation.

A novel dynamic multi-stage ESM (Energy-Saving Management) mechanism based on BS (Base Station) cooperation is proposed. The mechanism firstly introduces a local OP (Opposite Pair) cooperation method taking account of geographic topology and then divides time period into four domains. In time domains, regional dynamic multi-stage algorithms and efficient performance evaluation model for the mechanism is analyzed as well. The mechanism is simulated under a practical LTE BS deployment. Results show that 25.1% of regional energy can be saved at most. Still better coverage, interference, and throughput performance can be obtained comparing to other algorithms.

Because the quality of experience (QoE) of video is affected by the content type of the video, this paper firstly establishes a video content classification mechanism. Based on the content types and the objective parameters of bitstream layer and application layer, which have influences on video QoE, a multi-factor QoE evaluation method for H.264/AVC encoded video is proposed. The experimental study shows that the proposed method performs well in assessing the video QoE.

Service composition has been introduced to exploit heterogeneous resources of distributed nodes for purpose of supplying ubiquitous services in ubiquitous stub environments, especially in MANETs. However, due to the characteristics of infrastructure-less, the limited resources of the nodes and dynamic topology caused by the mobility of nodes, service composition faces great risk of failure. Therefore, service recovery handling failure is crucial to guarantee composite service's successful execution. In this paper, we propose a novel recovery selection function which incorporates device effective rate, individual capability and cooperative capability. Then we elaborate an original heuristic thought-based backup recovery algorithm: Dynamic Local Backup Recovery Algorithm (DLBRA). Finally, simulation results demonstrate that the proposed strategy ensures high performance, effectively guarantees the sustainability and significantly reduces the response time of the composite service.

For the sake of the increase of energy consumption, the emission of carbide can't be negligible. In this paper, we propose a mathematical model which adjusts the radius of base station, to figure out the problem of energy saving, while the quality of services such as the traffic volume of each base station and the service area should be satisfied. For the model, we use ant colony algorithm as a practical method to solve this problem. By using this mechanism, we can achieve the purpose of energy saving, when the traffic in a service area is low.

To solve the abnormal coverage problems caused by unreasonable network parameter settings more effectively, an automated coverage optimization scheme based on downtilt-adjustment of base stations in wireless access networks is proposed. After detecting and analyzing the abnormal coverage situation, simulated annealing algorithm is adopted by the scheme to figure out an optimal downtilt-adjustment solution for each base station. And then each base station can effectively adjust its electronic downtilt according to the solution to optimize the coverage. The whole process is completed without human intervention. Simulation results show that the proposed automated coverage optimization scheme can improve the wireless network coverage quality. Moreover, weak coverage and excessive coverage problems can be solved effectively.

With the development of Mobile Communication Network, flat network architecture has become a study focus. The flat traffic transmission can effectively lighten the burden on the operators' core network. The LIPA (Local IP Access) and SIPTO (Selected IP Traffic Offload) proposed by 3GPP are the typical technologies in the horizontal structure evolution. Current offload policy for LIPA&SIPTO either uses a coarse control bringing high cost to both of operators and mobile terminals, or performs rather complex in practice. This paper proposes a flexible and effective offload policy mechanism (the traffic offload mechanism based on policy of bearer granularity, TOMBOBP) to support the LIPA&SIPTO solution in H(e)NB subsystem. By matching the traffic information to the defined policy in the H(e)NB, it performs offload evaluation very well.