NOVEL POWER CONTROL GAME VIA PRICING ALGORITHM FOR COGNITIVE RADIOS

To compensate the service providers who have paid billions of dollars to use spectrum and to satisfy secondary users' requirements in cognitive radios, a Non-cooperative Power Control Game and Pricing algorithm (NPGP) is proposed. Simulation results show that the proposed algorithm can regulate the secondary users' transmitter powers, optimally allocate radio resource and increase the total throughput effectively.

Reconstructing the 3D digital core with a fully convolutional neural network

In this paper, the complete process of constructing 3D digital core by fullconvolutional neural network is described carefully. A large number of sandstone computedtomography (CT) images are used as training input for a fully convolutional neural networkmodel. This model is used to reconstruct the three-dimensional (3D) digital core of Bereasandstone based on a small number of CT images. The Hamming distance together with theMinkowski functions for porosity, average volume specifi c surface area, average curvature,and connectivity of both the real core and the digital reconstruction are used to evaluate theaccuracy of the proposed method. The results show that the reconstruction achieved relativeerrors of 6.26%, 1.40%, 6.06%, and 4.91% for the four Minkowski functions and a Hammingdistance of 0.04479. This demonstrates that the proposed method can not only reconstructthe physical properties of real sandstone but can also restore the real characteristics of poredistribution in sandstone, is the ability to which is a new way to characterize the internalmicrostructure of rocks.

UAV Autonomous Navigation for Wireless Powered Data Collection with Onboard Deep Q-Network

In a rechargeable wireless sensor network,utilizing the unmanned aerial vehicle(UAV)as a mobile base station(BS)to charge sensors and collect data effectively prolongs the network’s lifetime.In this paper,we jointly optimize the UAV’s flight trajectory and the sensor selection and operation modes to maximize the average data traffic of all sensors within a wireless sensor network(WSN)during finite UAV’s flight time,while ensuring the energy required for each sensor by wireless power transfer(WPT).We consider a practical scenario,where the UAV has no prior knowledge of sensor locations.The UAV performs autonomous navigation based on the status information obtained within the coverage area,which is modeled as a Markov decision process(MDP).The deep Q-network(DQN)is employed to execute the navigation based on the UAV position,the battery level state,channel conditions and current data traffic of sensors within the UAV’s coverage area.Our simulation results demonstrate that the DQN algorithm significantly improves the network performance in terms of the average data traffic and trajectory design.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

Holistic Service-Based Architecture for Space-Air-Ground Integrated Network for 5G-Advanced and Beyond

The Service-based Architecture(SBA) is one of the key innovations of 5G architecture that leverage modularized, self-contained and independent services to provide flexible and cloud-native 5G network. In this paper, SBA for Space-Air-Ground Integrated Network(SAGIN) is investigated to enable the 5G integration deployment. This paper proposes a novel Holistic Service-based Architecture(H-SBA)for SAGIN of 5G-Advanced and beyond, i.e., 6G. The H-SBA introduces the concept of end-to-end servicebased architecture design. The "Network Function Service", introduced in 5G SBA, is extended from Control Plane to User Plane, from core network to access network. Based on H-SBA, the new generation of protocol design is proposed, which proposes to use IETF QUIC and SRv6 to substitute 5G HTTP/2.0 and GTP-U. Testing results show that new protocols can achieve low latency and high throughput, making them promising candidate for H-SBA.

国家标准项目《信息安全技术 机密计算通用框架》试点实践

Data security and privacy protection have become the focus of cybersecurity protection in many countries.The utilization of confidential computing technology can significantly enhance data security.However,there are a variety of confidential computing technology routes,with significant differences in the principles and interfaces of implementation.There is an urgent need to develop relevant standards and specifications and guide the design,development,deployment and application of confidential computing related products.This paper introduces the development progress of the national standard project“Information security techniques—General framework for the confidential computing”and its pilot application scenarios across various industries.Additionally,it proposes suggestions on modifying and improving the standard to promote the healthy development of the confidential computing industry ecosystem.

Biased random walk with restart for essential proteins prediction

Predicting essential proteins is crucial for discovering the process of cellular organization and viability.We propose biased random walk with restart algorithm for essential proteins prediction,called BRWR.Firstly,the common process of practice walk often sets the probability of particles transferring to adjacent nodes to be equal,neglecting the influence of the similarity structure on the transition probability.To address this problem,we redefine a novel transition probability matrix by integrating the gene express similarity and subcellular location similarity.The particles can obtain biased transferring probabilities to perform random walk so as to further exploit biological properties embedded in the network structure.Secondly,we use gene ontology(GO)terms score and subcellular score to calculate the initial probability vector of the random walk with restart.Finally,when the biased random walk with restart process reaches steady state,the protein importance score is obtained.In order to demonstrate superiority of BRWR,we conduct experiments on the YHQ,BioGRID,Krogan and Gavin PPI networks.The results show that the method BRWR is superior to other state-of-the-art methods in essential proteins recognition performance.Especially,compared with the contrast methods,the improvements of BRWR in terms of the ACC results range in 1.4%–5.7%,1.3%–11.9%,2.4%–8.8%,and 0.8%–14.2%,respectively.Therefore,BRWR is effective and reasonable.

Dual Power Allocation Optimization Based on Stackelberg Game in Heterogeneous Network with Hybrid Energy Supplies

In heterogeneous network with hybrid energy supplies including green energy and on-grid energy, it is imperative to increase the utilization of green energy as well as to improve the utilities of users and networks. As the difference of hybrid energy source in stability and economy, thus, this paper focuses on the network with hybrid energy source, and design the utility of each user in the hybrid energy source system from the perspective of stability, economy and environment pollution. A dual power allocation algorithm based on Stackelberg game to maximize the utilities of users and networks is proposed. In addition, an iteration method is proposed which enables all players to reach the Stackelberg equilibrium(SE). Simulation results validate that players can reach the SE and the utilities of users and networks can be maximization, and the green energy can be efficiently used.

Next Decade of Telecommunications Artificial Intelligence

It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.

The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks

Virtual Landslide Disaster environments are important for multilevel simulation,analysis and decision-making about Landslide Disasters.However,in the existing related studies,complex disaster scene objects and relationships are not deeply analyzed,and the scene contents are fixed,which is not conducive to meeting multilevel visualization task requirements for diverse users.To resolve the above issues,a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper.The characteristics of relationships among users,scenes and data were first discussed in detail;then,a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects,and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph.Therefore,a personalized Landslide Disaster scene data recommendation mechanism was proposed.Finally,a prototype system was developed,and an experimental analysis was conducted.The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users.The recommendation accuracy stabilizes above 80%–a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments.

Cross-layer combining of power control and adaptive modulation with truncated ARQ for cognitive radios

To maximize throughput and to satisfy users＇ requirements in cognitive radios, a cross-layer optimization problem combining adaptive modulation and power control at the physical layer and truncated automatic repeat request at the medium access control layer is proposed. Simulation results show the combination of power control, adaptive modulation, and truncated automatic repeat request can regulate transmitter powers and increase the total throughput effectively.

Research on the low complexity adaptive power allocation algorithm in cooperative systems

Cooperative diversity is a new technology to improve bit error rate （BER） performance in wireless communications, A new power allocation algorithm to improve BER performance in cellular uplink has been proposed in this paper. Some existing power allocation schemes were proposed for the purpose of maximizing the channel capacity or minimizing the outage probability. Different from these schemes, the proposed algorithm aims at minimizing the BER of the systems under the constraint of total transmission power. Besides this characteristic, the proposed algorithm can realize a low complexity real-time power allocation according to the fluctuation of channels. Simulation results show that the proposed algorithm can decrease the BER performance of the systems effectively.

Spatiotemporal emotion recognition based on 3D time-frequency domain feature matrix

The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional states.Aiming at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was proposed.Specifically,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral cortex.Then,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional state.Spatiotemporal emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification tasks.The experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.

Game-based distributed noncooperation interference coordination scheme in ultra-dense networks

Ultra-dense networks(UDNs) is a promising solution to meet the exponential increase in mobile data traffic. But the ultra-dense deployment of cells inevitably brings complicated inter-cell interference(ICI) and existing interference coordination scheme cannot be directly applied. To minimize the aggregate interference of each small cells, this paper formulated the problem as a distributed noncooperation game-based interference coordination scheme in UDNs considering the real demand rate of each small cell user equipment(SUE) and proved it to be a potential game. An improved no-regret learning algorithm was introduced to coverage to the Nash equilibrium(NE) of the formulated game. Simulation results show that the proposed scheme has better performance compared with existing schemes.

DPKnob:A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios

Differential privacy is an essential approach for privacy preservation in data queries.However,users face a significant challenge in selecting an appropriate privacy scheme,as they struggle to balance the utility of query results with the preservation of diverse individual privacy.Customizing a privacy scheme becomes even more complex in dealing with queries that involve multiple data attributes.When adversaries attempt to breach privacy firewalls by conducting multiple regular data queries with various attribute values,data owners must arduously discern unpredictable disclosure risks and construct suitable privacy schemes.In this paper,we propose a visual analysis approach for formulating privacy schemes of differential privacy.Our approach supports the identification and simulation of potential privacy attacks in querying statistical results of multi-dimensional databases.We also developed a prototype system,called DPKnob,which integrates multiple coordinated views.DPKnob not only allows users to interactively assess and explore privacy exposure risks by browsing high-risk attacks,but also facilitates an iterative process for formulating and optimizing privacy schemes based on differential privacy.This iterative process allows users to compare different schemes,refine their expectations of privacy and utility,and ultimately establish a well-balanced privacy scheme.The effectiveness of this study is verified by a user study and two case studies with real-world datasets.

Low-modal-crosstalk doped-fiber amplifiers in few-mode-fiber-based systems

Independent light propagation through one or multiple modes is commonly considered as a basic demand for mode manipulation in few-mode fiber(FMF)-or multimode fiber(MMF)-based optical systems such as transmission links,optical fiber lasers,or distributed optical fiber sensors.However,the insertion of doped-fiber amplifiers always kills the entire effort by inducing significant modal crosstalk.In this paper,we propose the design of doped-fiber amplifiers in FMF-based systems adopting identical multiple-ring-core(MRC)index profiles for both passive and doped fibers to achieve low modal crosstalk.We develop the direct-glass-transition(DGT)modified chemical vapor deposition(MCVD)processing for precise fabrication of few-mode erbium-doped fibers(FM-EDFs)with MRC profiles of both refractive index and erbium-ion doping distribution.Then,a few-mode erbium-doped-fiber amplifier(FM-EDFA)with a maximum gain of 26.08 dB and differential modal gain(DMG)of 2.3 dB is realized based on fabricated FM-EDF matched with a transmission FMF supporting four linearly polarized(LP)modes.With the insertion of the FM-EDFA,60+60 km simultaneous LP_(01)∕LP_(11)∕LP_(21)∕LP_(02)transmission without inter-modal multiple-input multiple-output digital signal processing(MIMO-DSP)is successfully demonstrated.The proposed design of low-modal-crosstalk doped-fiber amplifiers provides,to our knowledge,new insights into mode manipulation methods in various applications.

Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode.If the reactive power regulation potentials of new energy and EVs can be tapped,it will greatly reduce the reactive power optimization pressure on the network.Keeping this in mind,our reasearch first adds EVs to the traditional distribution network model with new forms of energy,and then a multi-objective optimization model,with achieving the lowest line loss,voltage deviation,and the highest static voltage stability margin as its objectives,is constructed.Meanwihile,the corresponding model parameters are set under different climate and equipment conditions.Ultimately,the optimization model under specific scenarios is obtained.Furthermore,considering the supply and demand relation-ship of the network,an improved technique for order preference by similarity to an ideal solution decision method is proposed,which aims to judge the adaptability of different algorithms to the optimized model,so as to select a most suitable algorithm for the problem.Finally,a comparison is made between the constructed model and a model without new energy.The results reveal that the constructed model can provide a high quality reactive power regula-tion strategy.

Perspectives on point cloud-based 3D scene modeling and XR presentation within the cloud-edge-client architecture

With the support of edge computing,the synergy and collaboration among central cloud,edge cloud,and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture.This integration unlocks the value of data and computational power,presenting significant opportunities for large-scale 3D scene modeling and XR presentation.In this paper,we explore the perspectives and highlight new challenges in 3D scene modeling and XR presentation based on point cloud within the cloud-edge-client integrated architecture.We also propose a novel cloud-edge-client integrated technology framework and a demonstration of municipal governance application to address these challenges.

Novel Scheme for Essential Proteins Identification Based on Improved Multicriteria Decision Making

Identifying essential proteins from protein-protein interaction networks is important for studies onbiological evolution and new drug’s development.Most of the presented criteria for prioritizing essential proteinsonly focus on a certain attribute of the proteins in the network,which suffer from information loss.In order toovercome this problem,a relatively comprehensive and effective novel method for essential proteins identificationbased on improved multicriteria decision making(MCDM),called essential proteins identification-technique fororder preference by similarity to ideal solution(EPI-TOPSIS),is proposed.First,considering different attributes ofproteins,we propose three methods from different aspects to evaluate the significance of the proteins:gene-degreecentrality(GDC)for gene expression sequence;subcellular-neighbor-degree centrality(SNDC)and subcellular-indegree centrality(SIDC)for subcellular location information and protein complexes.Then,betweenness centrality(BC)and these three methods are considered together as the multiple criteria of the decision-making model.Analytic hierarchy process is used to evaluate the weights of each criterion,and the essential proteins are prioritizedby an ideal solution of MCDM,i.e.,TOPSIS.Experiments are conducted on YDIP,YMIPS,Krogan and BioGRIDnetworks.The results indicate that EPI-TOPSIS outperforms several state-of-the-art approaches for identifyingthe essential proteins through the performance measures.

VAECGAN:a generating framework for long-term prediction in multivariate time series

Long-term prediction is still a difficult problem in data mining.People usually use various kinds of methods of Recurrent Neural Network to predict.However,with the increase of the prediction step,the accuracy of prediction decreases rapidly.In order to improve the accuracy of long-term prediction,we propose a framework Variational Auto-Encoder Conditional Generative Adversarial Network(VAECGAN).Our model is divided into three parts.The first part is the encoder net,which can encode the exogenous sequence into latent space vectors and fully save the information carried by the exogenous sequence.The second part is the generator net which is responsible for generating prediction data.In the third part,the discriminator net is used to classify and feedback,adjust data generation and improve prediction accuracy.Finally,extensive empirical studies tested with five real-world datasets(NASDAQ,SML,Energy,EEG,KDDCUP)demonstrate the effectiveness and robustness of our proposed approach.