Early warning of core network capacity in space-terrestrial integrated networks

With the rapid development of low-orbit satellite com-munication networks both domestically and internationally,space-terrestrial integrated networks will become the future development trend.For space and terrestrial networks with limi-ted resources,the utilization efficiency of the entire space-terres-trial integrated networks resources can be affected by the core network indirectly.In order to improve the response efficiency of core networks expansion construction,early warning of the core network elements capacity is necessary.Based on the inte-grated architecture of space and terrestrial network,multidimen-sional factors are considered in this paper,including the number of terminals,login users,and the rules of users’migration during holidays.Using artifical intelligence(AI)technologies,the regis-tered users of the access and mobility management function(AMF),authorization users of the unified data management(UDM),protocol data unit(PDU)sessions of session manage-ment function(SMF)are predicted in combination with the num-ber of login users,the number of terminals.Therefore,the core network elements capacity can be predicted in advance.The proposed method is proven to be effective based on the data from real network.

Co-Sharing Waveform Design for Millimeter-Wave Radar Communication Systems

Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.

Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

Cooperative Spectrum Allocation with QoS Support in Cognitive Cooperative Vehicular Ad Hoc Networks

To solve the contradiction between the increasing demand of diverse vehicular wireless applications and the shortage of spectrum resource, a novel cognitive cooperative vehicular ad-hoc network （CC- VANET） framework is proposed in this paper. Firstly, we develop an adaptive cognitive spectrum sensing （ACSS） mechanism which can help to trigger and adjust the spectrum sensing window according to network traffic load status and communication quality. And then, Generalized Nash Bargaining SoLution （GNBS）, which can achieve a good tradeoff between efficiency and weighted fairness, is proposed to formulate the asymmetric inter- cell resource allocation. Finally, GNBS- Safety （GNBS-S） scheme is developed to enhance the Quality of Service （QoS） of safety applications, especially in the heavy load status, where the bandwidth demanded and supplied cannot be matched well. Furthermore, the primary user activity （PUA） which can cause rate loss to secondary users, is also considered to alleviate its influence to fairness. Simulation results indicate that the proposed CC-VANET scheme can greatly improve the spectrum efficiency and reduce the transmission delay and packet loss rate on the heavy contention status. And GNBS spectrum allocation scheme outperforms both the Max-rain and Max-rate schemes, and canenhance the communication reliability of safety service considerably in CC-VANET.

QoS-Aware Offloading Based on Communication-Computation Resource Coordination for 6G Edge Intelligence

Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual machines.Then we construct the system utility measuring QoS based on application latency and user devices’energy consumption.We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference.Simulation results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.

Self-Coexistence Strategy in Heterogeneous Cognitive Radio Networks

Different from general cognitive wireless networks, there is no centralized scheduling and management infrastructure among heterogeneous cognitive networks. Multiple cells may operate in the same vicinity resulting in unfair spectrum occupation time (when the cells belong to different industries) and degraded performance of the cellular networks. A distributed self-coexistence mechanism is necessary. In this paper, we take the self-coexistence of multi users in heterogeneous scenarios as the problem of spectrum allocation in non-cooperative mode. Hence we propose Fair Self-Coexistence Strategy (FSCS). In this strategy, not only the fairness of occupation time is considered, but also different competitive priority metric based on Quality of Service (QoS) is adopted. Each cognitive cell independently completes the spectrum allocation process, by use of sensing techniques and perceptual information about neighboring network cells. The simulation experiment results show that our spectrum allocation strategy guarantees the fairness among the heterogeneous secondary networks. And in the resource scarce environment, our strategy can effectively achieve the differentiation competition results.

MetaOracle:A High-Throughput Decentralized Oracle for Web 3.0-Empowered Metaverse

Recent rapid advancements in communication technology have brought forth the era of Web 3.0,representing a substantial transformation in the Internet landscape.This shift has led to the emergence of various decentralized metaverse applications that leverage blockchain as their underlying technology to enable users to exchange value directly from point to point.However,blockchains are blind to the real world,and smart contracts cannot directly access data from the external world.To address this limitation,the technology of oracles has been introduced to provide real-world data for smart contracts and other blockchain applications.In this paper,we focus on mitigating the risks associated with oracles providing corrupt or incorrect data.We propose a novel Web 3.0 architecture for the Metaverse based on the multiidentifier network(MIN),and its decentralized blockchain oracle model called Meta Oracle.The experimental results show that the proposed scheme can achieve minor time investment in return for significantly more reliable data and increased throughput.

Improved MPEG-4 High-Efficiency AAC With Variable-Length Soft-Decision Decoding of the Quantized Spectral Coefficients

MPEG-4 High-Efficiency Advanced Audio Coding (HE-AAC) is designed for low bit rate applications, such as audio streaming in mobile communications. The HE-AAC audio codec offers a better coding efficiency since variable-length codes (VLCs) are adopted. However, HE-AAC has originally been designed for storage and error-free transmission conditions. For the transmission over bit error-prone channels, error propagation is a serious problem for the VLCs. Therefore, a robust HE-AAC decoder is desired, especially for mobile communications. In contrast to traditional hard-decision decoding, utilizing bit-wise channel reliability information, softdecision (SD) decoding has been known to offer better audio quality. In HE-AAC, the global gain parameter is coded with fixedlength codes (FLCs), while the scale factors and quantized spectral coefficients are coded with VLCs. In this work, we apply FL/SD decoding to the global gain parameter, VL/SD decoding to the parameters scale factors and quantized spectral coefficients. Especially, in order to apply VL/SD decoding to the quantized spectral coefficients, a new modified trellis representation in VL/SD decoding is proposed. An improved HE-AAC performance is clearly observed, with the support of both instrumental measurements and a subjective listening test.

DMMF: Dominant and Max-Min Fair Allocation in Satellite Networks

Resource allocation is crucial for satellite networks. In this paper, we propose a multi-resource fair allocation scheme, namely Dominant and Max-min Fair(DMMF), to efficiently and fairly allocate resources. It consists of two allocation stages, dominant resource fair(DRF) allocation stage and max-min fairness(MMF) allocation stage. The proposed DMMF scheme exhibits desirable properties, including share incentive, strategy proofness, envy freeness and Pareto optimality. Meanwhile, DMMF can improve the allocation efficiency and reach 100% allocation efficiency.

Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.

Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey

Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.

A Motivate Mechanism for Large-Scale P2P Content Delivery System

For lack of effective resource adjustment method, the supply-demand relationship of each resource in P2P content delivery system are often unbalanced. Especially after a popular content releasing, a burst of downloaders often can＇t find sufficient uploaders and their request may starve the upload capacity of server. Therefore the overall system QoS may be degraded. To tackle such issue, this paper proposes a download rate accelerate mechanism, called motivate mechanism. With it, the system can quickly find out the files becoming insufficient by monitoring the operating status of the files hourly, Then it promptly increase the number of copies of those files by using free rider nodes so that the whole system QoS is maintained and the system performance is improved. The experiment results on the practical operating system of Tencent demonstrated that the proposed mechanism increases the download rate, saves the traffic on the server and optimizes the system performance.

Study on Acknowledgement Assistant Inner Loop Power Control for CDMA2000 System

This paper proposes an acknowledgement assistant inner loop power control (AILPC), which still obeys power control command from receiver with regard of power adjustment direction, but adopts a flexible adjustment step based on extra information from ACK feedback. Positive ACK feedback happening earlier reflects better signal strength at receiver, so when transmitter is instructed to decrease power, a larger adjustment step can be adopted. To evaluate AILPC's performance, link-level simulation platform emulating CDMA2000 1X Rev.E downlink is established. AILPC is compared with conventional power control algorithm in terms of discrepancy between instantaneous power and target power.

Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based on DQN

With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.

Measurements and Characterization for the Vehicle-to-Infrastructure Channel in Urban and Highway Scenarios at 5.92GHz

In the future smart transportation system, reliable vehicle-to-infrastructure(V2 I) communication is very important to ensure vehicle driving safety and to improve vehicle driving efficiency. In this paper, V2 I channel measurements at 5.92 GHz are conducted in typical urban and highway scenarios.The frequency and bandwidth of transmission, as well as the deployment of the RSU(roadside unit) and the OBU(on board unit), are selected by considering the recommendation proposed by 3 GPP TR 36.885. Then,based on the measured data, the key channel characteristic parameters of the V2 I channel are extracted,including path loss, root-mean-square delay spread,stationarity distance, and Doppler spread, etc. Also,the statistical characteristics of the parameters, including time-varying and Doppler characteristics, are investigated and characterized. The work in this paper helps researchers design technology and communication systems in similar scenarios.

Patient-Specific Echo-Based Fluid-Structure Interaction Modeling Study of Blood Flow in the Left Ventricle with Infarction and Hypertension

Understanding cardiac blood flow behaviors is of importance for cardiovascular research and clinical assessment of ventricle functions.Patient-specific Echo-based left ventricle(LV)fluid-structure interaction(FSI)models were introduced to perform ventricle mechanical analysis,investigate flow behaviors,and evaluate the impact of myocardial infarction(MI)and hypertension on blood flow in the LV.Echo image data were acquired from 3 patients with consent obtained:one healthy volunteer(P1),one hypertension patient(P2),and one patient who had an inferior and posterior myocardial infarction(P3).The nonlinear Mooney-Rivlin model was used for ventricle tissue with material parameter values chosen to match echo-measure LV volume data.Using the healthy case as baseline,LV with MI had lower peak flow velocity(30%lower at beginejection)and hypertension LV had higher peak flow velocity(16%higher at begin-filling).The vortex area(defined as the area with vorticity>0)for P3 was 19%smaller than that of P1.The vortex area for P2 was 12%smaller than that of P1.At peak of filling,the maximum flow shear stress(FSS)for P2 and P3 were 390%higher and 63%lower than that of P1,respectively.Meanwhile,LV stress and strain of P2 were 41%and 15%higher than those of P1,respectively.LV stress and strain of P3 were 36%and 42%lower than those of P1,respectively.In conclusion,FSI models could provide both flow and structural stress/strain information which would serve as the base for further cardiovascular investigations related to disease initiation,progression,and treatment strategy selections.Large-scale studies are needed to validate our findings.

Real Effective Exchange Rate and Regional Economic Growth in China： Evidence from Provincial Data

Using data for the period 2000-2011, we construct province-level real effective exchange rate （REER） indices for China and test the effect of REER depreciation on regional economic growth in a generalized method of moments regression framework. Our results show that REER depreciation, in general, promotes regional economic growth, through increasing net exports and lowering FDI costs. After dividing the full sample into coastal and inland subsamples, we find that REER depreciation influences economic growth in inland areas but not in coastal areas. This is due to the fact that the inland areas have more surplus labor or other resources to expand their production capacity when REER depreciation leads to increased worm demand. Furthermore, compared to inland areas, processing-and-assembly trade comprises a larger share of trade in the coastal areas, where traders import more raw materials and intermediate goods to process and assemble goods. When the exchange rate depreciates, the costs of imported materials and immediate goods increase. In this case, the benefits from REER depreciation in coastal areas are offset to some extent and are thus lower than in inland areas.

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).

Re-scaling and adaptive stochastic resonance as a tool for weak GNSS signal acquisition

Weak global navigation satellite system（GNSS） signal acquisition has been a limitation for high sensitivity GPS receivers. This paper modifies the traditional acquisition algorithms and proposes a new weak GNSS signal acquisition method using re-scaling and adaptive stochastic resonance（SR）. The adoption of classical SR is limited to low-frequency and periodic signals. Given that GNSS signal frequency is high and that the periodic feature of the GNSS signal is affected by the Doppler frequency shift, classical SR methods cannot be directly used to acquire GNSS signals. Therefore, the re-scaling technique is used in our study to expand its usage to high-frequency signals and adaptive control technique is used to gradually determine the Doppler shift effect in GNSS signal buried in strong noises. The effectiveness of our proposed method was verified by the simulations on GPS L1 signals. The simulation results indicate that the new algorithm based on SR can reach-181 d BW sensitivity with a very short data length of 1 ms.

Measuring QoE ofWeb Service with Mining DNS Resolution Data

Internet service providers(ISPs)are paying more attention to the Quality of Experience(QoE)of the web service that is one of the most widely used Internet services.Measuring it with existing systems deployed in the network so far may save investment for ISPs since no additional QoE system is required.In this paper,with Domain Name System(DNS)resolution data that are available in the ISP’network,we propose the First Webpage Time(FWT)algorithm in order to measure the QoE of the web service.The proposed FWT algorithm is analyzed in theory,which shows that its precision is guaranteed.Experiments based on the ISP’s DNS resolution data are carried out to evaluate the proposed FWT algorithm.