As network applications are increasingly offloaded to the programmable switches, program fitting problems come to the fore, which means effectively mapping the programming entities into the hardware resources. However...
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The rise of network function virtualization (NFV) technology makes the realization of network function change from hardware middleware to virtual network function (VNF). The existing methods allocate fixedresources t...
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ISBN:
(纸本)9781450397148
The rise of network function virtualization (NFV) technology makes the realization of network function change from hardware middleware to virtual network function (VNF). The existing methods allocate fixedresources to each VNF instance, but this resource allocation method will cause resource waste, which will affect the quality of service. The resource requirements prediction model solves the resource allocation problem by predicting the change of resource requirements. In this paper, deep learning is used to solve the regression prediction problem, and a VNF resource requirements prediction model based on convolutional neural network (CNN) and gatedrecurrent unit (GrU) is proposed. Compared with other single model and combined model, the experimental results show that the prediction errorrate of the proposed model is reduced by 23.3%.
The current mimic adjudication model is centered on a single adjudicator, and the adjudication algorithm is relatively single and static, which cannot cope with today's complex and changeable network environment. ...
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dynamic metasurface antenna (dMA) array is a new type of array structure with low complexity and low cost, which is emerging as a promising technique for next-generation wireless networks. In this paper, a downlink mu...
dynamic metasurface antenna (dMA) array is a new type of array structure with low complexity and low cost, which is emerging as a promising technique for next-generation wireless networks. In this paper, a downlink multi-user MISO system based on dMA arrays is investigated, and the mean square error (MSE) is chosen as a performance metric to characterize the transmission reliability. The beamforming performance of dMA arrays is investigated using the minimum mean square error (MMSE) criterion. For the multiuser scenario, the optimization problem minimizes MSE by jointly optimizing the transmit precoder anddMA weights while satisfying the transmit power constraint. Our numerical results show that the proposed MMSE scheme converges fast and performs close to full digital beamforming.
In the past decade, Fake Base Stations (FBS) have been consistently employed by criminals to target mobile users through spam text messages. despite the introduction of several techniques to mitigate this problem, spa...
In the past decade, Fake Base Stations (FBS) have been consistently employed by criminals to target mobile users through spam text messages. despite the introduction of several techniques to mitigate this problem, spam messages remain a persistent and challenging issue in some countries, such as china, resulting in billions of dollars in annual economic losses. Therefore, this paper proposes an algorithm named BErT-GCN that combines large-scale pre-training models with Graph Convolutional Networks (GCN) for multi-class detection of fraudulent base station telecommunications scams in Chinese text messages. First, BErT (Bidirectional Encoderrepresentation from Transformers) is used to encode the corpus and generate word embeddings. Subsequently, the generated word embeddings are fed into GCN for training. Finally, a Softmax layer is employed to perform multi-class detection on fraudulent base station telecommunications scams in Chinese text messages. Experimental results demonstrate that the proposed model achieves favorable performance on the FBS-SMS-dataset and exhibits outstanding performance in the domain of multi-class detection.
Website security detection is important for Internet security. Existing machine learning-based malicious UrL detection methods have a low accuracy and weak generalization ability. Thus, we proposed a new multi-feature...
Website security detection is important for Internet security. Existing machine learning-based malicious UrL detection methods have a low accuracy and weak generalization ability. Thus, we proposed a new multi-feature fusion malicious website detection model BGresNet by integrating the advantages of Bidirectional Encoderrepresentations from Transformers (BErT), Graph Convolutional Network (GCN), andresidual neural network (resNet). The method integrates three features: Uniform resource Locator (UrL) creator, rule features, and website titles. First, we used BGresNet to process UrL characters and website titles separately, transforming them into vectorrepresentations. Then, these two vectors were fused with UrL rule vectors. Finally, we employed the softmax function to realize the detection of malicious websites. The experimental results showed that the proposed model exhibited significant superiority in detecting malicious websites, providing a new and effective method for malicious UrL detection in Internet security.
As a next-generation mobile communication technology, 5G's network functions—cloudification, virtualization, and other characteristics enable the network to overcome single-point resource constraints and be deplo...
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diode rectifier unit(drU)-based high-voltage direct current(HVdC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof drUs,a grid-forming(G...
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diode rectifier unit(drU)-based high-voltage direct current(HVdC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof drUs,a grid-forming(GFM)strategy forwind-turbine converters isnecessary to support offshore AC voltageand ***,the active power-synchronization control in traditional GFM converters is unsuitable fordrU-based GFM ***,the stability issue fordrU-based HVdC systems involving drU-based GFM and grid-following(GFL)converters has not yet been *** solve these issues,this study begins with the characteristics of a drU-based HVdC system and presents a control scheme fordrU-based GFM converters for power ***,the dq-frame impedance model of the drU-based GFM converteris proposed for the stability analysis of the entire HVdC ***,a simulation platform is built to verify the model accuracy andsystem stability.
dear editor,Physical-layer security (PLS) has been regarded as a prominent technique to achieve secure communications for heterogeneous networks (HetNets)with simultaneous wireless information and power transfer (SWIP...
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dear editor,Physical-layer security (PLS) has been regarded as a prominent technique to achieve secure communications for heterogeneous networks (HetNets)with simultaneous wireless information and power transfer (SWIPT)[1].
Aiming at the shortcomings of existing user profile construction algorithms that do not fully utilize contextual structural information and multidimensional feature information., a user profile construction model base...
Aiming at the shortcomings of existing user profile construction algorithms that do not fully utilize contextual structural information and multidimensional feature information., a user profile construction model based on the pre-training model NEZHA, deep Pyramidal Convolutional Neural Networks (dPCNN), Gatedrecurrent Unit (GrU), and attention mechanism is proposed, NCdGA. The method uses multiple channels to process the text, capturing different dimensions of information and constructing features to reduce the prediction bias that occurs when a single model captures a single feature. The experimental results show that the NCdGA model has better accuracy and effectiveness compared with other modeling methods.
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