Key Technologies of 3G WCDMA Network Sharing

Based on the latest research findings of 3GPP on network sharing, this paper introduces 4 solutions to WCDMA 3G network sharing: site sharing, common network sharing, geographically split network sharing, and radio access network sharing. It also analyzes the key network sharing technologies, including the lu-Flex function in Release 5, the UTRAN sharing mechanism in the connected mode in Release 5 and the mechanism of network sharing support enhancement in Release 6.

Resurrection of GSM Core Network at WCDMA Age

The developing mobile technologies have resulted in operators focusing on how to optimize their 2G networks and ensure a smooth network end, there are two approaches for upgrade: one core network with a 2G-radio network, while the other is (Mobile Switching Center) with WCDMA MSCS (Mobile Gateway) to optimize the Core network hierararchy

Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Internet is used in wireless ad hoc network.Internet is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined environment.Internet fetches data from a fixed location.Data redundancy,mobility,and location dependency are the main issues of the IP network paradigm.All these factors result in poor performance of wireless ad hoc networks.The main disadvantage of IP is that,it does not provide in-network caching.Therefore,there is a need to move towards a new network that overcomes these limitations.Named Data Network(NDN)is a network that overcomes these limitations.NDN is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user queries.Implementing NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and privacy.By considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless AdHocNetwork.Various cachingmechanism-based results are also described.In the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

Design of an Interconnection Architecture and Sizing of Two (2) EPC Core Networks: The Case of Orange-Guinea

With the arrival of the 4G and 5G,the telecommunications networks have experienced a large expansion of these networks.That enabled the integration of many services and adequate flow,thus enabling the operators to respond to the growing demand of users.This rapid evolution has given the operators to adapt,their methods to the new technologies that increase.This complexity becomes more important,when these networks include several technologies to access different from the heterogeneous network like in the 4G network.The dimensional new challenges tell the application and the considerable increase in demand for services and the compatibility with existing networks,the management of mobility intercellular of users and it offers a better quality of services.Thus,the proposed solution to meet these new requirements is the sizing of the EPC(Evolved Packet Core)core network to support the 5G access network.For the case of Orange Guinea,this involves setting up an architecture for interconnecting the core networks of Sonfonia and Camayenne.The objectives of our work are of two orders:(1)to propose these solutions and recommendations for the heart network EPC sizing and the deployment to be adopted;(2)supply and architectural interconnection in the heart network EPC and an existing heart network.In our work,the model of traffic in communication that we use to calculate the traffic generated with each technology has link in the network of the heart.

Krige Method Application for the Coverage Analysis of a Node-B in a WCDMA Network

This paper shows the procedure and application of the Krige Method (or Kriging) for the analysis of the power level radiated by a Base Station (also called Node B), through a group of samples of this power level, measured at different positions and distances. These samples were obtained using an spectrum analyzer, which will allow to have georeferenced measurements, to implement the interpolation process and generate coverage maps, making possible to know the power level distribution and therefore understand the behavior and performance of the Node B.

Quality of Service (QoS) Evaluation in Wideband Code Division Multiplexing Access (WCDMA) Networks Using Empirical Analysis Methods

Performance evaluation is essential in maintaining the Quality of Service (QOS) of the Wideband Code Division Multiplexing Access (WCDMA). This work was motivated by the reception of the poor signals, increase call drop, failure rate which was a poor QoS Reception. The aim is to survey WCDMA services in Owerri environs and establish that there are degradation and the level of debasement in the network. The methodology involved an Empirical Analysis through Drive Test across Owerri City in Imo State. The work adopted the empirical approach and deduction of some Standard performance metrics like call drop rate, failure rate, call success rate, call completion rate, Handover success rate and handover Failure Rate, compare with expected KPI(key performance indicator) threshold. From the assessment, it was found that only one out Four Networks (“GLO”) met the target Call Drop Rate (CDR), Call Completion Success rate (CCSR), Call Setup Success Rate (CSSR) and Call Blocked Rate (CBR) and the Handover was better in “GLO” and 9 mobile than in the “MTN” and Airtel.

Improved Multiple-Scenario Radio Network Dimensioning for WCDMA

In wideband code division multiple access (WCDMA) cellular systems, the coverage radius of a cell depends on its current capacity level. As a result, existing WCDMA radio network dimensioning approaches require that coverage and capacity planning be carried out jointly in an iterative manner in order to obtain the minimum site count needed while fulfilling both coverage and capacity requirements. This requires relatively long computational time, particularly when there are many scenarios or what-if cases to be considered. To overcome this problem, we propose an alternative radio network dimensioning approach where coverage planning and capacity planning can be carried out separately to reduce computational time. Besides, a portion of the values calculated in the initial iteration is preserved in a lookup graph, allowing future what-if analysis to be accomplished rapidly. Simulation results show that, unlike the existing approach, the planning and what-if analysis times of the proposed dimensioning approach are independent of the number of sce-narios considered. Lastly, we present a few case studies and show that the proposed dimensioning method can give the same prediction accuracy as the existing method.

Verifying hierarchical network nonlocality in general quantum networks

Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.

JOINT RESOURCE ALLOCATION FOR WLAN&WCDMA INTEGRATED NETWORKS BASED ON SPECTRAL BANDWIDTH MAPPING

Next wireless network aims to integrate heterogeneous wireless access networks by sharing wireless resource.The spectral bandwidth mapping concept is proposed to uniformly describe the resource in heterogeneous wireless networks.The resources of codes and power levels in WCDMA system as well as statistical time slots in WLAN are mapped into equivalent bandwidth which can be allocated in different networks and layers.The equivalent bandwidth is jointly distributed in call admission and vertical handoff control process in an integrated WLAN/WCDMA system to optimize the network utility and guarantee the heterogeneous QoS required by calls.Numerical results show that,when the incoming traffic is moderate,the proposed scheme could receive 5%-10% increase of system revenue compared to the MDP based algorithms.

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

How do you evolve to a converged NG-GSM/WCDMA/LTE network without any extra hardware?

Easy-you start with the right base station. For example the all-new ZTE Multi-Carrier base station based on unified hardware platform that uses configurable software rather than separate hardware modules to support GSM and WCDMA simultaneously and achieve smooth evolution to LTE.

Soft Site Swap and Cutover in WCDMA Network

Traditionally,when a radio network site is swapped,it is necessary to interrupt existing services and perform a step-by-step serial cutover,which involves complex arrangements and huge resource investments.Therefore,there is an urgent need to discuss a fast and low-risk solution for site moving and cutover.ZTE Base Band Unit and Remote Radio Unit(BBU+RRU)Node B adopts distributed structure and combines full frequency reuse of the WCDMA system to conveniently implement soft site moving and cutover.The solution is to build an overlay network and make a one-time cutover without any impact on the operation of the existing network.Moreover,it supports timely fallback,and can considerably simplify the site moving and cutover operation and lower the network risk.

An improved multiplicative exponent weighting vertical handoff algorithm for WLAN/WCDMA heterogeneous wireless networks

An improved MEW （ muhiplicative exponent weighting） algorithm, SLE-MEW is proposed for vertical handoff decision in heterogeneous wireless networks. It introduces the SINR（ signal to interference plus noise ratio） effects, LS （least square） and information entropy method into the algorithm. An attribute matrix is constructed considering the SINR in the source network and the equivalent SINR in the target network, the required bandwidth, the traffic cost and the available bandwidth of participating access networks. Handoff decision meeting multi-attribute QoS（quality of serv- ice） requirement is made according to the traffic features. The subjective weight relation of decision elements is determined with LS method. The information entropy method is employed to derive the objective weights of the evaluation criteria, and lead to the comprehensive weight. Finally decision is made using MEW algorithm based on the attribute matrix and weight vector. Four 3GPP（ the 3rd generation partnership project） defined traffic classes are considered in performance evaluation. The simulation results have shown that the proposed algorithm can provide satisfactory performance fitting to the characteristics of the traffic.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.