Cellular Network Architecture Research Articles

Methodological Aspects and Mesh Convergence in Numerical Analysis of Athermal Fiber Network Material Deformation

A balance between model complexity, accuracy, and computational cost is a central concern in numerical simulations. In particular, for stochastic fiber networks, the non-affine deformation of fibers, related non-linear geometric features due to large global deformation, and size effects can significantly affect the accuracy of the computer experiment outputs and increase the computational cost. In this work, we systematically investigate methodological aspects of fiber network simulations with a focus on the output accuracy and computational cost in models with cellular (Voronoi) and fibrous (Mikado) network architecture. We study both p and h-refinement of the discretizations in finite element solution procedure, with uniform and length-based adaptive h-refinement strategies. The analysis is conducted for linear elastic and viscoelastic constitutive behavior of the fibers, as well as for networks with initially straight and crimped fibers. With relative error as the determining criterion, we provide recommendations for mesh refinement, comment on the necessity of multiple realizations, and give an overview of associated computational cost that will serve as guidance toward minimizing the computational cost while maintaining a desired level of solution accuracy.

Multi-Scale Spatial–Spectral Attention-Based Neural Architecture Search for Hyperspectral Image Classification

Convolutional neural networks (CNNs) are indeed commonly employed for hyperspectral image classification. However, the architecture of cellular neural networks typically requires manual design and fine-tuning, which can be quite laborious. Fortunately, there have been recent advancements in the field of Neural Architecture Search (NAS) that enable the automatic design of networks. These NAS techniques have significantly improved the accuracy of HSI classification, pushing it to new levels. This article proposes a Multi-Scale Spatial–Spectral Attention-based NAS, MS3ANAS) framework for HSI classification to automatically design a neural network structure for HSI classifiers. First, this paper constructs a multi-scale attention mechanism extended search space, which considers multi-scale filters to reduce parameters while maintaining large-scale receptive field and enhanced multi-scale spectral–spatial feature extraction to increase network sensitivity towards hyperspectral information. Then, we combined the slow–fast learning architecture update paradigm to optimize and iteratively update the architecture vector and effectively improve the model’s generalization ability. Finally, we introduced the Lion optimizer to track only momentum and use symbol operations to calculate updates, thereby reducing memory overhead and effectively reducing training time. The proposed NAS method demonstrates impressive classification performance and effectively improves accuracy across three HSI datasets (University of Pavia, Xuzhou, and WHU-Hi-Hanchuan).

5G Wireless Networks

Everybody loves speed and moreover speedy internet, so its no surprise that every major telecom in the world is working to make it even faster. Smartphones, watches, homes, and cars are increasingly requiring stable internet connections. In order to survive in the world where in every second the speed changes and where we urge for more and more technology, here comes the fifth generation technology: 5G. In future, i.e., a world beyond 4G, some of the prime objectives that need to be fulfilled are increased capacity, improved data rate, decreased latency, and quality service. To meet these demands, large scale improvement in the cellular architecture of 5G is required .This paper basically lays emphasis on the 5th generation i.e. 5G cellular network architecture and some of the essential emerging technologies that can prove fruitful in humanizing the architecture and summiting the demands of users. This paper is contented with the details related to 5g with the prime focus on the massive multiple input multiple output technology and device-to-device communication (D2D). A general credible 5G cellular network architecture is being proposed with the guideline taken from the internet books and by the detailed study of the topic

Future of 5G Wireless System

Every major telecom in the globe is attempting to make it even faster because everyone loves speed and, more specifically, fast internet. Smartphone’s, Stable internet connections are becoming more and more important for watches, houses, and vehicles. The fifth generation of technology, or 5G, is coming to help us survive in a world where pace is changing every second and where we need more and more technology. Some of the most important goals that must be achieved in the future, or in a world beyond 4G, are higher capacity, improved data rate, lower latency, and quality service. Large-scale improvements in the 5G cellular architecture are necessary to meet these expectations. Essentially, this study emphasizes the architecture of the fifth generation (5G) of mobile networks and some of the most important upcoming technologies that can help meet user demands while humanizing the architecture. The primary focus of this paper's coverage of 5G details is device-to-device communication and huge multiple input multiple output technologies (D2D). A general, believable 5G cellular network architecture is put out using guidelines from online sources and thorough research on the subject

A uw-Cellular Network: Design, Implementation and Experiments

The most significant increase of current task is in the desire for operational flexibility and agility in large-scale underwater network application scenarios in recent years. In order to address the challenging problems in Underwater Wireless Sensor Networks (UWSNs), we propose a large-scale UWSN based on the cellular network architecture called Underwater Cellular (uw-Cellular) network. It is designed especially for application scenarios where a large number of both fixed and mobile network nodes exist in a wide area to monitor the underwater environment. We also design protocols in each network layer in order to ensure reasonable channel sharing, data forwarding path selection and data reliability. The purpose of the simulation study we implement is to evaluate the performance of the CLA routing strategy compared to the VBF and the MFLOOD protocols in the uw-Cellular network. We also deploy a twenty-node uw-Cellular network in the real-world environment as the field case study. The experimental results showed that the Data Rate between any nodes could reach above 500 bps, and the network Average Throughput was no less than 550 bps under various load situations.

Future of 5G Wireless Systems: A Review

Everybody loves speed and moreover speedy internet, so it’s no surprise that every major telecom in the world is working to make it even faster. Smartphone’s, watches, homes, and cars are increasingly requiring stable internet connections. In order to survive in the world where in
 every second the speed changes and where we urge for more and more technology, here comes the fifth generation technology: 5G. In future, i.e., a world beyond 4G, some of the prime objectives that need to be fulfilled are increased capacity, improved data rate, decreased latency, and quality service. To meet these demands, large scale improvement in the cellular architecture of 5G is required .This paper basically lays emphasis on the 5th generation i.e. 5G cellular network architecture and some of the essential emerging technologies that can prove fruitful in humanizing the architecture and summiting the demands of users.
 This paper is contented with the details related to 5g with the prime focus on the massive multiple input multiple output technology and device-to-device communication (D2D). A general credible 5G cellular network architecture is being proposed with the guideline taken from the internet books and by the detailed study of the topic.
 Beginning with an exploration of the evolution from 4G to 5G, we dissect the technical intricacies that empower 5G networks, including spectrum allocation, massive MIMO, and network slicing.
 The paper navigates through the diverse spectrum of applications that 5G facilitates, from Enhanced Mobile Broadband (eMBB) .As 5G integrates with emerging technologies such as edge computing and artificial intelligence, we unravel the synergies that are reshaping industries and fostering innovation.

Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.

NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G

ABSTRACTNetwork softwarization has revolutionized the architecture of cellular wireless networks. State‐of‐the‐art container based virtual radio access networks (vRAN) provide enormous flexibility and reduced life‐cycle management costs, but they also come with prohibitive energy consumption. We argue that for future AI‐native wireless networks to be flexible and energy efficient, there is a need for a new abstraction in network softwarization that caters for neural network type of workloads and allows a large degree of service composability. In this paper we present the NeuroRAN architecture, which leverages stateful function as a user facing execution model, and is complemented with virtualized resources and decentralized resource management. We show that neural network based implementations of common transceiver functional blocks fit the proposed architecture, and we discuss key research challenges related to compilation and code generation, resource management, reliability and security.

Effect of the pH value of tannin extracts on properties of classic tannin foams

Natural tannin-based foams are three-dimensional porous materials. Many different formulations have been investigated in order to reach foams with improved performance and increased commercial attractiveness. In this sense, each different tannin pH may impart different physical and thermochemical properties to the foams, which has not yet been fully elucidated. In the present study, seven rigid tannin foams (formaldehyde/sulfur-free) were produced using tannin extracts with variable pH values ( c.a. from 2.3 to 12.2). The most acidic tannin extracts (below 4.8) yielded tannin foams with the most lightweight (apparent density below 0.077 g.cm−3), homogeneous, and hydrophobic (water solubility below 23.5%) cellular-network architectures. Also, in most cases, the smaller the tannin pH, the higher the thermal stability of the foam. Further studies may address other technological requirements needed for thermal insulation applications.

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information.

Efficient processing of large-scale time-series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand-engineered feature extraction often involve huge computational costs with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with the increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multidimensional time-series data with spatial information. The cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time-series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for the classification of multiclass time-series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series data sets: a multichannel scalp electroencephalogram (EEG) data set for seizure detection, and a machine fault detection data set obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

Review Paper on 5G Network

Everyone loves speed and besides speedy internet so it is not surprising that all the major telecommunications companies in the world is working to make it even faster. Smartphones, watches, homes and cars require more and more stable internet connections. In order to survive in the world where in every second the speed changes and where we ask for more and more technology, here comes the fifth generation technology: 5G. In future any world beyond 4Gsome of the main goals to be achived are increased capacity, improved data rate, decreased latency, and quality service. To meet these demands, large-scale improvements in the cellular architecture of 5G network is required for This paper basically emphasizes the 5 th generation i.e. 5G mobile network architecture and some of the emerging essential technologies that can provefruitful in humanizing the architecture and meeting the demands of users. This paper contents with 5g related details with the prime focus on the massive technology of multiple input and multiple output and device-to-device communication (D2D). 5G with public credible cellular network architecture is being proposed with the guideline taken from the internet books and by the detailed study of the topic.

Content Media Retrieval using Virtual Network Functions in Multi‐access Edge Computing architecture

SummaryThe mobile traffic grows every year. Resource requirements of mobile applications, as processing power and storage capacity, transformed the architecture of cellular networks into a centralized infrastructure, the C‐RANs. On the other hand, providing resources close to base stations, as the multi‐access edge computing suggests, allows an immediate processing for delay sensitive applications. In this work, we formulate as a mixed integer linear programming the retrieval of media contents through caches acting as virtual network functions. The optimization model minimizes both the media contents' retrieval cost and the number of instantiated virtual network functions. Results show that the characteristic that has most impact on caching is the high throughput between the node that stores the virtual network function images and the virtualized environment that hosts the image, where up to 70% of the requests are satisfied. When caches have low storage capacity and incurs high transferring delay cost for deployment, the most important characteristic is the throughput distribution between the virtualized environment and the base stations.

RobustSTL and Machine-Learning Hybrid to Improve Time Series Prediction of Base Station Traffic

Green networking is currently becoming an urgent compulsion applied for cellular network architecture. One of the treatments that can be undertaken to fulfill such an objective is a traffic-aware scheme of a base station. This scheme can control the power consumption of the cellular network based on the number of demands. Then, it requires an understanding of estimated traffic in future demands. Various studies have undertaken experiments to obtain a network traffic prediction with good accuracy. However, dynamic patterns, burstiness, and various noises hamper the prediction model from learning the data traffic comprehensively. Furthermore, this paper proposes a prediction model using deep learning of one-dimensional deep convolutional neural network (1DCNN) and gated recurrent unit (GRU). Initially, this study decomposes the network traffic data by RobustSTL, instead of standard STL, to obtain the trend, seasonal, and residual components. Then, these components are fed into the 1DCNN-GRU as input data. Through the decomposition method using RobustSTL, the hybrid model of 1DCNN-GRU can completely capture the pattern and relationship of the traffic data. Based on the experimental results, the proposed model overall outperforms the counterpart models in MAPE, RMSE, and MAE metrics. The predicted data of the proposed model can follow the patterns of actual network traffic data.

Device and network driven cellular networks architecture and mobility management technique for fog computing-based mobile communication system

In this paper, we propose a metric and performance-based mode switching procedure for 5G and beyond mobile fog networks. The network interchange mode of communication between network-centric and device-centric modes, the mode switching procedure is subject to the network’s quality of service (QoS) and quality of experience (QoE) parameters. If the quality parameters are less than the pre-set threshold value, the network selects a relay node as intermediate nodes to avail the device-centric mode of communication. During the communication, the network information’s are retrieved from the nearby fog nodes and the process by intelligent base stations. Our next contribution is a relay node assisted mobility management procedure. As the mobile nodes change the position frequently within the network service area, an efficient mobility management procedure is required to improve the network’s performance. We propose a unique mobility management procedure, it follows the movement prediction procedure and helps the mobile nodes to avail the device-centric communication in dynamic fog networks, we classify it for dynamic and static mobile users. To check the effectiveness, efficiency, and parameterized dependency of the proposed work, we formulate the process with respect to several dependent factors and comparing our results with the latest state-of-the-art networks technologies subject to communication delay and latency, energy consumption, without any compromise in node mobility and call to mobility ratio. Finally, we achieve better performance to gain the QoS and QoE.

AN ANALYSIS OF 5G WIRELESS NETWORKS

Every major telecom in the world is working to make it even faster because everyone loves speed and, more specifically, fast internet. Stable internet connections are becoming more and more important for smartphones, watches, homes, and automobiles. The fifth generation of technology, or 5G, is here to help us survive in a world where speed is changing every second and where we demand more and more technology. Some of the most important goals that must be achieved in the future, or in a world beyond 4G, are increased capacity, improved data rate, decreased latency, and quality service. Large-scale improvements in the 5G cellular architecture are necessary to meet these demands. The 5G cellular network architecture and some of the key emerging technologies that can help the architecture become more human and better meet user demands are primarily the focus of this paper. The primary focus of this paper’s coverage of 5G details is device-to-device communication and massive multiple input multiple output technology (D2D). A general, credible 5G cellular network architecture is put forth using guidelines from online books and thorough research on the subject.

Machine Learning at the Edge: A Data-Driven Architecture With Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers’ view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

3D mesoscopic architecture of a heterogeneous cellular network in the cementum-periodontal ligament-alveolar bone complex.

Cell-to-cell communication orchestrates various cell and tissue functions. This communication enables cells to form cellular networks with each other through direct contact via intercellular junctions. Because these cellular networks are closely related to tissue and organ functions, elucidating the morphological characteristics of cellular networks could lead to the development of novel therapeutic approaches. The tooth, periodontal ligament (PDL) and alveolar bone form a complex via collagen fibres. Teeth depend on the co-ordinated activity of this complex to maintain their function, with cellular networks in each of its three components. Imaging methods for three-dimensional (3D) mesoscopic architectural analysis include focused ion beam/scanning electron microscopy (FIB/SEM), which is characterized by its ability to select observation points and acquire data from complex tissue after extensive block-face imaging, without the need to prepare numerous ultrathin sections. Previously, we employed FIB/SEM to analyse the 3D mesoscopic architecture of hard tissue including the PDL, which exists between the bone and tooth root. The imaging results showed that the cementum, PDL and alveolar bone networks are in contact and form a heterogeneous cellular network. This cellular network may orchestrate mechanical loading-induced remodelling of the cementum-PDL-alveolar bone complex as the remodelling of each complex component is coordinated, as exemplified by tooth movement due to orthodontic treatment and tooth dislocation due to occlusal loss. In this review, we summarize and discuss the 3D mesoscopic architecture of cellular networks in the cementum, PDL and alveolar bone as observed in our recent mesoscopic and morphological studies.

Convergence of broadband and broadcast/multicast in maritime information networks

Recently, the fifth-generation (5G) of wireless networks mainly focuses on the terrestrial applications. However, the well-developed emerging technologies in 5G are hardly applied to the maritime communications, resulting from the lack of communication infrastructure deployed on the vast ocean, as well as different characteristics of wireless propagation environment over the sea and maritime user distribution. To satisfy the expected plethora of broadband communications and multimedia applications on the ocean, a brand-new maritime information network with a comprehensive coverage capacity in terms of all-hour, all-weather, and all-sea-area has been expected as a revolutionary paradigm to extend the terrestrial capacity of enhanced broadband, massive access, ultra-reliable, and low-latency to the vast ocean. Further considering the limited available resource of maritime communication infrastructure, the convergence of broadband and broadcast/multicast can be regarded as a possible yet practical solution for realizing an efficient and flexible resource configuration with high quality of services. Moreover, according to such multi-functionality and all-coverage maritime information network, the monitoring and sensing of vast ocean area relying on massive Ocean of Things and advanced radar techniques can be also supported. Concerning these issues above, this study proposes a Software Defined Networking (SDN) based Maritime Giant Cellular Network (MagicNet) architecture for broadband and multimedia services. Based on this network, the convergence techniques of broadband and broadcast/multicast, and their supporting for maritime monitoring and marine sensing are also introduced and surveyed.

Energy-Saving D2D Wireless Networking Based on ACO and AIA Fusion Algorithm

Lower energy consumption and higher data rate have been becoming the key factors of modern wireless mobile communication for the improvement of user experiences. At present, the commercialization of 5G communications is gradually promoting the development of Internet of things (IoT) techniques. Due to the limited coverage capability of direct wireless communications, the indirect device-to-device (D2D) communications using information relay, in addition to the single 5G base station deployment, have been introduced. Along with the increase of information nodes, the relay devices have to undertake the nonnegligible extra data traffic. In order to adjust and optimize the information routing in D2D services, we present an algorithmic investigation referring to the ant colony optimization (ACO) algorithm and the artificial immune algorithm (AIA). By analyzing the characteristics of these algorithms, we propose a combined algorithm that enables the improved the iterative convergence speed and the calculation robustness of routing path determination. Meanwhile, the D2D optimization pursuing energy saving is numerically demonstrated to be improved than the original algorithms. Based on the simulation results under a typical architecture of 5G cellular network including various information nodes (devices), we show that the algorithmic optimization of D2D routing is potentially valid for the realization of primitive wireless IoT networks.

Study of cell clustering optimization algorithm for load balancing in 5G scenario

In the context of 5G networks at this stage, massive data transmission and highly dense deployment of base stations make severe interference and huge load volumes an inevitable new challenge. The traditional interference coordinated cell-clustering scheme only considers the inter-micro base station path loss without jointly considering load balancing, an important factor affecting system performance. This paper first introduces the existing cell-clustering schemes, and then proposes a cell-clustering optimization algorithm that combines path loss and load balancing on the basis of 5G cellular network architecture, and then simulate the optimization algorithm and compare the results with the traditional schemes to verify its ability to balance the load, increase the amount of transceiver data, and optimize the resource allocation and performance effects.