scholarly journals Satellite Integration into 5G: Accent on First Over-The-Air Tests of an Edge Node Concept with Integrated Satellite Backhaul

2019 ◽  
Vol 11 (9) ◽  
pp. 193 ◽  
Author(s):  
Florian Völk ◽  
Konstantinos Liolis ◽  
Marius Corici ◽  
Joe Cahill ◽  
Robert T. Schwarz ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Satellite Networks ◽  
Base Stations ◽  
Geostationary Orbit ◽  
Core Network ◽  
5G Networks ◽  
Remote Areas ◽  
Edge Node ◽  
Satellite Integration

The 5G vision embraces a broad range of applications including the connectivity in underserved and remote areas. In particular, for these applications, satellites are going to play a role in future 5G networks to provide capacity on trains, vessels, aircraft, and for base stations around the globe. In this paper, a 5G edge node concept, developed and evaluated with over-the-air tests using satellites in the geostationary orbit, is presented. The article covers a testbed demonstration study in Europe with a large-scale testbed including satellites and the latest standardization for the network architecture. The main goal of this testbed is to evaluate how satellite networks can be best integrated within the convergent 5G environment. The over-the-air tests for 5G satellite integration in this article are based on a 3GPP Release 15 core network architecture.

scholarly journals A Study of 5G Edge-Central Core Network Split Options

Network ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 354-368
Author(s):  
Marius Corici ◽  
Pousali Chakraborty ◽  
Thomas Magedanz
Keyword(s):  
Network Management ◽  
Large Scale ◽  
Qualitative Assessment ◽  
Mobile Edge Computing ◽  
Core Network ◽  
5G Networks ◽  
Comprehensive Overview ◽  
5G Network ◽  
Infrastructure Services ◽  
Vertical Market

With the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of “on-premises” local 5G networks addressing the needs of different vertical industries and by new standardized infrastructure services such as Mobile Edge Computing (MEC). This article introduces a comprehensive set of deployment options for the 5G network and its network management, complementing MEC with the connectivity service and addressing different classes of use cases and applications. We have also practically implemented and tested the newly introduced options in the form of slices within a standard-based testbed. Our performed validation proved their feasibility and gave a realistic perspective on their impact. The qualitative assessment of the connectivity service gives a comprehensive overview on which solution would be viable to be deployed for each vertical market and for each large-scale operator situation, making a step forward towards automated distributed 5G deployments.

scholarly journals Wireless Sensor Networks Suitable for Large-Scale Heterogeneous Networking

2019 ◽  
Vol 15 (01) ◽  
pp. 58
Author(s):  
Xi Wang ◽  
Feifei Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Saving ◽  
Network Architecture ◽  
Large Scale ◽  
Base Stations ◽  
Wireless Sensor ◽  
Relay Nodes ◽  
Heterogeneous Nodes ◽  
Core Idea

In order to optimize the network architecture, addressing mechanism, heterogeneous nodes and other functions of wireless sensor networks, this study begins with the issue of networking of large-scale heterogeneous networks. A layered distributed network architecture is proposed, which provides a powerful reference for the future architecture of wireless sensor networks. Based on this architecture, the resource addressing of the corresponding hierarchical network, and the scale and location deployment of heterogeneous nodes such as sink nodes are discussed separately, and corresponding strategies and algorithms are proposed. The research results show that the core idea of the addressing mechanism is data-centric, address-oriented addressing is transformed into service-oriented addressing. Therefore, the proposed LBA addressing algorithm is suitable for other hierarchically structured networks. In addition, although the sink node is taken as an example for research, it is also suitable for the deployment of other heterogeneous nodes such as sink nodes, relay nodes, and base stations. In summary, regardless of the number of nodes or the location of the deployment, energy-saving factors need to be considered. Energy-saving is also an indispensable technology in wireless sensor network technology.

scholarly journals Dynamic Set Planning for Coordinated Multi-Point in B4G/5G Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1752
Author(s):  
Jia-Ming Liang ◽  
Ching-Kuo Hsu ◽  
Jen-Jee Chen ◽  
Po-Han Lin ◽  
Po-Min Hsu ◽  
...  
Keyword(s):  
Large Scale ◽  
Base Stations ◽  
System Throughput ◽  
Transmission Power ◽  
5G Networks ◽  
Radio Resource ◽  
Set Size ◽  
Full Study ◽  
Radio Resources ◽  
Save Energy

Coordinated Multi-Point (CoMP) is an important technique in B4G/5G networks. With CoMP, multiple base stations can be clustered to compose a cooperating set to improve system throughput, especially for the users in cell edges. Existed studies have discussed how to mitigate overloading scenarios and enhance system throughput with CoMP statically. However, static cooperation fixes the set size and neglects the fast-changing of B4G/5G networks. Thus, this paper provides a full study of off-peak hours and overloading scenarios. During off-peak hours, we propose to reduce BSs’ transmission power and use the free radio resource to save energy while guaranteeing users’ QoS. In addition, if large-scale activities happen with crowds gathering or in peak hours, we dynamically compose the cooperating set based on instant traffic requests to adjust base stations’ BSs’ transmission power; thus, the system will efficiently offload the traffic to the member cells which have available radio resources in the cooperating set. Experimental results show that the proposed schemes enhance system throughput, radio resource utilization, and energy efficiency, compared to other existing schemes.

3D deployment of UAVs in wireless networks for traffic offloading and edge computing

2019 ◽  
Author(s):  
◽  
Rania Islambouli
Keyword(s):  
Wireless Networks ◽  
Large Scale ◽  
Scale Up ◽  
Integer Program ◽  
Use Cases ◽  
Base Stations ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
5G Networks ◽  
Iot Devices

Unmanned aerial vehicles (UAVs) have recently emerged as enablers for mul- titude use cases in 5G networks leading to interesting industrial and business applications. 5G networks envision a multi-service network promoting various applications with a distinct set of performance and service demands. In this the- sis, we leverage the high exibility, low-cost, and mobility of UAVs to scale up and improve the e ciency of IoT and mobile networks. We study the utilization of UAVs to increase the capacity and coverage in wireless networks on one side and to extend low computational capabilities and mitigate battery limitations in constrained devices on another side. However, to unlock these promising use cases of UAVs, we address the challenges coupled with UAV utilization mainly 3D deployment and device association. First, we address the problem of deploying multiple UAVs to act as aerial base stations (ABS) in 3D space while autonomously adapting their positions as users move around within the network. We formulate the problem as a mixed integer program and then propose a novel autonomous positioning approach that can e ciently gear the UAV positions in a way to maintain target quality re- quirements. Next, we leverage the mobility and agility of UAVs and use them as mo- bile edge servers or cloudlets to o er computation o oading opportunities to IoT devices. This being said, computation tasks generated by IoT devices can be pro- cessed in less latency and with much lower energy consumption at the devices. To optimally deploy UAVs as mounted cloudlets, we formulate our problem as mixed integer program and then use an e cient meta-heuristic algorithm to generate optimized results for large scale IoT networks. The simulation results presented in this thesis demonstrate the e ectiveness of the proposed solutions and algo- rithms compared to the optimal solutions and related work in the literature for various network scenario

scholarly journals Signalling load reduction in 5G network based on cloud radio access network architecture

2021 ◽  
Vol 11 (6) ◽  
pp. 5127
Author(s):  
Mohammed Abbas Waheed ◽  
Azzad Bader Saeed ◽  
Thanaa Hussein Abd
Keyword(s):  
Network Architecture ◽  
Access Network ◽  
Base Stations ◽  
Core Network ◽  
Matlab Simulation ◽  
Traditional Architecture ◽  
Radio Access Network ◽  
Cloud Radio Access Network ◽  
Radio Access ◽  
Novel Approach

The rapid growth of both mobile users and application numbers has caused a huge load on the core network (CN). This is attributed to the large numbers of control messages circulating between CN entities for each communication or service request, however, making it imperative to develop innovative designs to handle this load. Consequently, a variety of proposed architectures, including a software defined network (SDN) paradigm focused on the separation of control and data plans, have been implemented to make networks more flexible. Cloud radio access network (C-RAN) architecture has been suggested for this purpose, which is based on separating base band units (BBU) from several base stations and assembling these in one place. In this work, a novel approach to realize this process is based on SDN and C-RAN, which also distributes the control elements of the CN and locates them alongside the BBU to obtain the lowest possible load. The performance of this proposed architecture was evaluated against traditional architecture using MATLAB simulation, and. the results of this assessment indicated a major reduction in signalling load as compared to that seen in the traditional architecture. Overall, the number of signalling messages exchanged between control entities was decreased by 53.19 percent as compared to that seen in the existing architecture.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

A Review on 3GPP 5G Security Aspects

2019 ◽  
Author(s):  
Rajavelsamy R ◽  
Debabrata Das
Keyword(s):  
Network Architecture ◽  
Base Station ◽  
Cellular Systems ◽  
Core Network ◽  
User Privacy ◽  
Wireless Backhaul ◽  
Level Of Use ◽  
Unified Authentication ◽  
3Rd Generation Partnership Project ◽  
5G Security

5G promises to support new level of use cases that will deliver a better user experience. The 3rd Generation Partnership Project (3GPP) [1] defined 5G system introduced fundamental changes on top of its former cellular systems in several design areas, including security. Unlike in the legacy systems, the 5G architecture design considers Home control enhancements for roaming customer, tight collaboration with the 3rd Party Application servers, Unified Authentication framework to accommodate various category of devices and services, enhanced user privacy, and secured the new service based core network architecture. Further, 3GPP is investigating the enhancements to the 5G security aspects to support longer security key lengths, False Base station detection and wireless backhaul in the Phase-2 of 5G standardization [2]. This paper provides the key enhancements specified by the 3GPP for 5G system, particularly the differences to the 4G system and the rationale behind the decisions.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

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