scholarly journals Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks

2021 ◽  
Vol 3 (3) ◽  
pp. 161-174
Author(s):  
Joy Iong-Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Wireless Network ◽  
Network Architecture ◽  
Stochastic Geometry ◽  
Large Scale ◽  
Ad Hoc ◽  
Scheduling Algorithm ◽  
Analytical Framework ◽  
Massive Growth ◽  
Monte Carlo Simulator ◽  
And Performance

Stochastic Geometry has attained massive growth in modelling and analysing of wireless network. This suits well for analysing the performance of large scale wireless network with random topologies. Analytical framework is established to evaluate the performance of the network. Here we have created a mathematical model for uplink analysis and the gain of uplink and downlink is obtained. Then ad-hoc network architecture is designed and the performance of the network is compared with the traditional method. Finally, a new scheduling algorithm is developed for cellular network and the gain parameter is quantified with the help of Stochastic Geometry tool. The accuracy is acquired from extensive Monte Carlo simulator.

Building New Reliable Wireless Network by Using Distributed Control

2011 ◽  
Vol 84-85 ◽  
pp. 160-166
Author(s):  
Xin Ying Wang
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Distributed Control ◽  
Network Architecture ◽  
Large Scale ◽  
Complex Function ◽  
Packet Delay ◽  
Control Performance ◽  
Wireless Data ◽  
Packet Losses

With the development of the pioneering deployments in multi-hop wireless networks, although the relative research have not proven successful. The performance of routing and transport is often unstable due to contention induced packet losses, especially when the network is large and the offered load is high. A reliable wireless network architecture by using distributed control for large-scale multi-hop wireless networks has been present in this paper, The design objective is to optimize the control performance. This control performance is a complex function of the network parameters, such as throughput, packet delay and packet loss probabilities. The goal of optimizing the control performance imposes implicit tradeoffs on the wireless network design as opposed to the explicit tradeoffs typical in wireless data and voice applications. Our analysis suggests that our approach will deliver improved service to users while greatly reducing support effort and cost.

scholarly journals Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking

2019 ◽  
Vol 11 (6) ◽  
pp. 139 ◽  
Author(s):  
Meng Kuai ◽  
Xiaoyan Hong
Keyword(s):  
Data Acquisition ◽  
Autonomous Vehicles ◽  
Network Architecture ◽  
Large Scale ◽  
Ad Hoc ◽  
Named Data Networking ◽  
Global Knowledge ◽  
Broadcast Storm ◽  
Broadcast Schemes ◽  
Data Networking

The emerging connected and autonomous vehicles (CAVs) challenge ad hoc wireless multi-hop communications by mobility, large-scale, new data acquisition and computing patterns. The Named Data Networking (NDN) is suitable for such vehicle ad hoc networks due to its information centric networking approach. However, flooding interest packets in ad-hoc NDN can lead to broadcast storm issue. Existing solutions will either increase the number of redundant interest packets or need a global knowledge about data producers. In this paper, a Location-Based Deferred Broadcast (LBDB) scheme is introduced to improve the efficiency and performance of interest broadcast in ad-hoc NDN. The scheme takes advantage of location information to set up timers when rebroadcasting an interest. The LBDB is implemented in V-NDN network architecture using ndnSIM simulator. Comparisons with several existing protocols are conducted in simulation. The results show that LBDB improves the overhead, the average number of hops and delay while maintaining an average satisfaction ratio when compared with several other broadcast schemes. The improvement can help offer timely data acquisition for quick responses in emergent CAV application situations.

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.

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