Towards ultra-latency using deep learning in 5G Network Slicing applying Approximate k-Nearest Neighbor Graph Construction

2021 ◽  
Author(s):  
Rohit Kumar Gupta ◽  
Praduman Pannu ◽  
Rajiv Misra
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Network Slicing ◽  
Neighbor Graph ◽  
Novel Approach ◽  
5G Network ◽  
Remote Healthcare ◽  
Nearest Neighbor Graph ◽  
Deep Learning Neural Network

Abstract The 5G Network Slicing with SDN and NFV have expended to support new-verticals such as intelligent transport, industrial automation, remote healthcare. Network slice is intended as parameter configurations and a collection of logical network functions to support particular service requirements. The network slicing resource allocation and prediction in 5G networks is carried out using network Key Performance Indicators (KPIs) from the connection request made by the devices on joining the network. We explore derived features as the network non-KPI parameters using the k-Nearest Neighbor (kNN) graph construction. In this paper, we use kNN graph construction algorithms to augment the dataset with triangle count and cluster coecient properties for ecient and reliable network slice. We used deep learning neural network model to simulate our results with KPIs and KPIs with non-KPI parameters. Our novel approach found that at k=3 and k=4 of the kNN graph construction gives better results and overall accuracy is imroved around 29%.

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

A Novel clustering method based on hybrid K-nearest-neighbor graph

2018 ◽  
Vol 74 ◽  
pp. 1-14 ◽  
Author(s):  
Yikun Qin ◽  
Zhu Liang Yu ◽  
Chang-Dong Wang ◽  
Zhenghui Gu ◽  
Yuanqing Li
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Clustering Method ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

Region-Based Graph Learning towards Large Scale Image Annotation

2012 ◽  
pp. 244-260
Author(s):  
Bao Bing-Kun ◽  
Yan Shuicheng
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Image Annotation ◽  
Learning Algorithm ◽  
Label Propagation ◽  
Locality Sensitive Hashing ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph ◽  
Modeling Data

Graph-based learning provides a useful approach for modeling data in image annotation problems. In this chapter, the authors introduce how to construct a region-based graph to annotate large scale multi-label images. It has been well recognized that analysis in semantic region level may greatly improve image annotation performance compared to that in whole image level. However, the region level approach increases the data scale to several orders of magnitude and lays down new challenges to most existing algorithms. To this end, each image is firstly encoded as a Bag-of-Regions based on multiple image segmentations. And then, all image regions are constructed into a large k-nearest-neighbor graph with efficient Locality Sensitive Hashing (LSH) method. At last, a sparse and region-aware image-based graph is fed into the multi-label extension of the Entropic graph regularized semi-supervised learning algorithm (Subramanya & Bilmes, 2009). In combination they naturally yield the capability in handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets well validate the effectiveness and efficiency of the framework for region-aware and scalable multi-label propagation.

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