Fifth Generation Wireless Channels Outlier Detection and Clustering

Citation Author(s):
Jojo
Blanza
John Bernard
Cipriano
Submitted by:
Jojo Blanza
Last updated:
Mon, 05/27/2024 - 05:22
DOI:
10.21227/q88v-xm96
Data Format:
License:
0
0 ratings - Please login to submit your rating.

Abstract 

The fifth generation (5G) wireless communications system offers faster data rates, lower latency, and higher number of interconnecting devices. Various 5G channel models were developed to study its stochastic characteristics prior to its implementation. These channel models generate multipath components that are grouped into clusters when they have similar properties in delay and angles. The multipaths and multipath clusters are used as datasets in multipath clustering which is used to examine the propagation properties of the 5G system. However, datasets are prone to outliers. They tend to affect clustering accuracy. Hence, this study clusters the datasets generated by the channel models, remove the outliers, and cluster again the datasets free of outliers. Outlier detection shows 5G channel model datasets contain noise and outlier removal improves the modelling characteristics shown by improved clustering accuracy. Results show that most of the outliers are detected in the 2*SD theshold. The removal of the outliers increased the clustering accuracy. This shows that outlier detection and removal also work well with channel model datasets and can be used in analyzing the propagation characteristics of 5G.

Instructions: 

Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in any component of these datasets/codes are trade names, service marks, trademarks or registered trademarks of their respective owners. The author(s)/publisher(s)/authorizing body is/are not associated with any product or vendor mentioned in any component of these datasets/codes.

The inclusion of an organization name, product, or service in any part of these datasets/codes should not be construed as an endorsement of such organization, product, or service, nor is failure to include an organization name, product, or service to be construed as disapproval.

Any component or publication of these datasets/codes is designed to provide accurate and authoritative information in regard to the subject matter covered. Every attempt has been made to ensure accuracy at the time of publication. Any component or publication of these datasets/codes is made on the understanding that the the author(s)/publisher(s)/authorizing body is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought.

Any statements expressed in any component or publication of these datasets/codes are those of the individual authors and do not necessarily represent the views of the authors' affiliations, which takes no responsibility for any statement made herein. No reference made in this publication to any specific method, product, process, or service constitutes or implies an endorsement, recommendation, or warranty thereof by the authors' affiliations. The materials are for general information only and do not represent a standard of the authors' affiliations, nor are they intended as a reference in applicable  specifications, contracts, regulations, statutes, or any other legal document. The authors' affiliations makes no representation or warranty of any kind, whether express or implied, concerning the accuracy, completeness, suitability, or utility of any information, apparatus, product, or process discussed in this publication, and assumes no liability therefor. The information contained in these materials should not be used without first securing competent advice with respect to its suitability for any general or specific application. Anyone utilizing such information assumes all liability arising from such use, including but not limited to infringement of any patent or patents.