Non-parametric statistical learning for URLLC transmission rate control

As an important service for 5G communications, ultra-reliable low-latency communications (URLLC) support emerging mission-critical applications, such as factory automation and autonomous driving. For such applications, the probability of failing to successfully transmit URLLC packets should be below...

Full description

Saved in:

Bibliographic Details
Main Authors:	Wenheng Zhang, Mahsa Derakhshani, Sangarapillai Lambotharan
Format:	Default Conference proceeding
Published:	2021
Subjects:	statistical learning non-parametric models kernel density estimation URLLC channel uncertainty
Online Access:	https://hdl.handle.net/2134/14096053.v1
Tags:	Add Tag No Tags, Be the first to tag this record!

id	rr-article-14096053
record_format	Figshare
spelling	rr-article-140960532021-08-06T00:00:00Z Non-parametric statistical learning for URLLC transmission rate control Wenheng Zhang (5854193) Mahsa Derakhshani (2572993) Sangarapillai Lambotharan (1252278) statistical learning non-parametric models kernel density estimation URLLC channel uncertainty As an important service for 5G communications, ultra-reliable low-latency communications (URLLC) support emerging mission-critical applications, such as factory automation and autonomous driving. For such applications, the probability of failing to successfully transmit URLLC packets should be below a certain threshold. However, in the case of limited knowledge of the channel distribution, achieving such a reliability target requires precise channel modeling. In this paper, we study applying a non-parametric statistical learning approach (i.e. kernel density estimation (KDE)) to estimate the information of the wireless transmission environment (i.e. the probability density function of the channel distribution). Based on the estimated cumulative distribution function, a transmission rate control technique has been developed and the corresponding reliability has been investigated using two measures representing the average performance and the confidence level. Moreover, this paper compares the performance of KDE and traditional empirical estimation scheme. The results show that KDE achieves a high level of confidence in guaranteeing the reliability constraint despite of the limited number of training data when choosing a suitable kernel bandwidth. 2021-08-06T00:00:00Z Text Conference contribution 2134/14096053.v1 https://figshare.com/articles/conference_contribution/Non-parametric_statistical_learning_for_URLLC_transmission_rate_control/14096053 All Rights Reserved
institution	Loughborough University
collection	Figshare
topic	statistical learning non-parametric models kernel density estimation URLLC channel uncertainty
spellingShingle	statistical learning non-parametric models kernel density estimation URLLC channel uncertainty Wenheng Zhang Mahsa Derakhshani Sangarapillai Lambotharan Non-parametric statistical learning for URLLC transmission rate control
description	As an important service for 5G communications, ultra-reliable low-latency communications (URLLC) support emerging mission-critical applications, such as factory automation and autonomous driving. For such applications, the probability of failing to successfully transmit URLLC packets should be below a certain threshold. However, in the case of limited knowledge of the channel distribution, achieving such a reliability target requires precise channel modeling. In this paper, we study applying a non-parametric statistical learning approach (i.e. kernel density estimation (KDE)) to estimate the information of the wireless transmission environment (i.e. the probability density function of the channel distribution). Based on the estimated cumulative distribution function, a transmission rate control technique has been developed and the corresponding reliability has been investigated using two measures representing the average performance and the confidence level. Moreover, this paper compares the performance of KDE and traditional empirical estimation scheme. The results show that KDE achieves a high level of confidence in guaranteeing the reliability constraint despite of the limited number of training data when choosing a suitable kernel bandwidth.
format	Default Conference proceeding
author	Wenheng Zhang Mahsa Derakhshani Sangarapillai Lambotharan
author_facet	Wenheng Zhang Mahsa Derakhshani Sangarapillai Lambotharan
author_sort	Wenheng Zhang (5854193)
title	Non-parametric statistical learning for URLLC transmission rate control
title_short	Non-parametric statistical learning for URLLC transmission rate control
title_full	Non-parametric statistical learning for URLLC transmission rate control
title_fullStr	Non-parametric statistical learning for URLLC transmission rate control
title_full_unstemmed	Non-parametric statistical learning for URLLC transmission rate control
title_sort	non-parametric statistical learning for urllc transmission rate control
publishDate	2021
url	https://hdl.handle.net/2134/14096053.v1
_version_	1768011658820583424

Non-parametric statistical learning for URLLC transmission rate control

Similar Items