Non-parametric Statistical Learning for URLLC Transmission Rate Control.pdf (277.62 kB)
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Non-parametric statistical learning for URLLC transmission rate control

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conference contribution
posted on 23.02.2021, 16:59 by Wenheng Zhang, Mahsa DerakhshaniMahsa Derakhshani, Sangarapillai LambotharanSangarapillai Lambotharan
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.

Funding

Royal Academy of Engineering under the Leverhulme Trust Research Fellowship scheme Derakhshani-LTRF1920\16\67)

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

ICC 2021 - IEEE International Conference on Communications

Source

IEEE International Conference on Communications

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

24/01/2021

Publication date

2021-08-06

Copyright date

2021

ISBN

9781728171227

eISSN

1938-1883

Language

en

Location

Montreal, QC, Canada (Virtual)

Event dates

14th June 2021 - 23rd June 2021

Depositor

Dr Mahsa Derakhshani. Deposit date: 23 February 2021