Bayesian optimization of blocklength for URLLC under channel distribution uncertainty
For block fading channels with uncertainty in channel distribution knowledge, we propose and optimize a statistical measure as a way to surely assess reliability in finite-block communications regime. In particular, the confidence level in guaranteeing average block-error rate lower than a specific target is introduced and maximized to find the optimal blocklength, aiming to meet the strict requirements of ultra-reliable low latency communications (URLLC). In order to compute the confidence level, non-parametric learning algorithms are employed for channel modeling with a limited number of training samples. Bayesian optimization, i.e., the tool for black-box optimization, is applied to solve the problem in the absence of the closed form of the confidence level.
Funding
Royal Academy of Engineering under the Leverhulme Trust Research Fellowship scheme (DerakhshaniLTRF1920\16\67)
Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
Engineering and Physical Sciences Research Council
Find out more...History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)Source
2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2022 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
2022-04-25Publication date
2022-08-25Copyright date
2022ISBN
9781665482431eISSN
2577-2465Publisher version
Language
- en