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Bayesian optimization of blocklength for URLLC under channel distribution uncertainty

conference contribution
posted on 12.05.2022, 11:07 by Wenheng ZhangWenheng Zhang, Mahsa DerakhshaniMahsa Derakhshani, Saeed R Khosravirad, Sangarapillai LambotharanSangarapillai Lambotharan

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.


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

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  • Mechanical, Electrical and Manufacturing Engineering

Published in

2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)


2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)




AM (Accepted Manuscript)

Publisher statement

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Acceptance date





Helsinki, Finland

Event dates

19th June 2022 - 22nd June 2022


Dr Mahsa Derakhshani. Deposit date: 11 May 2022