Adaptive importance sampling and quasi-Monte Carlo methods for 6G URLLC systems

Xiongwen Ke, Houying Zhu, Kai Yi, Gaoning He, Ganghua Yang, Yu Guang Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


In this paper, we propose an efficient simulation method based on adaptive importance sampling, which can automatically find the optimal proposal within the Gaussian family based on previous samples, to evaluate the probability of bit error rate (BER) or word error rate (WER). These two measures, which involve high-dimensional black-box integration and rare-event sampling, can characterize the performance of coded modulation. We further integrate the quasi-Monte Carlo method within our framework to improve the convergence speed. The proposed importance sampling algorithm is demonstrated to have much higher efficiency than the standard Monte Carlo method in the AWGN scenario.

Original languageEnglish
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
Place of PublicationRome, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)9781538674628
Publication statusPublished - 2023
Event2023 IEEE International Conference on Communications, ICC 2023 - Rome, Italy
Duration: 28 May 20231 Jun 2023

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607
ISSN (Electronic)1938-1883


Conference2023 IEEE International Conference on Communications, ICC 2023


  • adaptive importance sampling
  • bit error rate
  • error probability
  • Monte Carlo
  • quasi-Monte Carlo


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