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.


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    Title :

    Bayesian optimization of Blocklength for URLLC Under Channel Distribution Uncertainty




    Publication date :

    2022-06-01


    Size :

    1631125 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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