Logistic regression for BLER prediction in 5G

Abstract

In this work, a block error rate (BLER) predictor for 5G based on logistic regression is presented. The regression is fed with transmission parameters and channel statistics. With these features, the predictor can model the behaviour of the transmission chain, including the low parity channel code (LDPC). In particular, for each modulation and coding scheme (MCS), the regression model uses as features the mean of the SINR over the allocated resources and the squared distance to the mean. Moreover, a single model able to cope with a set of modulation and coding schemes (MCSs) at the expense of certain accuracy loss is also proposed, and its performance evaluated. Possible applications for the regression models such as end-to-end modelling or as part of the adaptive modulation and coding (AMC) function are explored. Results show that the model has excellent accuracy in a wide set of scenarios.