RT Conference Proceedings
T1 Logistic regression for BLER prediction in 5G
A1 Ruiz Sicilia, Juan Carlos
A1 Aguayo-Torres, María del Carmen
K1 Análisis de regresión
K1 Errores de diagnóstico
K1 Tecnología de la información
K1 Proceso electrónico de datos
K1 Sistemas de comunicaciones inalámbricos
AB 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.
YR 2020
FD 2020-10-09
LK https://hdl.handle.net/10630/19923
UL https://hdl.handle.net/10630/19923
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026