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dc.contributor.authorRuiz Sicilia, Juan Carlos
dc.contributor.authorAguayo-Torres, Maria del Carmen 
dc.date.accessioned2020-10-09T07:56:06Z
dc.date.available2020-10-09T07:56:06Z
dc.date.created2020-10
dc.date.issued2020-10-09
dc.identifier.urihttps://hdl.handle.net/10630/19923
dc.description.abstractIn 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.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnálisis de regresiónen_US
dc.subjectErrores de diagnósticoen_US
dc.subjectTecnología de la informaciónen_US
dc.subjectProceso electrónico de datosen_US
dc.subjectSistemas de comunicaciones inalámbricosen_US
dc.subject.other5Gen_US
dc.subject.otherModulation and coding schemeen_US
dc.subject.otherLogistic regressionen_US
dc.titleLogistic regression for BLER prediction in 5Gen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Telecomunicaciónen_US
dc.relation.eventtitleXXXV Simposio Nacional de la Unión Científica Internacional de Radio (URSI 2020)en_US
dc.relation.eventplaceOnlineen_US
dc.relation.eventdate2 de septiembre de 2020en_US


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