Titre :

Multiple Importance Sampling for Efficient Symbol Error Rate Estimation

Auteur(s) :

Elvira, Víctor [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
Santamaria, Ignacio [Auteur]

Titre de la revue :

IEEE Signal Processing Letters

Pagination :

420-424

Éditeur :

Institute of Electrical and Electronics Engineers

Date de publication :

2019

ISSN :

1070-9908

Discipline(s) HAL :

Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

Digital constellations formed by hexagonal or other non-square two-dimensional lattices are often used in advanced digital communication systems. The integrals required to evaluate the symbol error rate (SER) of these ...
Lire la suite >Digital constellations formed by hexagonal or other non-square two-dimensional lattices are often used in advanced digital communication systems. The integrals required to evaluate the symbol error rate (SER) of these constellations in the presence of Gaussian noise are in general difficult to compute in closed form, and therefore Monte Carlo simulation is typically used to estimate the SER. However, naive Monte Carlo simulation can be very inefficient and requires very long simulation runs, especially at high signal-to-noise ratios. In this letter, we adapt a recently proposed multiple importance sampling technique, called ALOE (for "at least one rare event"), to this problem. Conditioned to a transmitted symbol, an error (or rare event) occurs when the observation falls in a union of half-spaces or, equivalently, outside a given polytope. The proposal distribution for ALOE samples the system conditionally on an error taking place, which makes it more efficient than other importance sampling techniques. ALOE provides unbiased SER estimates with simulation times orders of magnitude shorter than conventional Monte Carlo.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Projet ANR :

Méthodes d'échantillonnage d'importance adaptatives pour l'inférence Bayésienne dans les systèmes complexes

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL