Multiple importance sampling for symbol error rate estimation of maximum-likelihood detectors in MIMO channels

Abstract: In this paper we propose a multiple importance sampling (MIS) method for the efficient symbol error rate (SER) estimation of maximum likelihood (ML) multiple-input multiple-output (MIMO) detectors. Given a transmitted symbol from the input lattice, obtaining the SER requires the computation of an integral outside its Voronoi region in a high-dimensional space, for which a closed-form solution does not exist. Hence, the SER must be approximated through crude or naive Monte Carlo (MC) simulations. This practice is widely used in the literature despite its inefficiency, particularly severe at high signal-to-noise-ratio (SNR) or for systems with stringent SER requirements. It is well-known that more sophisticated MC-based techniques such as MIS, when carefully designed, can reduce the variance of the estimators in several orders of magnitude with respect to naive Monte Carlo in rare-event estimation, or equivalently, they need significantly less samples for attaining a desired performance. The proposed MIS method provides unbiased SER estimates by sampling from a mixture of components that are carefully chosen and parametrized. The number of components, the parameters of the components, and their weights in the mixture, are automatically chosen by the proposed method. As a result, the proposed method is flexible, easy-to-use, theoretically sound, and presents a high performance in a variety of scenarios. We show in our simulations that SERs lower than 10?8 can be accurately estimated with just 104 random samples.

 Autoría: Elvira V., Santamaria I.,

 Fuente: IEEE Transactions on Signal Processing, 2021, 69, 1200-1212

Editorial: Institute of Electrical and Electronics Engineers, Inc.

 Fecha de publicación: 01/02/2021

Nº de páginas: 13

Tipo de publicación: Artículo de Revista

DOI: 10.1109/TSP.2021.3055961

ISSN: 1053-587X,1941-0476

Proyecto español: PID2019- 104958RB-C43

Url de la publicación: https://doi.org/10.1109/TSP.2021.3055961