Open AccessComputationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO ‐ OFDM systems
Author(s) -
Singh Davinder,
Sarin Rakesh Kumar
Publication year - 2019
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2018-0190
Subject(s) - orthogonal frequency division multiplexing , reduction (mathematics) , mimo ofdm , mimo , algorithm , hyperparameter , expectation–maximization algorithm , bayesian inference , multiplexing , bayesian probability , upper and lower bounds , gaussian , mathematics , inverse , computer science , mathematical optimization , beamforming , telecommunications , maximum likelihood , statistics , geometry , physics , quantum mechanics , estimator , mathematical analysis
This paper investigates the use of the inverse‐free sparse Bayesian learning ( SBL ) approach for peak‐to‐average power ratio ( PAPR ) reduction in orthogonal frequency‐division multiplexing ( OFDM )‐based multiuser massive multiple‐input multiple‐output ( MIMO ) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought‐after low‐ PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation‐maximization ( EM ) iterative algorithm. The matrix inversion involved in the expectation step (E‐step) is averted by invoking a relaxed evidence lower bound (relaxed‐ ELBO ). The resulting inverse‐free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.