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Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO-OFDM systems

  • Singh, Davinder (Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology) ;
  • Sarin, Rakesh Kumar (Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology)
  • Received : 2018.04.12
  • Accepted : 2018.09.05
  • Published : 2019.06.03

Abstract

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.

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