| 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. |
