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

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.