An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

摘要

We consider the problem of peak-to-average power ratio (PAPR) reduction inorthogonal frequency-division multiplexing (OFDM) based massive multiple-inputmultiple-output (MIMO) downlink systems. Specifically, given a set of symbolvectors to be transmitted to K users, the problem is to find an OFDM-modulatedsignal that has a low PAPR and meanwhile enables multiuser interference (MUI)cancellation. Unlike previous works that tackled the problem using convexoptimization, we take a Bayesian approach and develop an efficient PAPRreduction method by exploiting the redundant degrees-of-freedom of the transmitarray. The sought-after signal is treated as a random vector with ahierarchical truncated Gaussian mixture prior, which has the potential toencourage a low PAPR signal with most of its samples concentrated on theboundaries. A variational expectation-maximization (EM) strategy is developedto obtain estimates of the hyperparameters associated with the prior model,along with the signal. In addition, the generalized approximate message passing(GAMP) is embedded into the variational EM framework, which results in asignificant reduction in computational complexity of the proposed algorithm.Simulation results show our proposed algorithm achieves a substantialperformance improvement over existing methods in terms of both the PAPRreduction and computational complexity.