  Clipping is one of the simplest peak-to-average power ratio (PAPR) reduction
schemes for orthogonal frequency division multiplexing (OFDM). Deliberately
clipping the transmission signal degrades system performance, and clipping
mitigation is required at the receiver for information restoration. In this
work, we acknowledge the sparse nature of the clipping signal and propose a
low-complexity Bayesian clipping estimation scheme. The proposed scheme
utilizes a priori information about the sparsity rate and noise variance for
enhanced recovery. At the same time, the proposed scheme is robust against
inaccurate estimates of the clipping signal statistics. The undistorted phase
property of the clipped signal, as well as the clipping likelihood, is utilized
for enhanced reconstruction. Further, motivated by the nature of modern
OFDM-based communication systems, we extend our clipping reconstruction
approach to multiple antenna receivers, and multi-user OFDM. We also address
the problem of channel estimation from pilots contaminated by the clipping
distortion. Numerical findings are presented, that depict favourable results
for the proposed scheme compared to the established sparse reconstruction
schemes.
