Receiver-based Recovery of Clipped OFDM Signals for PAPR Reduction: A Bayesian Approach

TL;DR

This paper introduces a Bayesian method for recovering clipped OFDM signals to reduce PAPR, leveraging sparsity and robustness, applicable to multi-antenna and multi-user systems, with improved performance over existing methods.

Contribution

A novel low-complexity Bayesian clipping recovery scheme that exploits sparsity, robustness, and phase properties, extending to multi-antenna and multi-user OFDM systems, and addressing contaminated channel estimation.

Findings

Favorable recovery performance compared to existing sparse schemes.

Robustness against inaccurate clipping statistics.

Effective in multi-antenna and multi-user OFDM scenarios.

Abstract

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.