Sparsity-Learning-Based Iterative Compensation for Filtered-OFDM With Clipping

This letter is concerned with the high peak-to-average power ratio (PAPR) problem in filtered orthogonal frequency-division multiplexing (f-OFDM) systems. Iterative clipping and filtering (ICF) is adopted as a simple and effective method to suppress PAPR but introduces signal distortion. At the receiver, we propose a sparsity-learning based iterative algorithm for distortion compensation by exploiting the sparsity of the distortion. Specifically, our iterative algorithm consists of a linear estimator, a soft demodulator, and a distortion estimator. The iteration between the three modules is based on turbo message passing. We show that our scheme outperforms the conventional iterative methods in both complexity and performance. Numerical results demonstrate that our algorithm achieves more effective PAPR suppression and better error rate performance.