Modified Compressive Sensing Reconstruction Algorithm for Clipping Noise Estimation in OFDM Systems

A simple technique to reduce the peak-to-average power ratio (PAPR) is clipping the signal before the power amplifier. Th e noise resulted from the clipping process increases bit error rate (BER) which degrades the system performance. In this paper we have used d[n] as the distance between the clipped signal and the clipping level to modify compressive sensing (CS) algorithm to estimate the clipping noise. Th is modification is made in the sensing matrix by removing the redundant columns which is done by comparing d[n] with a parameter called opt. Th e performance of the proposed scheme depends on this parameter which is calculated by numerical experiments on different signal-to-noise ratio (SNR) values. Simulation results demonstrated that the proposed scheme leads to improved estimation for clipping noise and also complexity reduction for the reconstruction algorithm.