Compressive Sensing Recovery of Nonlinearly Distorted OFDM Signals

High peak to average power ratio is a major drawback in orthogonal frequency division multiplexing (OFDM) systems. A high PAPR can lead to saturation in the power amplifier and consequently increase spectral spreading, distorts the signal, and reduces power amplifier efficiency. In this paper, we propose a method based on compressed sensing (CS) to recover nonlinearly distorted OFDM signals. The method exploits pilot tones inserted in the OFDM signal for channel estimation. The key steps are a CS-based estimation of clipping noise and its removal from the received OFDM signal. In our work, we also include the effect of nonlinear distortion on channel estimation. Numerical results show that the proposed CS-based method significantly improves the bit error rate (BER) performance over previously proposed techniques which iteratively estimate the clipping noise and cancel it from the received signal.