A combined approach of MBAP/PR PAPR reduction and polynomial predistortion for performance enhancement

Orthogonal frequency division multiplexing (OFDM) has become a key modulation format in wireless communications. Its inherent high peak to average power ratio (PAPR) severely limits the power efficiency of the operating power amplifier to alleviate nonlinear distortion effects. PAPR reduction and PA linearization have both been extensively studied to deal with this important issue. In this paper, we propose a new approach which combines the metric based amplitude predistortion (MBAP)/phase reversal (PR) PAPR reduction scheme and memory polynomial predistorter (MPPD) with indirect learning (IDL) architecture. A comparative study of performance of different combinations using a Wiener power amplifier model is presented in terms of both total degradation (TD) and power spectral density (PSD). Simulation results indicate that our proposed combination approach can effectively improve the performance. In particular, MPPD with PR is the best one.