A Low-Complexity Joint PAPR Reduction and Predistortion Based on Generalized Memory Polynomial Model

Digital predistortion (DPD) technique is an effective means to alleviate the nonlinearity of power amplifiers (PAs) and improve transmitter efficiency. However, for orthogonal frequency division multiplexing (OFDM) systems with a high peak-to-average power ratio (PAPR), the PAPR reduction needs to be deployed in combination with DPD, to maximize transmitter efficiency. The extra complexity caused by the introduction of PAPR reduction on the main road has become a non-negligible defect. To address this issue, this letter proposes a joint PAPR reduction and DPD model based on the generalized memory polynomial (GMP). To train the joint GMP model, this letter improves the ping-pong joint optimization (P2JO) approach under the offline case, to obtain accurate training data. The comparative experimental results based on 100-MHz Doherty PA show that the proposed joint model significantly reduces the model complexity on the main road, while ensuring adjacent channel power ratio (ACPR) and bit error rate (BER).