A mask-based peak-to-average power ratio reduction scheme for affine frequency division multiplexing systems using Volterra Series-Driven PolyNet - Xiangxin Liu; Yushuai Zhang; Jianxin Guo; Rui Zhu; Feng Wang; Liping Wang (PLoS ONE, 2026)
Affine frequency division multiplexing (AFDM) demonstrates exceptional resilience to Doppler effects in doubly dispersive channels, making it a promising waveform for 6G high-mobility communications, but its high peak-to-average power ratio (PAPR) problem severely limits system energy efficiency. When migrated to AFDM, existing mask-based deep learning PAPR reduction schemes face the challenges of contextual loss caused by local sampling and the lack of physical interpretability in black-box models. Therefore, this paper proposes a physics-aware mask-based PAPR reduction scheme tailored for AFDM. First, a full-frame input strategy is introduced to exploit the global time-domain correlation of AFDM signals for precisely recovering the impaired symbols. Second, to address the nonlinear distortion induced by masking, a PolyNet-Volterra network is proposed by integrating Volterra series theory. Departing from the traditional design of blindly stacking layers, this model explicitly constructs first-order linear and third-order power feature layers, which substantially enhances the nonlinear reconstruction accuracy while avoiding overfitting. Simulation results demonstrate that, under a threshold of 0.9, the proposed scheme achieves a significant PAPR reduction of approximately 5.9 dB. Furthermore, requiring only about 165,000 parameters, the PolyNet-Volterra model comprehensively outperforms baseline models such as DNN and ResNet in terms of both bit error rate (BER) and mean squared error (MSE). Compared with the conventional PolyNet, the proposed model reduces the parameter and computational overhead, suggesting its potential as a low-complexity receiver-side reconstruction module for resource-constrained future wireless systems. Further hardware-oriented and over-the-air validation is still needed before drawing deployment-level conclusions.