Neural Network Aided Equalization for AFDM Systems with Power Amplifier Nonlinearity

Affine frequency division multiplexing (AFDM) is a promising waveform for future high mobility wireless communication, modulating as chirps-based multicarrier to resist time-varying channels. This paper firstly analyzes the peak-to-average power ratio (PAPR) performance of AFDM signal and the nonlinear effect caused by power amplifier (PA). The reason for nonlinearity occurred is that AFDM signal generates a high PAPR level resulting in PA working at saturation point. The nonlinear distortion deteriorates target performance in a large degree. To this end, we introduce an equalization approach consisting of maximal-ratio combining (MRC) and super-resolution network to tackle the distortion challenges. Specifically, the MRC algorithm is used to exploit the sparse representation of communication channel and the proposed retrieval network (RE-Net) based super-resolution model alleviates the nonlinearity induced by PA. Simulation results reveal the designed equalization scheme executes a pronounced performance promotion compared with conventional solutions.