A Model-Driven Approach to Enhance Faster-than-Nyquist Signaling over Nonlinear Channels

In order to increase the capacity of future satellite communication systems, faster-than-Nyquist (FTN) signaling is increasingly considered. Existing methods for compensating for the high power amplifier (HPA) nonlinearity require perfect knowledge of the HPA model. To address this issue, we analyze the FTN symbol distribution and propose a complex-valued deep neural network (CVDNN) aided compensation scheme for the HPA nonlinearity, which does not require perfect knowledge of the HPA model and can learn the HPA nonlinearity during the training process. A model-driven network for nonlinearity compensation is proposed to further enhance the performance. Furthermore, two training sets based on the FTN symbol distribution are designed for training the network. Extensive simulations show that the Gaussian distribution is a good approximation of the FTN symbol distribution. The proposed model-driven network trained by employing a Gaussian distribution to approximate an FTN signaling can achieve a performance gain of 0.5 dB compared with existing methods without HPA's parameters at the receiver. The proposed neural network is also applicable for non-linear compensation in other systems, including orthogonal frequency-division multiplexing (OFDM).