Learning based Transmitter/Receiver Design for the Nonlinear Channel

We present our study results on the neural network based approaches to the transmitter/receiver design over nonlinear channel. In the first part, we discuss the transmitter design for the peak-to-average power (PAPR) reduction. The algorithm unrolling is applied to a well-known PAPR reduction algorithm. The learning-based approach achieves the performance on par with the classical approach, but at the smaller computational complexity. The second part discusses the joint transmitter/receiver design for the mitigation of the transmitter nonlinearity. It is shown that by combining the neural network with the unfolded algorithm for PAPR reduction at the transmitter and employing the receiver neural network, sizable gain is achieved when the emission criterion is relaxed.