A Model-Driven Deep Learning Network for Quantized GFDM Receiver

Low-resolution analog-to-digital converter (ADC) is a promising solution to reduce hardware cost and power consumption in generalized frequency division multiplexing (GFDM) systems. The severe nonlinear distortion of ADCs and the non-orthogonality of GFDM make receiver design a great challenge. In this paper, we propose a novel model-driven receiver architecture for GFDM with low-resolution ADCs. Orthogonal approximate message passing (OAMP) framework is combined with the classical linear estimator in this work to create a robust iterative receiver for GFDM systems with low-precision ADCs. The corresponding model-driven network is organized based on the proposed novel iterative algorithm according to the procedures of the receiver. The network of OAMP can reduce the gap between the approximate algorithm and the Bayesian optimal result due to the information loss of ADCs. The signal flow of the neural network is designed by unfolding the iterative algorithms for channel estimation and data detection. Numerical results are provided to show that the proposed OAMP-based receiver algorithm outperforms traditional receivers and the model-driven network can further improve the system performance on the basis of the corresponding novel algorithm.