Deep Learning-Assisted OFDM Detection with Hardware Impairments

Abstract

This paper introduces a deep learning (DL) algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing (OFDM) communication systems affected by hardware impairments (HIs). In practice, hardware imperfections are present at the transceivers, which are modeled as direct current (DC) offset, carrier frequency offset (CFO), and in-phase and quadrature-phase (IQ) imbalance at the transmitter and the receiver in OFDM system. In HIs, the explicit system model could not be mathematically derived, which limits the performance of conventional least square (LS) or minimum mean square error (MMSE) estimators. Thus, we consider time-frequency response of a channel as a 2D image, and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution, and image restoration techniques. Further, a deep neural network (DNN) is designed to fit the mapping between the received signal and transmit symbols, where the number of outputs equals to the size of the modulation order. Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm. The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe HIs.