Deep Learning-based Mitigation of Nonlinear Hardware Impairments for THz Communication

Terahertz (THz) wireless communication is a promising technique to meet the rising demand for high-bandwidth services. Propagation of the $\mathbf{T H z}$ waves through the atmosphere is influenced by factors like attenuation, water vapour, weather, turbulence, rain and beam misalignment. Furthermore, performance of a THz link is severely degraded by non-linear hardware impairments introduced by power amplifier (PA). Conventional estimators such as zero-forcing (ZF) and minimum mean squared error (MMSE) fall short of handling beam misalignment, rain attenuation, and non-linearity. To circumvent this limitation, we propose a deep neural network (DNN) based receiver for THz communication systems in the presence of PA non-linearity. Simulation results for bit error rate (BER) performance considering non-linear distortion due to power amplifier indicate that the proposed deep learning (DL) based receiver performs better in terms of BER and is more robust compared to ZF, MMSE and orthogonal matching pursuit (OMP) based receiver.