Terahertz (THz) communication is envisioned as a key technology for 6G and beyond wireless systems owing to its multi-GHz bandwidth. To maintain the same aperture area and the same link budget as the lower frequencies, ultra-massive multi-input and multi-output (UM-MIMO) with hybrid beamforming is promising. Nevertheless, the hardware imperfections particularly at THz frequencies, can degrade spectral efficiency and lead to a high symbol error rate (SER), which is often overlooked yet imperative to address in practical THz communication systems. In this paper, the hybrid beamforming is investigated for THz UM-MIMO systems accounting for comprehensive hardware imperfections, including DAC and ADC quantization errors, in-phase and quadrature imbalance (IQ imbalance), phase noise, amplitude and phase error of imperfect phase shifters and power amplifier (PA) nonlinearity. Then, a two-stage hardware imperfection compensation algorithm is proposed. A deep neural network (DNN) is developed in the first stage to represent the combined hardware imperfections, while in the second stage, the digital precoder in the transmitter (Tx) or the combiner in the receiver (Rx) is designed using NN to effectively compensate for these imperfections. Furthermore, to balance the performance and network complexity, three slimming methods including pruning, parameter sharing, and removing parts of the network are proposed and combined to slim the DNN in the first stage. Numerical results show that the Tx compensation can perform better than the Rx compensation. Additionally, using the combined slimming methods can reduce parameters by 97.2% and running time by 39.2% while maintaining nearly the same performance in both uncoded and coded systems.

翻译：太赫兹通信因其多GHz带宽特性被视为6G及未来无线系统的关键技术。为在保持相同孔径面积和链路预算的条件下实现与低频段相当的性能，采用混合波束成形的超大规模多输入多输出系统展现出巨大潜力。然而，硬件不完美性（尤其在太赫兹频段）会降低频谱效率并导致高符号错误率，这一在实际太赫兹通信系统中至关重要却常被忽视的问题亟待解决。本文针对存在综合硬件不完美性的太赫兹超大规模多输入多输出系统展开混合波束成形研究，涵盖数模/模数转换器量化误差、同相正交不平衡、相位噪声、非理想移相器的幅相误差以及功率放大器非线性等效应。在此基础上，提出一种两阶段硬件不完美补偿算法：第一阶段构建深度神经网络以表征综合硬件不完美性；第二阶段通过神经网络设计发射端的数字预编码器或接收端的合并器，以有效补偿这些不完美性。此外，为权衡性能与网络复杂度，提出剪枝、参数共享及网络局部移除三种精简方法，并组合应用于第一阶段的深度神经网络精简。数值结果表明：发射端补偿性能优于接收端补偿；在编码与非编码系统中，采用组合精简方法可在保持近乎相同性能的同时，减少97.2%的参数并降低39.2%的运行时间。