Deep Compressed Sensing for Terahertz Ultra-Massive MIMO Channel Estimation

Abstract

Envisioned as a pivotal technology for sixth-generation (6G) and beyond, Terahertz (THz) band communications can potentially satisfy the increasing demand for ultra-high-speed wireless links. While ultra-massive multiple-input multiple-output (UM-MIMO) is promising in counteracting the exceptionally high path loss at THz frequency, the channel estimation (CE) of this extensive antenna system introduces significant challenges. In this paper, we propose a deep compressed sensing (DCS) framework based on generative neural networks for THz CE. The proposed estimator generates realistic THz channel samples to avoid complex channel modeling for THz UM-MIMO systems, especially in the near field. More importantly, the estimator is optimized for fast channel inference. Our results show significant superiority over the baseline generative adversarial network (GAN) estimator and traditional estimators. Compared to conventional estimators, our model achieves at least 8 dB lower normalized mean squared error (NMSE). Against GAN estimator, our model achieves around 3 dB lower NMSE at 0 dB SNR with one order of magnitude lower computation complexity. Moreover, our model achieves lower training overhead compared to GAN with empirically 4 times faster training convergence.