  Due to spectrum scarcity and increasing wireless capacity demands, terahertz
(THz) communications at 0.1-10THz and the corresponding spectrum
characterization have emerged to meet diverse service requirements in future 5G
and 6G wireless systems. However, conventional compressed sensing techniques to
reconstruct the original wideband spectrum with under-sampled measurements
become inefficient as local spectral correlation is deliberately omitted.
Recent works extend communication methods with deep learning-based algorithms
but lack strong ties to THz channel properties. This paper introduces novel THz
channel-aware spectrum learning solutions that fully disclose the uniqueness of
THz channels when performing such ultra-broadband sensing in vehicular
environments. Specifically, a joint design of spectrum compression and
reconstruction is proposed through a structured sensing matrix and two-phase
reconstruction based on high spreading loss and molecular absorption at THz
frequencies. An end-to-end learning framework, namely compression and
reconstruction network (CRNet), is further developed with the mean-square-error
loss function to improve sensing accuracy while significantly reducing
computational complexity. Numerical results show that the CRNet solutions
outperform the latest generative adversarial network (GAN) realization with a
much higher cosine and structure similarity measures, smaller learning errors,
and 56% less required training overheads. This THz Ultra-broadband Learning
Vehicular Channel-Aware Networking (TULVCAN) work successfully achieves
effective THz spectrum learning and hence allows frequency-agile access.
