  The large bandwidth combined with ultra-massive multiple-input
multiple-output (UM-MIMO) arrays enables terahertz (THz) systems to achieve
terabits-per-second throughput. The THz systems are expected to operate in the
near, intermediate, as well as the far-field. As such, channel estimation
strategies suitable for the near, intermediate, or far-field have been
introduced in the literature. In this work, we propose a cross-field, i.e.,
able to operate in near, intermediate, and far-field, compressive channel
estimation strategy. For an array-of-subarrays (AoSA) architecture, the
proposed method compares the received signals across the arrays to determine
whether a near, intermediate, or far-field channel estimation approach will be
appropriate. Subsequently, compressed estimation is performed in which the
proximity of multiple subarrays (SAs) at the transmitter and receiver is
exploited to reduce computational complexity and increase estimation accuracy.
Numerical results show that the proposed method can enhance channel estimation
accuracy and complexity at all distances of interest.
