  Near-field communication comes to be an indispensable part of the future
sixth generation (6G) communications at the arrival of the forth-coming
deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO)
systems. Due to the substantial number of antennas, the electromagnetic
radiation field is modeled by the spherical waves instead of the conventional
planar waves, leading to severe weak sparsity to angular-domain near-field
channel. Therefore, the channel estimation reminiscent of the conventional
compression sensing (CS) approaches in the angular domain, judiciously utilized
for low pilot overhead, may result in unprecedented challenges. To this end,
this paper proposes a brand-new near-field channel estimation scheme by
exploiting the naturally occurring useful side information. Specifically, we
formulate the dual-band near-field communication model based on the fact that
high-frequency systems are likely to be deployed with lower-frequency systems.
Representative side information, i.e., the structural characteristic
information derived by the sparsity ambiguity and the out-of-band spatial
information stemming from the lower-frequency channel, is explored and tailored
to materialize exceptional near-field channel estimation. Furthermore, in-depth
theoretical analyses are developed to guarantee the minimum estimation error,
based on which a suite of algorithms leveraging the elaborating side
information are proposed. Numerical simulations demonstrate that the designed
algorithms provide more assured results than the off-the-shelf approaches in
the context of the dual-band near-field communications in both on- and off-grid
scenarios, where the angle of departures/arrivals are discretely or
continuously distributed, respectively.
