  Future sixth-generation (6G) systems are expected to leverage extremely
large-scale multiple-input multiple-output (XL-MIMO) technology, which
significantly expands the range of the near-field region. The spherical
wavefront characteristics in the near field introduce additional degrees of
freedom (DoFs), namely distance and angle, into the channel model, which leads
to unique challenges in channel estimation (CE). In this paper, we propose a
new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably
reduces the required quantity of baseband samples and the dictionary size. In
particular, we first propose a sensing method that can be accomplished in a
single time slot. It employs power sensors embedded within the antenna elements
to measure the received power pattern rather than baseband samples. A time
inversion algorithm is then proposed to precisely estimate the locations of
users and scatterers, which offers a substantially lower computational
complexity. Based on the estimated locations from sensing, a novel dictionary
is then proposed by considering the eigen-problem based on the near-field
transmission model, which facilitates efficient near-field CE with less
baseband sampling and a more lightweight dictionary. Moreover, we derive the
general form of the eigenvectors associated with the near-field channel matrix,
revealing their noteworthy connection to the discrete prolate spheroidal
sequence (DPSS). Simulation results unveil that the proposed time inversion
algorithm achieves accurate localization with power measurements only, and
remarkably outperforms various widely-adopted algorithms in terms of
computational complexity. Furthermore, the proposed eigen-dictionary
considerably improves the accuracy in CE with a compact dictionary size and a
drastic reduction in baseband samples by up to 77%.
