  This paper proposes a joint channel and data estimation (JCDE) algorithm for
uplink multiuser extremely large-scale multiple-input-multiple-output (XL-MIMO)
systems. The initial channel estimation is formulated as a sparse
reconstruction problem based on the angle and distance sparsity under the
near-field propagation condition. This problem is solved using non-orthogonal
pilots through an efficient low complexity two-stage compressed sensing
algorithm. Furthermore, the initial channel estimates are refined by employing
a JCDE framework driven by both non-orthogonal pilots and estimated data. The
JCDE problem is solved by sequential expectation propagation (EP) algorithms,
where the channel and data are alternately updated in an iterative manner. In
the channel estimation phase, integrating Bayesian inference with a model-based
deterministic approach provides precise estimations to effectively exploit the
near-field characteristics in the beam-domain. In the data estimation phase, a
linear minimum mean square error (LMMSE)-based filter is designed at each
sub-array to address the correlation due to energy leakage in the beam-domain
arising from the near-field effects. Numerical simulations reveal that the
proposed initial channel estimation and JCDE algorithm outperforms the
state-ofthe-art approaches in terms of channel estimation, data detection, and
computational complexity.
