  Extremely large-scale massive multiple-input multiple-output (XL-MIMO)
systems introduce the much higher channel dimensionality and incur the
additional near-field propagation effect, aggravating the computation load and
the difficulty to acquire the prior knowledge for channel estimation. In this
article, an XL-MIMO channel network (XLCNet) is developed to estimate the
high-dimensional channel, which is a universal solution for both the near-field
users and far-field users with different channel statistics. Furthermore, a
compressed XLCNet (C-XLCNet) is designed via weight pruning and quantization to
accelerate the model inference as well as to facilitate the model storage and
transmission. Simulation results show the performance superiority and
universality of XLCNet. Compared to XLCNet, C-XLCNet incurs the limited
performance loss while reducing the computational complexity and model size by
about $10 \times$ and $36 \times$, respectively.
