  Holographic Multiple-Input Multiple-Output (HMIMO), which densely integrates
numerous antennas into a limited space, is anticipated to provide higher rates
for future 6G wireless communications. The increase in antenna aperture size
makes the near-field region enlarge, causing some users to be located in the
near-field region. Thus, we are facing a hybrid near-field and far-field
communication problem, where conventional far-field modeling methods may not
work well. In this paper, we propose a near-far field channel model that does
not presuppose whether each path is near-field or far-field, different from the
existing work requiring the ratio of the number of near-field paths to that of
far-field paths as prior knowledge. However, this gives rise to a new challenge
for accurately modeling the channel, as conventional methods of obtaining
channel model parameters are not applicable to this model. Therefore, we
propose a new method, Expectation-Maximization (EM)-based Near-Far Field
Channel Modeling, to obtain channel model parameters, which considers whether
each path is near-field or far-field as a hidden variable, and optimizes the
hidden variables and channel model parameters through an alternating iteration
method. Simulation results show that our method is superior to conventional
near-field and far-field algorithms in fitting the near-far field channel in
terms of outage probability.
