  Channel knowledge map (CKM) is an emerging technique to enable
environment-aware wireless communications, in which databases with
location-specific channel knowledge are used to facilitate or even obviate
real-time channel state information acquisition. One fundamental problem for
CKM-enabled communication is how to efficiently construct the CKM based on
finite measurement data points at limited user locations. Towards this end,
this paper proposes a novel map construction method based on the
\emph{expectation maximization} (EM) algorithm, by utilizing the available
measurement data, jointly with the expert knowledge of well-established
statistic channel models. The key idea is to partition the available data
points into different groups, where each group shares the same modelling
parameter values to be determined. We show that determining the modelling
parameter values can be formulated as a maximum likelihood estimation problem
with latent variables, which is then efficiently solved by the classic EM
algorithm. Compared to the pure data-driven methods such as the nearest
neighbor based interpolation, the proposed method is more efficient since only
a small number of modelling parameters need to be determined and stored.
Furthermore, the proposed method is extended for constructing a specific type
of CKM, namely, the channel gain map (CGM), where closed-form expressions are
derived for the E-step and M-step of the EM algorithm. Numerical results are
provided to show the effectiveness of the proposed map construction method as
compared to the benchmark curve fitting method with one single model.
