  Channel knowledge map (CKM) has been recently proposed to enable
environment-aware communications by utilizing historical or simulation
generated wireless channel data. This paper studies the construction of one
particular type of CKM, namely channel gain map (CGM), by using a finite number
of measurements or simulation-generated data, with model-based spatial channel
prediction. We try to answer the following question: How much data is
sufficient for CKM construction? To this end, we first derive the average mean
square error (AMSE) of the channel gain prediction as a function of the sample
density of data collection for offline CGM construction, as well as the number
of data points used for online spatial channel gain prediction. To model the
spatial variation of the wireless environment even within each cell, we divide
the CGM into subregions and estimate the channel parameters from the local data
within each subregion. The parameter estimation error and the channel
prediction error based on estimated channel parameters are derived as functions
of the number of data points within the subregion. The analytical results
provide useful guide for CGM construction and utilization by determining the
required spatial sample density for offline data collection and number of data
points to be used for online channel prediction, so that the desired level of
channel prediction accuracy is guaranteed.
