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

How Much Data is Needed for Channel Knowledge Map Construction?