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.

翻译：信道知识图谱（CKM）通过利用历史或仿真生成的无线信道数据实现环境感知通信，近期被提出。本文研究利用有限数量的实测或仿真生成数据，结合基于模型的空间信道预测，构建特定类型的信道知识图谱——即信道增益图（CGM）。我们试图回答以下问题：信道知识图谱构建需要多少数据才足够？为此，我们首先推导了离线CGM构建中数据采集样本密度与在线空间信道增益预测所用数据点数量对应的信道增益预测平均均方误差（AMSE）函数。为建模无线环境的空间变化（即使在同一小区内），我们将CGM划分为子区域，并利用各子区域内的局部数据估计信道参数。推导了基于估计信道参数的参数估计误差和信道预测误差作为子区域内数据点数量的函数。分析结果通过确定离线数据采集所需的空间样本密度和在线信道预测所需的数据点数量，为CGM的构建与利用提供了有效指导，从而保证所需的信道预测精度。