How Much Data is Needed for Channel Knowledge Map Construction?

TL;DR

This paper analyzes the amount of data needed to accurately construct channel gain maps for environment-aware wireless communication, providing analytical models to guide data collection and prediction accuracy.

Contribution

It derives analytical expressions for channel prediction error as a function of data sample density, guiding efficient data collection for channel knowledge map construction.

Findings

Derived AMSE of channel gain prediction based on data density

Estimated channel parameters and prediction errors within subregions

Provided guidelines for data requirements to achieve desired accuracy

Abstract

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.