Channel Knowledge Map for Cellular-Connected UAV via Binary Bayesian Filtering

TL;DR

This paper presents a binary Bayesian filtering approach to construct a line-of-sight link probability map for cellular-connected UAVs, combining empirical models and measurement data to improve environment-aware wireless communication.

Contribution

It introduces a novel binary Bayesian filter for LSM construction that effectively integrates empirical models and measurement data, especially under sparse measurement conditions.

Findings

The proposed algorithm outperforms benchmark schemes.

Spatial correlation models improve LSM updates.

Effective in sparse measurement scenarios.

Abstract

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing. Link state map (LSM) is one particular type of CKM that aims to learn the location-specific line-of-sight (LoS) link probability between the transmitter and the receiver at all possible locations, which provides the prior information to enhance the communication quality of dynamic networks. This paper investigates the LSM construction for cellularconnected unmanned aerial vehicles (UAVs) by utilizing both the expert empirical mathematical model and the measurement data. Specifically, we first model the LSM as a binary spatial random field and its initial distribution is obtained by the empirical model. Then we propose an effective binary Bayesian filter to sequentially update the LSM by using the channel measurement. To efficiently update the LSM, we establish the spatial correlation models of LoS probability on the location pairs in both the distance and angular domains, which are adopted in the Bayesian filter for updating the probabilities at locations without measurements. Simulation results demonstrate the effectiveness of the proposed algorithm for LSM construction, which significantly outperforms the benchmark scheme, especially when the measurements are sparse.