Optimal Measurement Point Selection for Radio Environment Maps: A Two-Stage Approach

Abstract

Electromagnetic (EM) situational awareness is essential for the deployment and operation of local and temporary non-public networks (NPNs). Radio Environment Maps (REMs) provide an effective basis for network planning and operation by capturing the prevailing EM conditions. However, efficient measurement location selection for REM construction remains a key challenge, particularly under strict time and cost constraints. This paper presents a two-stage measurement selection framework that employs Optimal Experimental Design (OED)-based strategies to generate a reduced candidate set during a pre-selection phase. In a subsequent optimization stage, combinatorial optimization is applied to select an improved subset of measurement locations by minimizing the Mean Absolute Error (MAE) of the resulting REM, using a scenario-specific propagation simulation model. The results demonstrate that the proposed framework reduces the number of required measurement locations while consistently improving REM construction accuracy across all evaluated strategies. Validation using real-world measurement data confirms the practical applicability of the approach, with the optimization stage achieving MAE reductions from about 4% to around 50%, independent of the chosen pre-selection strategy.