A Coordinate-Attention-Based Path Loss Prediction Scheme for Indoor IoT Applications

Indoor Internet of Things (IoT) applications, such as smart buildings and office automation, often require dense node deployments with power constraints. Reliable connections between gateways (GWs) and end nodes (ENs) rely on accurate knowledge of the path loss (PL). In this paper, we present a coordinate-attention (CA)-based scheme for PL prediction in indoor IoT scenarios. Within this scheme, a network termed CATransPropa integrates a CA module into the convolutional neural network (CNN) to capture the spatial distribution of indoor obstacles, aiming to enhance prediction accuracy. The network utilizes a multimodal input that combines environmental images and propagation features, including indoor-specific characteristics such as wall penetration thickness. Measurements are carried out in an indoor scenario at 433 MHz to verify the performance of the proposed scheme. It is shown that our scheme achieves a root mean square error (RMSE) value of 3.62 dB between the predicted and actual PL, outperforming the compared methods.