IRM-NET: An Enhanced Attention Networks for Indoor Radio Map Estimation

QiChen, Jingjing Yang, Ming Huang, Haidong Tan, Boyuan Chen

Keywords: Indoor radio map, U-Net, Detail enhancement, Path loss

TL;DR: A deep learning framework for indoor radio map estimation.

Abstract: In this paper, we introduce IRM-Net, a novel variant of the U-Net architecture designed for indoor radio map estimation. IRM-Net incorporates two principal improvements over the standard U-Net. First, we replace conventional convolutional layers with a cascaded combination of a Detail Enhancement Block (DEB) and a Detail Enhancement Attention Block (DEAB), which enhances the model’s ability to capture fine-grained features associated with local small-scale fading. Second, we implement dense connections in both the encoder and decoder, facilitating multi-level semantic interactions that mitigate information loss more effectively than traditional serial connections. IRM-Net was trained and evaluated on the benchmark dataset provided by the Sampling-Assisted Pathloss Radio Map Prediction Data Competition. Experimental results demonstrate that our approach can reliably predict path loss distributions in previously unseen indoor environments.

Submission Number: 2