DC-Net: A Distant-range Content Interaction Network for Radio Map construction

Abstract

The Radio Map (RM) stands as a powerful tool in wireless communication and radio regulation. Currently, several RM construction models based on Convolutional Neural Networks (CNNs) have been proposed. However, due to the inherent limitations of vanilla convolutions, capturing distant-range information of the radio propagation environment becomes challenge. Therefore, this letter introduces a novel RM construction model called Distant-range Content interaction Network (DC-Net) and validates its effectiveness using the RadioMapSeer dataset. Experiment results reveal that DC-Net achieves state-of-the-art performance in mapping accuracy for both known and unknown transmitter locations in RM construction.