RecNet: A Convolutional Network for Efficient Radiomap Reconstruction
Qun Niu, Ying Nie, Suining He, Ning Liu, Xiaonan Luo · 2018
Wireless signals have been a strong indicator of nearby wireless environment, which can be used in a wide range of applications, including the construction and maintenance of wireless network in smart cities. However, wireless signals can change drastically due to moving pedestrians and automatic power adjustment of Access Points (APs), which renders previous radiomap inaccurate. To achieve sufficient accuracy, surveyors have to update the radiomap constantly, which incurs high maintenance cost in the long run. To address this, we propose RecNet, a neural network to reconstruct a fine-grained radiomap with a small number of new samples. The intuition lies in the visualization of numerical signal strength values by a heatmap. A high-resolution heatmap corresponds to a fine-grained radiomap while a low-resolution one corresponds to a coarse-grained radiomap. Then we reduce the radiomap reconstruction to the image super-resolution: generating a high-resolution image from a low-resolution one. Based on the above, we design and implement the RecNet based on the image super-resolution neural network. Extensive experiments in two large test sites demonstrate that RecNet is able to reconstruct an accurate radiomap with only 50% of fingerprints, and reduces the signal error by more than 20% compared with a recent reconstruction algorithm.