Prediction of Indoor Wireless Coverage from 3D Floor Plans Using Deep Convolutional Neural Networks

With significant advancements in Machine Learning and Deep Learning, Convolutional Neural Networks (CNNs) have shown promising results in handling classification and regression problems. We present 3DRSSNet, a model that can predict the signal strength of an Access point in a 3-D environment based on the 3-D floor map of the building. Our deep CNN approach differs from previous work in that (i) it can generalize to unseen environments, and (ii) to the best of our knowledge, it is the first work to utilize 3D maps to build a signal strength prediction model and validate its results using actual measured data. The proposed neural network model can help solve problems lie optimal access point placement and blind spot detection. Experimental results show that CNNs can predict indoor radio link quality with a performance with a Mean Absolute Error of 4.4 dBm and is able to generalize well to unseen environments.