In this paper, we propose a deep learning pipeline to predict the signal strength map of a
wireless channel at unvisited locations over the space, based on very sparse channel …

Efficient and accurate indoor radio signal strength prediction methods are essential for the
design and operation of wireless communication systems. Recently, attempts have been …

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied
to simulating complex physics by learning physics models thanks to large data. Inspired by …

We present NeWRF, a novel deep-learning-based framework for predicting wireless
channels. Wireless channel prediction is a long-standing problem in the wireless community …

We present NeWRF, a deep learning framework for predicting wireless channels. Wireless
channel prediction is a long-standing problem in the wireless community and is a key …

The techniques used in deep learning for channel estimation are generally model-centric.
These models have changed significantly over the years with each iteration yielding a better …

In this thesis, the neural network, a powerful tool which has demonstrated its ability in many
fields, is studied for the indoor localization system and channel prediction system. This …

We introduce efficient deep learning models that estimate maps of two indoor radio
propagation parameters: i) received power and ii) cell association. These models, based on …

Confining wireless signals (WiFi) in specific areas of indoor spaces is an efficient way to
protect these networks against unwanted access. Unfortunately, these same WiFi signals …

Site-specific radio frequency (RF) propagation pre-diction increasingly relies on models built
from visual data such as cameras and LIDAR sensors. When operating in dynamic settings …