Sixth-generation (6G) mobile communication networks are expected to have dense
infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified …

In this paper we propose a highly efficient and very accurate deep learning method for
estimating the propagation pathloss from a point (transmitter location) to any point on a …

Radio maps characterize quantities of interest in radio communication environments, such
as the received signal strength and channel attenuation, at every point of a geographical …

Outdoor radio coverage map estimation is an important tool for network planning and
resource management in modern Internet of Things (IoT) and cellular systems. A radio map …

In this paper we propose a highly efficient and very accurate method for estimating the
propagation pathloss from a point x to all points y on the 2D plane. Our method, termed …

Radio maps provide radio frequency metrics, such as the received signal strength, at every
location of a geographic area. These maps, which are estimated using a set of …

Recently, there have been many attempts to apply Machine Learning (ML)-based prediction
mechanisms In wireless networks. One open question is how reliable such predictions can …

Pathloss quantifies the reduction in power density of a signal radiated from a transmitter. The
attenuation is due to large-scale effects such as free-space propagation loss and …

This paper addresses the off-grid tensor-guided interpolation problem, aiming to reconstruct
a 3D power spectrum map from sparse observations. A segmented polynomial model is …

Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting
multi-domain (eg, frequency and space) radio power propagation maps from limited sensor …