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

With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

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 …

With outstanding features, Machine Learning (ML) has been the backbone of numerous
applications in wireless networks. However, the conventional ML approaches have been …

Radio maps provide metrics such as power spectral density for every location in a
geographic area and find numerous applications such as UAV communications, interference …

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 …

The future of cellular networks is contingent on artificial intelligence (AI) based automation,
particularly for radio access network (RAN) operation, optimization, and troubleshooting. To …

Radio maps quantify received signal strength or other magnitudes of the radio frequency
environment at every point of a geographical region. These maps play a vital role in a large …

Network performance modeling using machine learning (ML) has proven to be essential for
proactive network and service management. Dynamic changes and re-configurations in the …