Mobile communication technology is evolving rapidly and becoming increasingly ubiquitous,
thereby increasing the demand for uplink data-intensive applications (eg, personal …

Recently, deep learning has emerged as a state-of-the-art machine learning technique with
promising potential to drive significant breakthroughs in a wide range of research areas. The …

While the 5G New Radio (NR) network promises a huge uplift of the uplink throughput, the
improvement can only be seen when the User Equipment (UE) is connected to the high …

Accurate and efficient resource utilization predictions are of vital importance for the future
generation of mobile wireless networks. By anticipating network resource demand, the …

Deep learning is a branch of machine learning that learns the high-level abstraction of data
in a layered structure. Since its invention, it has been successfully applied in many image …

5G networks are expected to provide high-speed, low-latency, and reliable connectivity to
support various applications such as autonomous vehicles, smart cities, and the Internet of …

Cellular traffic prediction at mobile edges is extremely valuable to ultra high-reliability low-
latency (URLLC) communication of 5G. Many network actions depend on this prediction …

The design and deployment of fifth-generation (5G) wireless networks pose significant
challenges due to the increasing number of wireless devices. Path loss has a landmark …

Machine learning-based data rate prediction is one of the key drivers for anticipatory mobile
networking with applications such as dynamic Radio Access Technology (RAT) selection …

By integrating communications in different domains, integrated radio and optical networks
can serve a wider range of applications and services. Integrated radio and optical network …