In our numerical study, we find that providing requirements by single connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antenna.
This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN).
There is a lack of research on the analysis of per-user traffic in cellular networks, for deriving and following traffic-aware network management.
Machine learning-based systems are rapidly gaining popularity and in-line with that there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process.
Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm.
The results further provide insights on the benefits of leveraging intelligent RRM, e. g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99. 99% reliability of both scheduled and nonscheduled traffic types is satisfied.