  Radio Environment Maps (REMs) are crucial for numerous applications in
Telecom. The construction of accurate Radio Environment Maps (REMs) has become
an important and challenging topic in recent decades. In this paper, we present
a method to estimate REMs using Graph Neural Networks. This approach utilizes
both physical cell information and sparse geo-located signal strength
measurements to estimate REMs. The method first divides and encodes mobile
network coverage areas into a graph. Then, it inputs sparse geo-located signal
strength measurements, characterized by Reference Signal Received Power (RSRP)
and Reference Signal Received Quality (RSRQ) metrics, into a Graph Neural
Network Model to estimate REMs. The proposed architecture inherits the
advantages of a Graph Neural Network to capture the spatial dependencies of
network-wide coverage in contrast with network Radio Access Network node
locations and spatial proximity of known measurements.
