Data-Driven Radio Environment Map Estimation Using Graph Neural Networks
This work addresses the challenge of accurate REM estimation for Telecom operators, but it appears incremental as it applies an existing GNN method to a specific domain problem.
The paper tackles the problem of constructing accurate Radio Environment Maps (REMs) for Telecom applications by proposing a method that uses Graph Neural Networks to estimate REMs from sparse signal strength measurements and physical cell information, achieving results that capture spatial dependencies effectively.
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.