GPU Accelerated Radio Path Loss Estimation With Neural Networks
Radio path loss prediction is an important but computationally expensive component of wireless communications simulation. Models may require significant computation to reach a solution or require that information about the environment between transceivers be collected as model inputs which may also be computationally expensive. Despite the complexity of the underlying model that generates a path loss solution, the resulting function is not necessarily complex, and there may be ample opportunity for compression. The authors introduce a method for rapidly estimating radio path loss with Feed-Forward Neural Networks (FFNNs), in which not only path loss models but map topology is implicitly encoded in the network.