Date Added: Jul 2010
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.