Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks

Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this article, we leverage graph signal processing (GSP) to characterize the...

Underwater Acoustic Communication Using Multiple-Input-Multiple-Output Doppler-Resilient Orthogonal Signal Division Multiplexing

In this paper, we propose a novel underwater acoustic (UWA) communication scheme that achieves energy and spectrum efficiency simultaneously by combining Doppler-resilient orthogonal signal division multiplexing (D-OSDM) and multiple-input-multiple-output (MIMO) signaling. We present both the transmitter processing and the receiver processing...