Random versus structured projections: Compressive channel sensing for underwater communications under waveguide constraints
Underwater acoustic channels have rich selectivity in time, frequency, space and Doppler. These channel features need to be properly modeled and captured for effective channel equalization and data communication. However, estimation of high-dimensional acoustic channels entails heavy implementation costs, in terms of computational load, processing time and the number of data samples required for effective equalization. Compressed sensing offers a new paradigm for sparse channel estimation by collecting a small number of samples via random projections. Each random projection captures and (equally) weights in all components in the search space, without relying on any structural knowledge of the search space. On the other hand, some physics-based waveguide knowledge can be available for underwater acoustic channels, which can constrain both the number of arrivals and the range of their angles of arrival at a receiver based on the source-receiver geometry and water column, surface, and bottom properties. A structure-based projection approach is hence motivated for data sampling and channel reconstruction. Balancing between structured versus random projections, this paper develops new compressed sensing algorithms under partial structural knowledge that is obtained from a geometric model of the propagation channels. Simulations confirm the benefits of partially-structured random projections in terms of both improved recovery performance and reduced sampling costs. © 2012 SPIE.
Proceedings of SPIE - The International Society for Optical Engineering
Random versus structured projections: Compressive channel sensing for underwater communications under waveguide constraints.
Proceedings of SPIE - The International Society for Optical Engineering,
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