William Mantzel
Compressive Matched-Field Processing.
Mon, 09/05/2011 - 15:01 — SomNambulAuthors: William Mantzel, Justin Romberg, Karim Sabra
Subjects: Information Theory
AbstractSource localization by matched-field processing (MFP) generally involves
solving a number of computationally intensive partial differential equations.
This paper introduces a technique that mitigates this computational workload by
"compressing" these computations. Drawing on key concepts from the recently
developed field of compressed sensing, it shows how a low-dimensional proxy for
the Green's function can be constructed by backpropagating a small set of
random receiver vectors.Channel Protection: Random Coding Meets Sparse Channels.
Mon, 08/31/2009 - 17:16 — SomNambulAuthors: M. Salman Asif, William Mantzel, Justin Romberg
Subjects: Information Theory
AbstractMultipath interference is an ubiquitous phenomenon in modern communication
systems. The conventional way to compensate for this effect is to equalize the
channel by estimating its impulse response by transmitting a set of training
symbols. The primary drawback to this type of approach is that it can be
unreliable if the channel is changing rapidly. In this paper, we show that
randomly encoding the signal can protect it against channel uncertainty when
the channel is sparse. Before transmission, the signal is mapped into a
slightly longer codeword using a random matrix.
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