Exploiting time varying sparsity for underwater acoustic communication via dynamic compressed sensing.

While it has been recognized that the multipath structure of the underwater acoustic (UWA) channel offers the potential for compressed sensing (CS) sparsity exploitation, the rapidly time varying arrivals induced by highly dynamic surfaces unfortunately pose significant difficulties to channel estimation. From the viewpoint of underwater acoustic propagation, with the exception of the highly time varying arrivals caused by dynamic surface, generally there exist relatively stationary or slowly changing arrivals caused by direct path or bottom reflection, which imply the adoption of a discriminate estimation method to handle sparse components with different time variation scale. By modeling the time varying UWA channels as a sparse set consisting of constant and time-varying supports, in this paper, estimation of time varying UWA channel is transformed into a problem of dynamic compressed sensing sparse recovery.

Distributed compressed sensing based channel estimation for underwater acoustic multiband transmissions.

Distributed compressed sensing techniques are applied to enhance sparse channel estimation performance in underwater acoustic multiband systems. The core idea is to use receptions from multiple sub-bands to enhance the detection of channel tap positions. A known variant of the orthogonal matching pursuit (OMP) algorithm based on the distributed compressed sensing principle is simultaneous orthogonal matching pursuit (SOMP).

Underwater acoustic channel characteristics and communication performance at 85 kHz.

A high frequency acoustic experiment was conducted in the northern portion of the Gulf of Mexico in August 2016 to examine the operating range, data rates, and performance of acoustic communication systems at the carrier frequency of 85 kHz. The received signal-to-noise ratios, channel coherence, and impulse responses were reported between two 85 kHz transducers and a five-element hydrophone array over multiple ranges up to 1500 m. Channel estimation based decision feedback equalizers (DFEs) were applied to process the communication measurements.