Distributed compressed sensing based channel estimation for underwater acoustic multiband transmissions

Abstract

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). However, the impulse responses across multiple sub-bands may have different arrival structures, although they often show a certain level of similarity. To address such differences at the sub-bands, a multiple selection strategy is applied to select multiple candidates at individual sub-bands at each iteration. This is different from the conventional OMP and SOMP algorithms that select only one candidate at each iteration. When the multiple selection strategy is combined with the SOMP algorithm, the proposed algorithm is referred to as JB-MSSOMP algorithm. To take advantage of channel coherence between adjacent data blocks from different sub-bands, the multiple selection strategy is further used over time. This leads to JBT-MSSOMP algorithm. Computer simulations show improved channel estimation performance of the proposed JB-MSSOMP and JBT-MSSOMP algorithms over the OMP or SOMP algorithms. Communication data from a recent acoustic experiment demonstrates improved receiver performance with the proposed channel estimators.

Exploiting Spatial–Temporal Joint Sparsity for Underwater Acoustic Multiple-Input–Multiple-Output Communications

“…Two adjacent data blocks in the frequency domain were combined to obtain joint sparsity channel estimation using SOMP algorithm. In [4], DCS was used for underwater acoustic multiband transmissions. The traditional SOMP algorithm was used to enhance channel estimates that have common delays, and the proposed multiple selection strategy based SOMP algorithm was utilized to reconstruct channel taps that have different delays.…”

Sparse Reconstruction-Based Inverse Scattering Imaging in a Shallow Water Environment

“…Compressive sensing or sparse reconstruction method is widely used in underwater acoustics [22] , especially in the field such as direction of arrival (DOA) estimation [23]- [25] , CIR estimation [26], [27] , and near-field acoustic holography [28], [29] . However, seldom underwater acoustic imaging works are under the compressive sensing framework.…”

Channel Estimation Based Equalizer for Underwater Acoustic Multiple-Input-Multiple-Output Communication

“…So far CEE has been used in SIMO underwater acoustic communication [13]- [16]. For example, in [13], the channel estimation based decision feedback equalizer (CE-DFE) was used in high frequency underwater acoustic communication; in [14], the CE-DFE was used in underwater acoustic multi-band communication; in [15] and [16], the CE-DFE was used in time-varying underwater acoustic communication. However, so far, there is few reports of applying CE-DFE in MIMO communication scenario, or adopting CE-DFE to mitigate CoI either.…”