Journal article
Recent results in shallow water acoustic channel estimation and equalization
The Journal of the Acoustical Society of America, Vol.152(4), pp.A40-A40
10/2022
DOI: 10.1121/10.0015469
Abstract
Shallow water acoustic channel estimation and equalization has been widely studied and explored across the last three decades, with several techniques being proposed that account for the rapidly time-varying nature of the channel. Despite advances in integrating robust channel equalization techniques with channel estimation, incorporating well-known models of acoustic propagation into the equalization setup has been hard. In this work, we will present some of our recent explorations in this regard involving a hybrid setting as well as a model-based simulation to test the performance of simple equalization schemes under a variety of channel conditions. The shallow water acoustic channel is first estimated from experimental field data, and the real-time channel estimates are used to simulate an end-to-end communication system with ambient noise at different signal-to-noise ratios (SNR). In tandem, we also simulate a time-varying shallow water acoustic channel under similar oceanic conditions using the Bellhop model, along with a communication system with similar signaling schemes and SNR variations. Equalization results from both the hybrid and model-based settings will be presented and implications of including machine learning to learn the channel parameters from both setups to inform the channel equalizers will be explored.
Details
- Title: Subtitle
- Recent results in shallow water acoustic channel estimation and equalization
- Creators
- Ananya Sen Gupta - University of IowaNathan KofronEva Riherd - Dept. of Elec. and Comput. Eng., Univ. of Iowa, Iowa City, IA
- Resource Type
- Journal article
- Publication Details
- The Journal of the Acoustical Society of America, Vol.152(4), pp.A40-A40
- DOI
- 10.1121/10.0015469
- ISSN
- 0001-4966
- eISSN
- 1520-8524
- Number of pages
- 1
- Language
- English
- Date published
- 10/2022
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984319357902771
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