The propagation of acoustic waves under water is a highly complex and stochastic process. Such channel dynamics renders large performance variation in underwater acoustic (UWA) communications. Prediction of the UWA communication performance is critical for selection and adaptation of the communication strategies. This work explores the use of supervised learning for performance prediction in UWA communications. This work first quantifies the transmitter design, the UWA channel characteristics and the receiver design by numerical and categorical parameters. For a chosen performance metric (e.g., the bit error rate or the packet error rate), the performance prediction is cast individually into a numerical prediction problem and a classification problem. Using the data sets from two field experiments, the performance of typical supervised learning methods are examined. The data processing results reveal that some supervised learning methods can achieve fairly good numerical prediction or classification performance, and the discriminative models typically outperform the generative models.
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A Variational Auto-Encoder Model for Underwater Acoustic Channels
An underwater acoustic (UWA) channel model with high validity and re-usability is widely demanded. In this paper, we propose a variational auto-encoder (VAE)-based deep generative model which learns an abstract representation of the UWA channel impulse responses (CIRs) and can generate CIR samples with similar features. A customized training process is proposed to avoid the model collapse and being trapped in a gradient pit. The proposed deep generative model is validated using field experimental data sets.
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- Award ID(s):
- 1651135
- NSF-PAR ID:
- 10314504
- Date Published:
- Journal Name:
- the 15th International Conference on Underwater Networks & Systems (WUWNet)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3491315.3491330
