Supervised Learning for Performance Prediction in Underwater Acoustic Communications

Evan Lucas, Zhaohui Wang

Abstract:

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.