Link Reliability Prediction for Long-range Underwater Acoustic Communications between Gliders

Abstract

Channel modeling and the prediction of the reliability of acoustic link is the key to a successful deployment of underwater acoustic networks (UAN). In this paper, we build a Bellhop-based simulation framework driven by the environmental data to assess and predict the quality of the long-range and low-frequency communication links between the unmanned platforms in the deep ocean. The environmental data is measured in the South China Sea Experiment 2020 (SCSEx20). Results of channel modeling and link reliability prediction are reported in terms of the estimated channel impulse response (CIR), signal-to-noise ratio (SNR), and bit rate error (BER). The analysis shows how the communication performance of the physical level is dominated by the environmental data, aiming at evaluating the communication performance, and correlating the variation of the environmental conditions to the reliability of the entire communication link.