On the Effect of Channel Knowledge in Underwater Acoustic Communications: Estimation, Prediction and Protocol

Abstract

Underwater acoustic communications are limited by the following channel impairments: time variability, narrow bandwidth, multipath, frequency selective fading and the Doppler effect. Orthogonal Frequency Division Modulation (OFDM) is recognized as an effective solution to such impairments, especially when optimally designed according to the propagation conditions. On the other hand, OFDM implementation requires accurate channel knowledge atboth transmitter and receiver sides. Long propagation delay may lead to outdated channel information. In this work, we present an adaptive OFDM scheme where channel state information is predicted through a Kalman-like filter so as to optimize communication parameters, including the cyclic prefix length. This mechanism aims to mitigate the variability of channel delay spread. This is cast in a protocol where channel estimation/prediction are jointly considered, so as to allow efficiency. The performance obtained through extensive simulations using real channels and interference show the effectiveness of the proposed scheme, both in terms of rate and reliability, at the expense of an increasing complexity. However, this solution is significantly preferable to the conventional mechanism, where channel estimation is performed only at the receiver, with channel coefficients sent back to the transmit node by means of frequent overhead signaling.

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

“…In [43], by exploiting channel knowledge obtained from prediction techniques, the author proposed an OFDM scheme which can adaptively adjust the length of the cyclic prefix. Channel prediction was operated in the frequency domain, and the evolution of a single sub-channel was tracked via a Kalman filter.…”

“…Algorithm Classification Whether the Algorithm Needs Historical Channel Data to Train RLS [4] Linear Algorithm No LMMSE [33] Linear Algorithm No ES [36] Linear Algorithm No Kalman filtering [43] Linear Algorithm No KRLS [56] Kernel-Based Algorithm No SVR [62] Kernel-Based Algorithm Yes LSTM [73] Deep Learning Algorithm Yes Each tap of the channel is predicted separately and then combined together for error calculation and performance analysis. The NMSE and normalized channel prediction error are taken as the performance metrics.…”

A High-Precision Positioning Method for Autonomous Underwater Vehicles with Communication Delays

“…For instance, in [29], a variational Bayesian-based Adaptive EKF algorithm is proposed for cooperative navigation of master-slave autonomous underwater vehicles (AUVs), improving the robustness of unknown or time-varying noises accordingly. Nonetheless, within the practical underwater navigation environment, underwater acoustic communications are limited by the following channel impairments: time variability, narrow bandwidth, multipath, frequency-selective fading [30]. The mutual communication between AUVs and other nodes, along with the processes of data handling and underwater data transmission, inevitably entail a specific duration.…”