Adaptive modulation and coding in underwater acoustic communications: a machine learning perspective

Abstract

The increasing demand for exploring and managing the vast marine resources of the planet has underscored the importance of research on advanced underwater acoustic communication (UAC) technologies. However, owing to the severe characteristics of the oceanic environment, underwater acoustic (UWA) propagation experiences nearly the harshest wireless channels in nature. This article resorts to the perspective of machine learning (ML) to cope with the major challenges of adaptive modulation and coding (AMC) design in UACs. First, we present an ML AMC framework for UACs. Then, we propose an attention-aided k-nearest neighbor (A-kNN) algorithm with simplicity and robustness, based on which an ML AMC approach is designed with immunity to channel modeling uncertainty. Leveraging its online learning ability, such A-kNN-based AMC classifier offers salient capabilities of both sustainable self-enhancement and broad applicability to various operation scenarios. Next, aiming at higher implementation efficiency, we take strategies of complexity reduction and present a dimensionality-reduced and data-clustered A-kNN (DRDC-A-kNN) AMC classifier. Finally, we demonstrate that these proposed ML approaches have superior performance over traditional model-based methods by simulations using actual data collected from three lake experiments.

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

“…Multiple kinds of classification models are used to predict MCS in [14], [22], [29], [30]. The underwater environment dataset is trained and extracted the feature, using the CNN classification model in [14].…”

“…In addition, [22], [30] predicted MCS values by using boosted tree; [30] shows 99% accuracy. In [29], the cluster algorithm is used, and in [27], [28] MCS are predicted by Q-learning; that one of the reinforcement learning. However, all previous studies have used machine…”

Adaptive modulation based on steady-state mean square error for underwater acoustic communication

“…In order to obtain the parameter value c i , a necessary condition for E(•) to be a minimum is that After the differential calculation for Eq. (13), we obtain Because function F (•) has the form of (12), we get Therefore, applying (16) to (15), we have Equation (17) forms a system of k equations in k unknown parameters c i By solving formula (18), the estimation ĉ i for parameters c i can be obtained. Then, we substitute ĉ i into (12), the correction estimation value for γ a,n can be given by γ o,n can be calculated with (10) Substitute (20) into (19), the relationship between SMSE, SMSE n and estimation SNR γ a,n can be given by (13)…”

“…In [11,12], the future BER is predicted based on the CSI and SNR using the decision tree approach. In [13][14][15], the machine learning algorithms are used to select the modulation mode. However, these schemes in [9][10][11][12][13][14][15] assume that the CSI can be well estimated and non-blind equalization approach is adopted.…”

“…In [13][14][15], the machine learning algorithms are used to select the modulation mode. However, these schemes in [9][10][11][12][13][14][15] assume that the CSI can be well estimated and non-blind equalization approach is adopted. For practical underwater acoustic communication, due to the complexity and variability of the underwater acoustic environment, the CSI is actually difficult to obtain.…”