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.
Highlights
Ocean, as the origin of life, covers two thirds of our planet, supports 90% of the world’s freight traffic, and contains a vast amount of underutilized resources
The proposed online learning aided k-nearest neighbor (A-kNN) classifier based on supervised learning (SL) enables a novel implementation of Adaptive modulation and coding (AMC), which has excellent immunity to channel modeling uncertainty
To handle the inherent high-complexity issues, we further present the DRDC-A-kNN classifier for feature dimensionality reduction and data condensation, which can offer a great complexity reduction compared to the A-kNN approach, and facilitate an easier implementation of the AMC systems
Summary
As the origin of life, covers two thirds of our planet, supports 90% of the world’s freight traffic, and contains a vast amount of underutilized resources. Directly adopting the conventional kNN algorithm where each neighbor has a equal weight in the voting stage will inevitably bring some performance degradation to the classification, or even lead to incorrect results To address this issue, we resort to the attention mechanism and propose the A-kNN algorithm for the underwater AMC task. Such mapping relation will significantly confuse the classifier and make it impossible to determine the optimal MCS for each specific fj through training To solve this problem, we use Eq (5) to modify the sets and only retain information of the desired mopt, so as to obtain a one-to-one mapping function for model training.
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More From: EURASIP Journal on Wireless Communications and Networking
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