Underwater Acoustic Communication System Based on MC-MFSK

Gao, Chun Xian; Yang, Hongxing; Yuan, Fei; Cheng, En

doi:10.4028/www.scientific.net/amm.556-562.4897

Cited by 2 publications

(1 citation statement)

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“…From FSK and phase-shift keying (PSK), through orthogonal frequency-division multiplexing (OFDM), to the latest orthogonal signal-division multiplexing (OSDM) [32][33][34], these modulation schemes have been investigated extensively and proven useful in the harsh oceanic environment. In this paper, we adopt the scheme of MC-MFSK, which combines the techniques of MFSK and OFDM to transmit information in parallel over multiple orthogonal subchannels [35,36]. As such, this method not only inherits the robust performance of MFSK, but also integrates the high spectral efficiency of OFDM.…”

Section: Mc-mfskmentioning

confidence: 99%

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

Huang

Zhang

Tan

et al. 2020

J Wireless Com Network

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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.

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