Variability in Shallow Water Communication Performance Near a Busy Shipping Lane

Koay, Teong Beng; Deane, Grant B.; Chua, Gabriel

doi:10.1109/ucomms50339.2021.9598017

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…In sum, the underwater acoustic transmission channel is a challenging transmission channel, with multipath propagation, noise and massive Doppler shifts. Furthermore, the acoustic underwater channel changes frequently for example due to temperature variations, waves, different shipping activity, or tidal changes [65].…”

Section: ) Comparison the Over-water Rf Channelmentioning

confidence: 99%

Doppler Shift and SFO Robust Synchronization for LoRa-Like Acoustic Underwater Communication

Steinmetz,

Renner

2023

IEEE Access

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Frequency shift chirp modulation (FSCM) is the modulation technique utilized by the LoRa physical layer, a widely used and relatively new communication technique for radio low-power wide area networks (LPWANs). In this paper, we adapt and implement FSCM for low-power acoustic underwater communication. Opposed to over-water communication based on the electromagnetic wave, the acoustic wave in water travels much slower. Therefore, acoustic underwater communication is more sensitive to motion-based Doppler shifts. For a resilient acoustic underwater communication Doppler tracking and removal is mandatory. In this paper, we mathematically derive the effect of Doppler shifts to FSCM symbols. Afterwards, we describe a novel algorithm for a low-power receiver to track Doppler shifts without modification of the transmitted packet. Finally, we evaluate and compare our algorithm to another Doppler estimation algorithm from literature and study the effect on the bit error rate (BER). We show, that our algorithm can improve with lightweight additional computational overhead the BER by a factor of more than 10 4 in some cases.

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Section: ) Comparison the Over-water Rf Channelmentioning

confidence: 99%

Doppler Shift and SFO Robust Synchronization for LoRa-Like Acoustic Underwater Communication

Steinmetz,

Renner

2023

IEEE Access

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“…This array can be interpreted as our 224 bit packet, where the bits set to "1" are wrong and the bits set to "0" are correct. Therefore, since we have adopted Hamming (7,4), the packet is scanned with a 7-bits step: since Hamming (7,4) cannot correct more than one error every 7 bits, whenever the sum of the bits in a block is greater than 1 we mark the block as compromised and the whole packet is considered corrupted. The process is iterated for N = 1000 times and the PER value is given by the number of corrupted packets divided by N.…”

Section: Ber Thresholdsmentioning

confidence: 99%

“…Second, the presence of water currents, wind, and mobile nodes causes a strong Doppler effect that affects the received signal [3]. Lastly, noise caused by wind waves, rain, snapping shrimps, bubbles brought by tidal inflow, and ship propellers [4] causes the degradation of the signal to noise ratio (SNR). The use of realistic channel models, such as the Bellhop ray tracer [5] where a subset of these parameters can be included, is computationally demanding and hence restricted to networks with a small number of nodes.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Modeling Acoustic Channel Variability in Underwater Network Simulators from Real Field Experiment Data

2022

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The underwater acoustic channel is remarkably dependent on the considered scenario and the environmental conditions. In fact, channel impairments differ significantly in shallow water with respect to deep water, and the presence of external factors such as snapping shrimps, bubbles, rain, or ships passing nearby, changes of temperature, and wind strength can change drastically the link quality in different seasons and even during the same day. Legacy mathematical models that consider these factors exist, but are either not very accurate, like the Urick model, or very computationally demanding, like the Bellhop ray tracer. Deterministic models based on lookup tables (LUTs) of sea trial measurements are widely used by the research community to simulate the acoustic channel in order to verify the functionalities of a network in certain water conditions before the actual deployment. These LUTs can characterize the link quality by observing, for instance, the average packet error rate or even a time varying packet error rate computed within a certain time window. While this procedure characterizes well the acoustic channel, the obtained simulation results are limited to a single channel realization, making it hard to fully evaluate the acoustic network in different conditions. In this paper, we discuss the development of a statistical channel model based on the analysis of real field experiment data, and compare its performance with the other channel models available in the DESERT Underwater network simulator.

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“…This implies that relying on a classical Gaussian or Rician distribution to understand channel performance will not lead to accurate results, unless we are considering simpler short-term dependencies [3]. A number of factors are responsible for this behavior: from temperature, wind, water currents, to Doppler caused by Vortex Induced Vibrations (VIV) [4] or bubbles brought by tidal inflow and produced by ship propellers in a near shipping lane [5]. Furthermore, the use of realistic channel models, such as the Bellhop ray tracer [6], is highly computationally demanding and hence restricted to networks with a small number of nodes.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Modeling acoustic channel variability in underwater network simulators from real field experimental data

Toffolo

Montanari

Campagnaro

et al. 2022

OCEANS 2022, Hampton Roads

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The features of the underwater acoustic channel are remarkably dependent on the considered scenario; for instance, the link quality differs significantly in shallow water with respect to deep water, and series of events such as the presence of rain or ships passing nearby, changes of temperature and wind strength, can change drastically the channel conditions observed in a certain area in different seasons and even during the same day. Mathematical models that consider these parameters exist, but are either very computationally demanding, like the Bellhop ray tracer, or not sufficiently accurate, like the Urick model that often exhibits optimistic results. In this paper, we discuss the development of a statistical channel model based on the analysis of real field experimental data and compare its performance with the other channel models available in the DESERT Underwater network simulator.

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Variability in Shallow Water Communication Performance Near a Busy Shipping Lane

Cited by 5 publications

References 16 publications

Doppler Shift and SFO Robust Synchronization for LoRa-Like Acoustic Underwater Communication

Doppler Shift and SFO Robust Synchronization for LoRa-Like Acoustic Underwater Communication

Modeling Acoustic Channel Variability in Underwater Network Simulators from Real Field Experiment Data

Modeling acoustic channel variability in underwater network simulators from real field experimental data

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