Underwater acoustic channel characteristics and communication performance at 85 kHz

Zhou, Yuehai; Song, Aijun; Tong, Feng

doi:10.1121/1.5006141

Cited by 28 publications

(12 citation statements)

References 8 publications

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“…The UAC coherence time is usually in the order of hundreds of milliseconds for practical system designs for horizontal transmission scenarios [13]. Therefore, for a practical vertical UAC of similar range, the coherence time is expected to be higher because the transmission is less affected by surface and seabed reflections [19]. If we consider an RMS Doppler spread of 1 Hz, the channel coherence time can be considered to be around 100 ms, which already characterises a dynamic UAC.…”

Section: Simulation Resultsmentioning

confidence: 99%

Performance of OFDM‐based massive MIMO OTFS systems for underwater acoustic communication

Bocus¹,

Doufexi²,

Agrafiotis³

2020

IET Communications

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Section: Simulation Resultsmentioning

confidence: 99%

Performance of OFDM‐based massive MIMO OTFS systems for underwater acoustic communication

Bocus¹,

Doufexi²,

Agrafiotis³

2020

IET Communications

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“…With the model defined in (26), a number of approaches can be measured the optimal filter coefficients (f m , b m , and r m,k ). In this paper, we adopt the linear minimum mean square error method to calculate the filter coefficients.…”

Section: Channel Estimation Based Decision Feedback Equalization With Interference Cancellationmentioning

confidence: 99%

Exploiting Spatial–Temporal Joint Sparsity for Underwater Acoustic Multiple-Input–Multiple-Output Communications

Zhou

Tong

Song

et al. 2021

IEEE J. Oceanic Eng.

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Multiple-input-multiple-output (MIMO) system offers a promising way for high data rate communication over bandwidth limited underwater acoustic channels. However, MIMO communication not only suffers from inter-symbol interference, but also introduces the additional co-channel interference, which brings challenge for underwater acoustic MIMO channel estimation and for channel equalization. In this paper, we propose novel interference cancellation (IC) methods for handling this co-channel interference problem in the design of both channel estimation and channel equalization. Our method for channel estimation utilizes the spatial joint sparsity and the temporal joint sparsity in the multipath structure to estimate sparse channels with common delays under distributed compressed sensing (DCS) framework. In this way, we enhance channel estimates with common delays, thus suppress co-channel interference. Meanwhile, to address the case of multipath arrivals with different delays, which are estimated as noise under simultaneous orthogonal matching pursuit (SOMP) algorithm, we introduce forward-reverse strategy to SOMP algorithm, which is referred to as FRSOMP algorithm. Our proposed FRSOMP algorithm performs SOMP algorithm to achieve the initial channel estimates, performs forward-add process which attempts to add promising candidates into support-sets, and performs reverse-fetch process to check if the candidates in the support-set are retained or removed. The purpose of channel estimation is to directly calculate the filter coefficients for channel estimation based decision feedback equalization (CE-DFE). In this paper, we also propose a novel CE-DFE receiver with IC component. We design IC filters based on the traditional CE-DFE, and we derive the coefficients of the feedforward filters, feedback filters and IC filters based on the channel estimate metric obtained by FRSOMP algorithm, so the co-channel interference will be suppressed both in channel estimation and channel equalization. We demonstrate the performance of our approach by numerical simulation, lake experiment, and sea experiment. Results are provided to demonstrate the effectiveness of the proposed methods, results show that the proposed methods obtain higher output signal-to-noise ratio (SNR), lower bit error rate (BER), and more separated constellations compared with traditional compressed sensing channel estimation method and traditional CE-DFE method.

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“…Generally for radio WSNs, the reliability of radio communication channels is considered better than that of underwater acoustic channels in UWANs [52] due to multiple acoustic propagation paths and plentiful acoustic interference sources in oceans, such as marine animals (e.g., whales and even shrimps), rains, ships and even bubbles besides other acoustic devices [20]. For example, the measured bit error rate is 4 × 10 −3 for a data rate of 34 kbps over a 1500m-link with 9dB-SNR and 21.25 kHz bandwidth at 85 kHz frequency [53]. Therefore, more bandwidth is needed for reliable transmission in UWANs, resulting in much less bandwidth available for network security.…”

Section: Challenges For Uwans Securitymentioning

confidence: 99%

On Securing Underwater Acoustic Networks: A Survey

Jiang

2019

IEEE Commun. Surv. Tutorials

123

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Underwater acoustic networks (UWANs) are often deployed in unattended and untransparent or even hostile environments and face many security threats, while many applications based on UWANs require secure communication, such as costal defense, submarine communication and harbor security. Peculiar features of UWANs such as very constrained resources pose big challenges in defending UWANs against security threats, and many research results are published to address these issues along with several brief surveys available in the literature. This paper aims to provide a comprehensive survey on UWAN security by first discussing the fundamental of network security in general and the main UWAN security threats faced by the physical layer to the transport layer. Then the paper reviews countermeasure schemes against the typical UWAN security threats, securing UWAN protocols and cryptographic primitives designed for UWANs as well as UWAN security structures that address several security issues systematically. The research of UWAN security is still in an early stage, and the paper discusses several important issues necessarily for further studies at the end.

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Underwater acoustic channel characteristics and communication performance at 85 kHz

Cited by 28 publications

References 8 publications

Performance of OFDM‐based massive MIMO OTFS systems for underwater acoustic communication

Performance of OFDM‐based massive MIMO OTFS systems for underwater acoustic communication

Exploiting Spatial–Temporal Joint Sparsity for Underwater Acoustic Multiple-Input–Multiple-Output Communications

On Securing Underwater Acoustic Networks: A Survey

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