Editorial Underwater Acoustic Communications: Where We Stand and What Is Next?

Song, Aijun; Stojanovic, Milica; Chitre, Mandar

doi:10.1109/joe.2018.2883872

Cited by 108 publications

(30 citation statements)

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“…Such local sensing networks in remote deployments may have the capability to optimize observational assets and sampling strategies, as well as conduct calibrations without human input. If the community can forecast the types of such capabilities that will be most advantageous, we can identify promising pathways forward in underwater communication and networking (e.g., Leonard et al, 2007;Taya et al, 2018;Song et al, 2019) and data fusion (already used for above-water radar applications like (Guerriero et al, 2010).…”

Section: Remaining Challenges: Power and Data Managementmentioning

confidence: 99%

Advancing Observation of Ocean Biogeochemistry, Biology, and Ecosystems With Cost-Effective in situ Sensing Technologies

et al. 2019

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Section: Remaining Challenges: Power and Data Managementmentioning

confidence: 99%

Advancing Observation of Ocean Biogeochemistry, Biology, and Ecosystems With Cost-Effective in situ Sensing Technologies

et al. 2019

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“…When there is the relative motion between the transmitter and the receiver, the channels can drastically change, making it challenging to achieve accurate estimates and reliable equalizations of the time-varying multi-path channels [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Joint Channel Estimation and Generalized Approximate Messaging Passing-Based Equalization for Underwater Acoustic Communications

et al. 2021

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Acquiring channel state information and mitigating multi-path interference are challenging for underwater acoustic communications under time-varying channels. We address the issues using a superimposed training (ST) scheme with a least squares (LS) based channel estimation algorithm. The training sequences with a small power are linearly superimposed with the symbol sequences, and the training signals are transmitted over all time, resulting in enhanced tracking capability to deal with timevarying underwater acoustic channels at the cost of only a small power loss. To realize the full potentials of the ST scheme, we develop a LS based channel estimation algorithm with superimposed training, where the Toeplitz matrix is used, which is formed by the training sequences, enabling channel estimation with superimposed training. In particular, a low-complexity channel equalization algorithm based on generalized approximate messaging passing (GAMP) is proposed, where the a priori, a posteriori, extrinsic means and variances of interleaved coded bits are computed, and then convert them into extrinsic log likelihood ratios for BCJR decoding. Its computational complexity is only in a logarithmic order per symbol. Moreover, the channel estimation, GAMP equalization and decoding are performed jointly in an iterative manner, so that the estimated symbol sequences can also be used as virtual training sequences to improve the channel estimation and tracking performance, thereby remarkably enhance the overall system performance. Moving communication experiments in Jiaozhou Bay (communication frequency 12 kHz, bandwidth 6 kHz, sampling frequency 96 kHz, symbol transmission rate 4 ksym/s) were carried out, and the experimental results verify the effectiveness of the proposed technique.INDEX TERMS Time-varying underwater acoustic channels, superimposed training, generalized approximate messaging passing, iterative turbo receiver

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“…With the development of underwater acoustic channel estimation and tracking, there has emerged a trend of researchers increasingly considering the physics of the channel before to search for solutions in radio communications [3]- [6]. One of the physical phenomena of the underwater acoustic channel was demonstrated in [6]- [8], where it was shown that for many underwater acoustic channel impulse responses (CIRs), the multipath taps are correlated.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-Varying Channels

Huang

Zhan

et al. 2021

IEEE Access

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In this work, we focus on the model-mismatch problem for model-based subspace channel tracking in the correlated underwater acoustic channel. A model based on the underwater acoustic channel's correlation can be used as the state-space model in the Kalman filter to improve the underwater acoustic channel tracking compared that without a model. Even though the data support the assumption that the model is slow-varying and uncorrelated to some degree, to improve the tracking performance further, we can not ignore the model-mismatch problem because most channel models encounter this problem in the underwater acoustic channel. Therefore, in this work, we provide a dynamic time-variant state-space model for underwater acoustic channel tracking. This model is tolerant to the slight correlation after decorrelation. Moreover, a forward-backward Kalman filter is combined to further improve the tracking performance. The performance of our proposed algorithm is demonstrated with the same at-sea data as that used for conventional channel tracking. Compared with the conventional algorithms, the proposed algorithm shows significant improvement, especially in rough sea conditions in which the channels are fast-varying.INDEX TERMS Correlated channels, model mismatch, underwater acoustic channels, channel tracking, forward-backward Kalman filter.

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Editorial Underwater Acoustic Communications: Where We Stand and What Is Next?

Cited by 108 publications

References 0 publications

Advancing Observation of Ocean Biogeochemistry, Biology, and Ecosystems With Cost-Effective in situ Sensing Technologies

Advancing Observation of Ocean Biogeochemistry, Biology, and Ecosystems With Cost-Effective in situ Sensing Technologies

Joint Channel Estimation and Generalized Approximate Messaging Passing-Based Equalization for Underwater Acoustic Communications

Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-Varying Channels

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