Exploiting joint sparsity for underwater acoustic MIMO communications

Zhou, Yuehai; Jiang, Weihua; Tong, Feng; Zhang, Gangqiang

doi:10.1016/j.apacoust.2016.10.010

Cited by 24 publications

(17 citation statements)

References 11 publications

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“…where r e = [r e 1 , r e 2 , • • • , r e N s ] T . From (11) to (20), it is an inner iterative equalization, in order to reduce the computational amount of GAMP equalization, we incorporate the inner iteration into outer iteration between the GAMP equalizer and the BCJR decoder, that is, the inner iteration and the outer iteration are combined into one iteration. The computational complexity of GAMP equalization is only in the logarithmic order per symbol.…”

Section: A Channel Estimation Based On the St Schemementioning

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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Section: A Channel Estimation Based On the St Schemementioning

confidence: 99%

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

et al. 2021

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“…Þ and m is chosen to satisfy the total power constraint defined by P i P Ã i = P. Note that each l i corresponds to an eigenmode of the channel, and each non-zero eigenchannel can support a data stream; thus, the MIMO channel can support the spatial multiplexing of minðn; mÞ multiple streams. Different MIMO systems have been developed to increase the capacity underwater; [11][12][13] here also, because the CSI at the transmitter is not available, and as such, the capacity improvement is sub-optimal.…”

Section: Mimo For Underwater Acoustic Communicationmentioning

confidence: 99%

“…12 More recently, the MIMO channel has been estimated using a joint sparse recovery method at the receiver. 13 Due to the variable underwater channel conditions, software-defined acoustic modems (SDAMs) have been receiving increased interest. 14 In fact, most commercial modems describe a high-throughput mode relying on spectrally efficient coherent modulation techniques, as well as an alternative mode relying on a robust low-bit rate FSK modulation.…”

Section: Introductionmentioning

confidence: 99%

A Janus compatible software-defined underwater acoustic multiple-input multiple-output modem

Singh

Crispo

Bousquet

et al. 2021

International Journal of Distributed Sensor Networks

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This article describes the implementation of a multiple-input multiple-output acoustic communication link in shallow water conditions to enable a software-defined acoustic modem with a maximum transmission rate of 20 kbps in a 5-kHz bandwidth. The reliability improvement of a low-complexity Alamouti space–time block code is evaluated to improve the diversity in a high-rate transmission mode using single carrier modulation, as well as in a low-rate transmission mode relying on continuous-phase frequency-shift keying. Using measurements in realistic subsea conditions, the effect of the spatial channel correlation is demonstrated. It is found that for the space–time block code/continuous-phase frequency-shift keying, the spatial diversity is significantly degraded due to the high spatial correlation. In contrast, for the high-mode transmission rate, space–time block code with single carrier modulation offers a bit error rate improvement by a factor over hundred, in comparison to a single transmit element, demonstrating that the multiple-input multiple-output optimal code depends on the software-defined acoustic modem transmission mode.

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“…Similarly, [31] and [32] proposed a successive interference cancellation combined with Turbo equalization. Relying on the sparseness of the underwater acoustic channel's impulse response, in [33], we proposed a distributed compressed sensing method to suppress the interference during channel estimation.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Decoding Approach for Mitigating Near–Far Interference in Internet of Underwater Things

Zhou

Diamant

2020

IEEE Internet Things J.

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With the massive development of underwater small robotic vehicles and matching acoustic modems, applications for Internet of Underwater Things (IoUT) are emerging. IoUT involves communication between non-synchronized network nodes organized in a mesh. A limiting factor of such communication is the so-called near-far effect, where transmissions from a node (near) close to a common receiver blocks the transmissions of a farther node (far). Due to the high-power attenuation in the underwater acoustic channel, near-far is common in underwater acoustic communication networks, and the phenomena occurs even for a distance ratio of 80% between the near and far nodes to the receiver, and the large number of nodes in IoUT compounds the effect of this phenomena. While current approaches only consider the jamming effect to the far signal, in this paper, we consider cancelling the interference from both sources by estimating and equalizing the channels on parallel, thereby significantly improving the decoding of both signals. As a result, the IoUT can function much better. To limit mutual interference, we propose an automatic switching mechanism that controls the cancellation operation both in channel estimation and channel equalization. Simulation results show that our approach obtains significant improvement for communication from both near and far nodes. Results from a designated sea trial demonstrate that when both nodes are affected by their mutual transmissions, our proposed method improves the output signal-to-noise ratio (SNR) significantly.

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Exploiting joint sparsity for underwater acoustic MIMO communications

Cited by 24 publications

References 11 publications

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

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

A Janus compatible software-defined underwater acoustic multiple-input multiple-output modem

A Parallel Decoding Approach for Mitigating Near–Far Interference in Internet of Underwater Things

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