Iterative sparse channel estimation and decoding for underwater MIMO-OFDM

Huang, Biao; Berger,; Zhou, Quan; Willett,

doi:10.23919/oceans.2009.5422203

Cited by 7 publications

(7 citation statements)

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“…The constraint condition can be described that, all the channels contain S multipath arrivals with common delays, while the sparsity of each channel may be different. The SOMP algorithm can be directly utilized to solve (9). The key idea of the SOMP algorithm is shown in ( 10), the candidate is measured via multiple data blocks, while in the traditional OMP algorithm, the candidate is measured by only one data block.…”

Section: A Distributed Compressed Sensingmentioning

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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Section: A Distributed Compressed Sensingmentioning

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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“…In [20], pilot-aided channel estimation and iterative equalization are associated with two-step Doppler compensation to remove phase rotation providing a spectral efficiency up to 3.5 bit/s/Hz. In [21] the authors suggest including in the iterative loop a channel estimation algorithm exploiting the sparse structure of the UWA channel to improve performance and reduce pilot overhead, a spectral June 12, 2019 DRAFT efficiency around 5.3 bit/s/Hz is approached with 3 transmitted stream and 64-QAM constellation.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

On the Achievable Rate of Multiple-Input–Multiple-Output Underwater Acoustic Communications

Bouvet¹,

Auffret²

2020

IEEE J. Oceanic Eng.

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This paper investigates the data gain brought by Multiple Input Multiple Output (MIMO) principle for Underwater Acoustic (UWA) communication. By considering a class of acoustic MIMO channel where each fading coefficient is modelled by a Rice distribution law with correlation factor depending on channel Doppler spread, we derive the achievable rate of such system that takes into account both channel estimation error and training sequence overhead. Model parameters are then estimated from an experiment campaign in MIMO shallow water channel conducted in the roadstead of Brest, France.The system achievable rate is evaluated for several MIMO architectures and channel configurations and then compared against conventional Single Input Multiple Output (SIMO) transmission. The achievable rate gain is finally put in perspective with end-to-end data rate performance of a single carrier MIMO transmission system experimented at sea.

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“…The traditional LS based channel estimation algorithms are greatly influenced by additive noise and inter carrier interference (ICI). Moreover, there is the noise enhancement problem, so the LS based channel estimation is not appropriate for sparse channels [40,41,42,43,44].…”

Section: Introductionmentioning

confidence: 99%

“…When the signal is sparse in a certain domain [36,37,38], one can use fewer sampling points to accurately reconstruct the sparse signal. Due to this advantage, the CS is widely used for the sparse UWA channel estimation [40,41,42,43]. Multiple sparse recovery algorithms have been recently proposed.…”

Section: Introductionmentioning

confidence: 99%

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

Zhang

Zakharov

et al. 2019

Applied Acoustics

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In this paper, we propose a computationally efficient multipath matching pursuit (MMP) channel estimation algorithm for underwater acoustic (UWA) transform domain communication systems (TDCSs). The algorithm, referred to as the MMP-DCD-CV algorithm, is based on the dichotomous coordinate descent (DCD) iterations and cross validation (CV). The MMP-DCD-CV sparse channel estimator in each iteration searches for multiple promising path candidates most relevant to a residual vector and chooses the best candidate. The DCD iterations are used to solve the corresponding least squares problem with low complexity and numerical stability. The CV provides a stopping criterion of the algorithm without a priori information on the channel sparsity and noise level and examines whether the algorithm overfits its data, thus improving the estimation accuracy. The performance of the proposed algorithm is evaluated under simulated sparse UWA channels. The numerical results show that the algorithm achieves better performance than the original MMP algorithm, has lower complexity, and does not require prior knowledge on the channel sparsity and noise level. We also propose an UWA TDCS with sparse channel estimation based on the proposed MMP-DCD-CV algorithm. The proposed UWA communication system is tested by the Waymark simulator, providing the virtual signal transmission in the UWA channel, with a measured Sound Speed Profile and bathymetry. Numerical results demonstrate that the UWA TDCS with the proposed sparse channel estimator offers considerable improvement in system performance compared to other TDCS schemes.

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Iterative sparse channel estimation and decoding for underwater MIMO-OFDM

Cited by 7 publications

References 0 publications

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

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

On the Achievable Rate of Multiple-Input–Multiple-Output Underwater Acoustic Communications

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

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