Frequency-Domain Turbo Equalization With Iterative Channel Estimation for MIMO Underwater Acoustic Communications

Chen, Zhenrui; Wang, Jintao; Zheng, Yu

doi:10.1109/joe.2016.2600106

Cited by 40 publications

(16 citation statements)

References 30 publications

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“…For saving of energy in terrestrial wireless sensor networks the channel coding methods are preferred [10]. In [11], wireless sensor networks the authors have determine the trade-off between transmission and consumption of energy.…”

Section: Related Workmentioning

confidence: 99%

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Coding Techniques using IDMA OFDM MIMO for Underwater Wireless Acoustic Communication

Shahapur*,

Khanai,

Torse

2020

IJRTE

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Future Underwater communication demands high speed data transmission. Present Underwater communication can only provide a limited data rate, where the available bandwidth is limited. To increase the data rate, we simulate the MIMO-OFDM with IDMA technique. In MIMO method many transmitter and many receiver antennas are present. MIMO method offer a very high capacity which raises linearly with the numeral of antennas. Data rate increases with MIMO method, bandwidth is saved and fading is reduced with OFDM. In OFDM technique the complete sign orthogonally overlays in a frequency field that secures the bandwidth. IDMA system is constructed on interleaving procedure. In this paper different coding techniques Convolutional, RS code, LDPC code, Turbo code with IDMA OFDM MIMO technique are compared. IDMA OFDM MIMO with Turbo code, BER performance is improved up to 10-6 .

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Section: Related Workmentioning

confidence: 99%

“…For underwater acoustic wireless communication, the attenuation ( , ) is influenced by the frequency over distance , which can be approximated as [10] , = ,…”

Section: B Underwater Propagation Modelmentioning

confidence: 99%

Coding Techniques using IDMA OFDM MIMO for Underwater Wireless Acoustic Communication

Shahapur*,

Khanai,

Torse

2020

IJRTE

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“…The reflection of sound from the ocean surface describes reflection, and surface reflection is affected by the unevenness or evenness of the ocean [9], [10]. Using the Beckmann-Spizzichino surface reflection model the transmission loss on reflection can be calculated when the ocean is uneven, and is given by…”

Section: B Surface Reflectionmentioning

confidence: 99%

“…The total acoustic pathway loss is calculated by using the following equation = . 10 + . 10 ( ) + (4)…”

Section: B Surface Reflectionmentioning

confidence: 99%

Performance Analysis of Underwater Acoustic Communication using IDMA-OFDM-MIMO with Reed Solomon and Turbo Code

Khanai¹,

Shahapur²,

Torse³

2018

IJCNIS

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The underwater acoustic environment is a promising technology which explores the real-time data collection for various applications. However, these channels are prone to errors, and characterized by propagation delay, half duplex communication. At low frequencies, the fading phenomenon extensively affect the behavior of the channel and hence the effect the design of reliable communication system. The underwater acoustic channels to perform appreciably reliable communication, an attempt are made by various modulation and coding techniques. Simulation results for the combination of BPSK modulation with Reed Solomon code (BPSK-RS) having various interleavers Random Interleaver, Matrix Interleaver, have been investigated. To improve the Bit Error Rate performance various modulation techniques such as BPSK, QPSK, and QAM were combined with coding algorithms like RS code, Turbo code and different Interleavers. The investigation of the above combination reveals that IDMA-OFDM-MIMO with BPSK modulation, Turbo code with Random Interleaver technique improves significantly Bit Error Rate performance.

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“…Due to the complexity and instability of underwater acoustic communication systems, it is difficult to identify modulation during actual communication. There are now some methods of underwater acoustic communication that have been used, such as sparse adaptive convolution cores [3–5], time-domain turbo equalization [6, 7], and frequency-domain turbo equalization [8], but these methods still have the problem of high computational complexity and low classification success rate. To recognize modulation, accurately is hence becoming more challenging.…”

Section: Introductionmentioning

confidence: 99%

Modulation Classification of Underwater Communication with Deep Learning Network

Wang

Zhang

Sang

et al. 2019

Computational Intelligence and Neuroscience

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Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

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Frequency-Domain Turbo Equalization With Iterative Channel Estimation for MIMO Underwater Acoustic Communications

Cited by 40 publications

References 30 publications

Coding Techniques using IDMA OFDM MIMO for Underwater Wireless Acoustic Communication

Coding Techniques using IDMA OFDM MIMO for Underwater Wireless Acoustic Communication

Performance Analysis of Underwater Acoustic Communication using IDMA-OFDM-MIMO with Reed Solomon and Turbo Code

Modulation Classification of Underwater Communication with Deep Learning Network

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