CSI feedback-based CS for underwater acoustic adaptive modulation OFDM system with channel prediction

Kuai, Xiaoyan; Sun, Haixin; Qi, Jie; Cheng, En; Xu, Xiaoka; Guo, Yuhui; Chen, Yougan

doi:10.1007/s13344-014-0031-6

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…In [33], the authors proposed a sparse channel parameter feedback method based on CS theory in adaptive modulation OFDM systems. Subsequently, the predicted channels were transformed into the frequency domain, where the received signal-to-noise ratio (SNR) for each subcarrier could be obtained, and the appropriate modulation scheme could be selected.…”

Section: Algorithm 2 Mmse Prediction Algorithmmentioning

confidence: 99%

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Liu,

Ma,

Wang

et al. 2024

Remote Sensing

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Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.

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Section: Algorithm 2 Mmse Prediction Algorithmmentioning

confidence: 99%

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Liu,

Ma,

Wang

et al. 2024

Remote Sensing

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“…For underwater channel state forecast, linear prediction models, such as statistical analysis [ 23 ] and exponential moving average (EMA) [ 24 ], were utilized, as well. In Reference [ 11 ], auto-regression was used every several symbols for the adaptive modulation of underwater communication. Zhang et al [ 25 ] proposed an adaptive channel prediction scheme based on the exponential weighted recursive least square (EWRLS) algorithm, which used current and past estimated channel parameters in the delay-Doppler domain.…”

Section: Related Workmentioning

confidence: 99%

“…Different modulation methods were used in Reference [ 10 ] for opportunistic cooperative transmission of underwater networks under various environmental conditions to achieve the best compromise between robustness and data rate. Kuai et al [ 11 ] proposed a fixed threshold adaption algorithm and the required SNR of different modulation methods were presented to meet the target bit error rate (BER). An adaptive OFDM transmission system was proposed in Reference [ 12 ] based on the SNR while maintaining a certain bit error rate (BER).…”

Section: Related Workmentioning

confidence: 99%

“…

represents the i -th coefficient of AR prediction model and and

is a noise term. The coefficients of the AR model can be calculated by the least squares algorithm [ 5 , 11 , 25 ].…”

Section: Double-scale Channel State Predictionmentioning

confidence: 99%

“…In the literature, adaptive transmission according to channel state has become a hot topic. The rule-based adaptive modulation and coding (AMC) methods usually utilize fixed thresholds for transmission mode determination [ 1 , 10 , 11 , 12 ]. These methods are easy for implementation and could be used in the resource-constrained networks, while the threshold selection should be carefully designed.…”

Section: Introductionmentioning

confidence: 99%

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Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

Cen

Liu

et al. 2021

Sensors

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The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.

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Adaptivity at the Physical Layer

Dol

Blom

Walree

et al. 2020

SpringerBriefs in Electrical and Computer Engineering

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CSI feedback-based CS for underwater acoustic adaptive modulation OFDM system with channel prediction

Cited by 4 publications

References 7 publications

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

Adaptivity at the Physical Layer

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