OFDM system with adaptive modulation for shallow water acoustic channel environment

Huda, Miftahul; Putri, Nisyadea Bachara; Santoso, Tri Budi

doi:10.1109/comnetsat.2017.8263573

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…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). Although these rule-based schemes are convenient to operate, fixed thresholds are not applicable for complex channel fluctuations and varying data transmission requirements, which will result in performance degradation.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cen

Liu

et al. 2021

Sensors

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“…Since the model is learned from the training data with the label according to feature, the proposed AM model is based on the supervised learning. Although, the proposed model can be applied to all supervised learning algorithms, we focus on the application for deep neural network (DNN) [13]. DNN parameterizes the relationship between input and output through the weights between layers.…”

Section: Adaptive Modulation Based On Machine Learning Model In mentioning

confidence: 99%

“…The estimation of optimal transmission mode is based on improving the spectral efficiency while satisfying the pre-defined target error performance. In [11]- [13], AM was implemented by defining the table for use of modulation schemes according to signal to noise ratio (SNR) as channel condition. This AM is referred to as the rule-based AM.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for Adaptive Modulation of Multi-Stream in MIMO-OFDM System

Ha¹,

You²,

Song³

2019

IEEE Access

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In this paper, we propose the adaptive modulation (AM) model based on machine learning for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. Since 5G new radio (NR) system can be used in a large variety of Internet of Things fields than the conventional systems, the AM scheme that adjusts data rate and reliability according to channel condition can be effectively utilized. The conventional AM schemes are implemented by defining modulation schemes to be used according to each channel condition as table in advance. However, since the rule-based AM cannot analyze the communication performance according to the correlation of channels between antennas and the number of transmission modes is exponentially increased according to the number of available modulation schemes and antennas, it is not suitable for 5G NR system. The learning of the proposed AM model is based on the generated training signal by the extracted features from the received signal and assigned label through the performance analysis for signal detection. We focus on the application of deep neural network for AM and cover the precedence method of principal component analysis to improve the performance of the model. The simulations on the classification of optimal transmission mode for the MIMO-OFDM signal demonstrate that the proposed model supports the adaptability according to the condition of complex MIMO channel. INDEX TERMS MIMO-OFDM, adaptive modulation, machine learning, deep neural network, principal component analysis.

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“…However, the transmission of OFDM signals in Underwater channels is challenging because of the dispersive channel nature, where some subcarriers may be subjected to a deep fading. Adaptive transmission may be used to mitigate heavy fading effect and improve the system performance by matching transmission parameters following the channel characteristics and variations [7].…”

Section: Introductionmentioning

confidence: 99%

Channel-Matched OFDM (CM-OFDM) for Enhanced Performance of Multiuser Systems Operating in Underwater Acoustic Channels

El‐Khamy

Ibrahim²

EPiC Series in Computing

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The design of multiuser OFDM underwater acoustic (UWA) communication systems is very challenging due to the time-varying and frequency selective fading of UWA channels. The key for ensuring a reliable optimum transmission for every user is the suitable resource assignment for each. This paper proposes a new adaptive channel- matched OFDM scheme (CM-OFDM) to overcome the performance degradation caused by the frequency selective fading across the OFDM subcarriers. In the proposed scheme, the OFDM subcarriers are sorted depending on their corresponding channel gains. Then the resource assignment to the different users is accomplished according to their required quality of service (QOS). The user with the highest QOS is assigned the best subcarriers and the other users are similarly assigned the remaining subcarriers. This optimized resource assignment technique guarantees enhanced performance with no need of increasing the transmitted power or changing the modulation schemes. The performance of the proposed technique is investigated and is compared with uniform and random subcarriers’ assignment methods used for multiuser OFDM systems. The simulations are performed for a multipath frequency selective UWA channel model. The simulation results clearly show the advantages of CM-OFDM scheme for the users with high QOS on the expense of a slight degradation in the performance for the other user with lower QOS.

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OFDM system with adaptive modulation for shallow water acoustic channel environment

Cited by 8 publications

References 11 publications

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

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

Machine Learning Model for Adaptive Modulation of Multi-Stream in MIMO-OFDM System

Channel-Matched OFDM (CM-OFDM) for Enhanced Performance of Multiuser Systems Operating in Underwater Acoustic Channels

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