Deep-Learning-Based Carrier Frequency Offset Estimation and Its Cross-Evaluation in Multiple-Channel Models

Wang, Zhenyi; Wei, Shengyun; Zou, Li; Liao, Feifan; Lang, Weimin; Li, Yuanzhuo

doi:10.3390/info14020098

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…The channel is disturbed due to underground noise, interference, and malicious interactions. The rate of channel interruptions is not common for surface transmission with widely varying uncertainty rates [53][54][55].…”

Section: Discussionmentioning

confidence: 99%

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Rajasoundaran

Stanislaus

Ramasamy³

et al. 2023

Energies

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Wireless Underground Sensor Networks (WUGSNs) transmit data collected from underground objects such as water substances, oil substances, soil contents, and others. In addition, the underground sensor nodes transmit the data to the surface nodes regarding underground irregularities, earthquake, landslides, military border surveillance, and other issues. The channel difficulties of WUGSNs create uncertain communication barriers. Recent research works have proposed different types of channel assessment techniques and security approaches. Moreover, the existing techniques are inadequate to learn the real-time channel attributes in order to build reactive data transmission models. The proposed system implements Deep Learning-based Multi-Channel Learning and Protection Model (DMCAP) using the optimal set of channel attribute classification techniques. The proposed model uses Multi-Channel Ensemble Model, Ensemble Multi-Layer Perceptron (EMLP) Classifiers, Nonlinear Channel Regression models and Nonlinear Entropy Analysis Model, and Ensemble Nonlinear Support Vector Machine (ENLSVM) for evaluating the channel conditions. Additionally, Variable Generative Adversarial Network (VGAN) engine makes the intrusion detection routines under distributed environment. According to the proposed principles, WUGSN channels are classified based on the characteristics such as underground acoustic channels, underground to surface channels and surface to ground station channels. On the classified channel behaviors, EMLP and ENLSVM are operated to extract the Signal to Noise Interference Ratio (SNIR) and channel entropy distortions of multiple channels. Furthermore, the nonlinear regression model was trained for understanding and predicting the link (channel behaviors). The proposed DMCAP has extreme difficulty finding the differences of impacts due to channel issues and malicious attacks. In this regard, the VGAN-Intrusion Detection System (VGAN-IDS) model was configured in the sensor nodes to monitor the channel instabilities against malicious nodes. Thus, the proposed system deeply analyzes multi-channel attribute qualities to improve throughput in uncertain WUGSN. The testbed was created for classified channel parameters (acoustic and air) with uncertain network parameters; the uncertainties of testbed are considered as link failures, noise distortions, interference, node failures, and number of retransmissions. Consequently, the experimental results show that DMCAP attains 10% to 15% of better performance than existing systems through better throughput, minimum retransmission rate, minimum delay, and minimum energy consumption rate. The existing techniques such as Support Vector Machine (SVM) and Random Forest (RF)-based Classification (SMC), Optimal Energy-Efficient Transmission (OETN), and channel-aware multi-path routing principles using Reinforcement Learning model (CRLR) are identified as suitable for the proposed experiments.

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Section: Discussionmentioning

confidence: 99%

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Rajasoundaran

Stanislaus

Ramasamy³

et al. 2023

Energies

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“…In 2023 Wang, Z., et al, [23] provide an OFDM system built using carrier frequency offset (CFO) models based on DL. Peer evaluations show that DL-based models can work well for large classes of channels without further training if they are trained with the worst (heaviest) multi-pass channel model.…”

Section: Literature Reviewmentioning

confidence: 99%

Hexagonal Convolutional Neural Network for Noma Rician Channel Estimator using Hexagonal Quadrature Amplitude Modulation

2024

Informacije MIDEM

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NOMA techniques have attracted much attention due to their ability to support massive connectivity, heterogeneous data traffic, and ultra-low latency requirements, making them ideal for next-generation wireless communication networks. In this paper proposed a novel Hexa CNN for NOMA Rician channel estimator using Hexagonal Quadrature Amplitude Modulation for signal detection and channel estimation (Hexa-QAM). Conventional OFDM-NOMA with HQAM is utilized at the transmitter (tx) side as pilot symbols which inserted to OFDM-NOMA signals to employs the channel estimation (CE) and signal detection advantages of OFDM. Three pilot insertion types Comp, Block, and Hexa were used in the proposed model. The proposed Hexa-QAM can detect the symbols for all users without additional operations based on pilot responses and data signals. However, Hexa CNN is used at the receiver to accomplish a joint flexible signal detection. The Hexagonal Quadrature Amplitude Modulation (Hexa CNN) achieves much better error performance than similar detectors. The Energy Efficiency of the proposed Hexa-QAM technique is 17%, 18%, 23.2%, 23% and 28% better than existing techniques. The accuracy of the proposed technique can be as high as 99.95%, while that of traditional models like the OMA-NOMA, MRC, PD-NOMA and UR-NOMA is 84.9%, 87.58%, and 93.91%, respectively.

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TrsNet: A TRS-based deep learning network for carrier frequency offset estimation in 5G system

Li,

Wang,

Zhao

et al. 2024

Telecommun Syst

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Deep-Learning-Based Carrier Frequency Offset Estimation and Its Cross-Evaluation in Multiple-Channel Models

Cited by 6 publications

References 26 publications

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Hexagonal Convolutional Neural Network for Noma Rician Channel Estimator using Hexagonal Quadrature Amplitude Modulation

TrsNet: A TRS-based deep learning network for carrier frequency offset estimation in 5G system

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