Statistical Modeling of Evaporation Duct Channel for Maritime Broadband Communications

Zhang, Hanzhong; Zhou, Ting; Xu, Tianheng; Wang, Yuzhen; Hu, Honglin

doi:10.1109/tvt.2022.3187753

Cited by 13 publications

(8 citation statements)

References 50 publications

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“…Thus, it was important to predict the duct conditions in real time. Such predictions can be used in various fields, such as radar systems, military operations, aviation safety, and wireless communications [29][30][31][32][33]. In the duct condition, electromagnetic waves from a radar were trapped along the Earth's surface, which resulted in the radar receiving strong clutter power from the sea surface or terrain.…”

Section: Calculation Of Clutter Power Spectrumsmentioning

confidence: 99%

Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

Jin,

Cho,

Jang

et al. 2024

Remote Sensing

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This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921.

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Section: Calculation Of Clutter Power Spectrumsmentioning

confidence: 99%

Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

Jin,

Cho,

Jang

et al. 2024

Remote Sensing

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“…In contrast to the homogeneous assumptions in [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ], the duct layers are range-dependent. The strong spatial–temporal variability of the lower atmosphere in near-shore areas can cause horizontal variability in atmospheric ducts.…”

Section: Introductionmentioning

confidence: 96%

“…Using the propagation loss obtained from PETOOL, a vertically separated MIMO ship-to-ship communication system was proposed [ 33 ]. In [ 34 ], the authors proposed a large-scale path loss model for the ducting channel. For rough sea surface conditions, an improved parabolic equation method was proposed based on a pade approximation form [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

A Sliced Parabolic Equation Method to Characterize Maritime Radio Propagation

Wang

Zhou

et al. 2023

Sensors

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For maritime broadband communications, atmospheric ducts can enable beyond line-of-sight communications or cause severe interference. Due to the strong spatial–temporal variability of atmospheric conditions in near-shore areas, atmospheric ducts have inherent spatial heterogeneity and suddenness. This paper aims to evaluate the effect of horizontally inhomogeneous ducts on maritime radio propagation through theoretical analysis and measurement validation. To make better use of meteorological reanalysis data, we design a range-dependent atmospheric duct model. Then, a sliced parabolic equation algorithm is proposed to improve the prediction accuracy of path loss. We derive the corresponding numerical solution and analyze the feasibility of the proposed algorithm under the range-dependent duct conditions. A 3.5 GHz long-distance radio propagation measurement is utilized to verify the algorithm. The spatial distribution characteristics of atmospheric ducts in the measurements are analyzed. Based on actual duct conditions, the simulation results are consistent with the measured path loss. The proposed algorithm outperforms the existing method during the multiple duct periods. We further investigate the influence of different duct horizontal characteristics on the received signal strength.

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“…It is expected that society will step into a new era of intelligent 6G in 2030 [2]. The visions of 6G include further enhanced mobile broadband, massive machine-type communication, ultra-massive machine-type communication, long-distance and high-mobility communications, and extremely low-power communications [3,4]. It is anticipated that 6G will achieve deep integration between intelligent applications and networks to enable appealing use cases such as virtual reality and environment sensing based on artificial intelligence and the Internet of Things (IoT) [5].…”

Section: Introductionmentioning

confidence: 99%

ISAC towards 6G Satellite–Terrestrial Communications: Principles, Status, and Prospects

Gu,

Xu,

Feng

et al. 2024

Electronics

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With the evolution of fifth-generation (5G) to sixth-generation (6G) communication systems, the utilization of spectrum resources faces incremental challenges. Integrated sensing and communication (ISAC) technology, as a crucial element in 6G technology, is expected to enhance energy efficiency and spectrum utilization efficiency by integrating radar and communication signals, achieving environmental awareness, and enabling scene interconnection. Simultaneously, to realize the vision of seamless coverage in 6G, research on integrated satellite-terrestrial communication has been prioritized. To integrate the advantages, ISAC for integrated satellite–terrestrial networks (ISTNs) in 6G has emerged as a potential research direction. This paper offers an extensive overview of the present state of key technologies for ISAC and the development of ISTNs. Meanwhile, with a focus on the ISTN-oriented 6G ISAC system, several hotspot topics, including future application scenarios and key technological developments, are outlined and demonstrated.

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Statistical Modeling of Evaporation Duct Channel for Maritime Broadband Communications

Cited by 13 publications

References 50 publications

Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

A Sliced Parabolic Equation Method to Characterize Maritime Radio Propagation

ISAC towards 6G Satellite–Terrestrial Communications: Principles, Status, and Prospects

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