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Signal estimation in cognitive satellite networks for satellite-based industrial internet of things
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Liu, Mingqian, Qu, Nan, Tang, Jie, Chen, Yunfei, Song, Hao and Gong, Fengkui (2021) Signal estimation in cognitive satellite networks for satellite-based industrial internet of things. IEEE Transactions on Industrial Informatics, 17 (3). pp. 2062-2071. doi:10.1109/TII.2020.2983390
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WRAP-signal-estimation-cognitive-satellite-networks-satellite-based-industrial-internet-things-Chen-2020.pdf - Accepted Version - Requires a PDF viewer. Download (851Kb) | Preview |
Official URL: https://doi.org/10.1109/TII.2020.2983390
Abstract
Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved M 2 M ∞ method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT.
| Item Type: | Journal Article | ||||||||||||||||||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||||||||||||||||||
| Divisions: | Faculty of Science > Engineering | ||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Internet of things -- Industrial applications, Cognitive radio networks, Artificial satellites in telecommunication -- Systems engineering | ||||||||||||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||||||||||||||||||
| Publisher: | IEEE | ||||||||||||||||||||||||
| ISSN: | 1551-3203 | ||||||||||||||||||||||||
| Official Date: | March 2021 | ||||||||||||||||||||||||
| Dates: |
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| Volume: | 17 | ||||||||||||||||||||||||
| Number: | 3 | ||||||||||||||||||||||||
| Page Range: | pp. 2062-2071 | ||||||||||||||||||||||||
| DOI: | 10.1109/TII.2020.2983390 | ||||||||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||||||||
| Publisher Statement: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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