Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96332
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | en_US |
dc.creator | Ding, C | en_US |
dc.creator | Ho, IWH | en_US |
dc.date.accessioned | 2022-11-18T06:50:31Z | - |
dc.date.available | 2022-11-18T06:50:31Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/96332 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 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. | en_US |
dc.rights | The following publication C. Ding and I. W. -H. Ho, "Digital-Twin-Enabled City-Model-Aware Deep Learning for Dynamic Channel Estimation in Urban Vehicular Environments," in IEEE Transactions on Green Communications and Networking, vol. 6, no. 3, pp. 1604-1612, Sept. 2022 is available at https://dx.doi.org/10.1109/TGCN.2022.3173414. | en_US |
dc.subject | Digital twin | en_US |
dc.subject | Vehicular networks | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Channel estimation | en_US |
dc.title | Digital-twin-enabled city-model-aware deep learning for dynamic channel estimation in urban vehicular environments | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1604 | en_US |
dc.identifier.epage | 1612 | en_US |
dc.identifier.volume | 6 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1109/TGCN.2022.3173414 | en_US |
dcterms.abstract | Most of the existing works on vehicle-to-everything (V2X) communications assume some deterministic or stochastic channel models, which is unrealistic for highly-dynamic vehicular channels in urban environments under the influence of high-speed vehicle motion, intermittent connectivity, and signal attenuation in urban canyon. Enabled by the concept of digital twin, the digital replica of a real-world physical system, this paper proposes a city-model-aware deep learning algorithm for dynamic channel estimation in urban vehicular environments. Specifically, the digital twin simulation allows us to accurately model radio ray reflection and attenuation in urban canyon, and the data can be supplemented and validated with empirical measurements. Our results indicates that the city-model-aware deep neural network (CMA DNN) estimator performs much better than conventional methods and has more than 32% improvement in BER when compared with generic DNN approaches that do not take the 3D city model into account. Since some geometry-based models like ray-tracing techniques used in the digital twin simulation for dynamic channel modeling could be computational expensive, we also propose a basis expansion model (BEM) approach to simplify the computation load of the overall methodology to gain a good balance between accuracy and timeliness. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE Transactions on green communications and networking, Sept. 2022, v. 6, no. 3, p. 1604-1612 | en_US |
dcterms.isPartOf | IEEE Transactions on green communications and networking | en_US |
dcterms.issued | 2022-09 | - |
dc.identifier.scopus | 2-s2.0-85132520857 | - |
dc.identifier.eissn | 2473-2400 | en_US |
dc.description.validate | 202211 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1408 | - |
dc.identifier.SubFormID | 44881 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Ding_Digital-Twin-Enabled_Vehicular_Environments.pdf | Pre-Published version | 17.57 MB | Adobe PDF | View/Open |
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