MoDANet: Multi-task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25547526%3A_____%2F21%3AN0000004" target="_blank" >RIV/25547526:_____/21:N0000004 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9632584" target="_blank" >https://ieeexplore.ieee.org/document/9632584</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/LCOMM.2021.3132018" target="_blank" >10.1109/LCOMM.2021.3132018</a>
Alternative languages
Result language
angličtina
Original language name
MoDANet: Multi-task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation
Original language description
In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the effect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/TM02000035" target="_blank" >TM02000035: NEO classification of signals (NEOCLASSIG) for radio surveillance systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.
Data specific for result type
Name of the periodical
MoDANet: Multi-Task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation
ISSN
1089-7798
e-ISSN
1558-2558
Volume of the periodical
neuveden
Issue of the periodical within the volume
01 December 2021
Country of publishing house
VN - VIET NAM
Number of pages
5
Pages from-to
335 - 339
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85120580975