MoDANet: Multi-task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

MoDANet: Multi-task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

MoDANet: Multi-task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/TM02000035" target="_blank" >TM02000035: Pokroková klasifikace signálů (NEOCLASSIG) pro radio-průzkumné systémy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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.

Název periodika

MoDANet: Multi-Task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation

ISSN

1089-7798

e-ISSN

1558-2558

Svazek periodika

neuveden

Číslo periodika v rámci svazku

01 December 2021

Stát vydavatele periodika

VN - Vietnamská socialistická republika

Počet stran výsledku

5

Strana od-do

335 - 339

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85120580975