Institute for Communication Technologies and Embedded Systems

Automatic Modulation Classification Based on Decentralized Learning and Ensemble Learning

Authors:
Fu, X. ,  Wang, Y. ,  Gacanin, H. ,  Adachi, F.
Journal:
IEEE Transactions on Vehicular Technology
Volume:
71
Page(s):
7942-7946
Date:
Jul. 2022
DOI:
10.1109/TVT.2022.3164935
hsb:
RWTH-2022-07088
Language:
English
Abstract:
To deal with the deep learning-based automatic modulation classification (AMC) in the scenario that the training dataset are distributed over a network without gathering the data at a centralized location, the decentralized learning-based AMC (DecentAMC) had been presented. However, there exists frequent model parameter uploading and downloading in DecentAMC method, which cause high communication overhead. In this paper, an innovative learning framework are proposed for AMC (named DeEnAMC), in which the framework is realized by utilizing the combination of decentralized learning and ensemble learning. Our results show that the proposed DeEnAMC reduces communication overhead while keeping a similar classification performance to DecentAMC.
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