Lightweight Automatic Modulation Classification Based on Decentralized Learning

Xue Fu, Guan Gui, Yu Wang, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin, Fumiyuki Adachi

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the implementation and performance limitations of centralized learning automatic modulation classification (CentAMC) method, this paper proposes a decentralized learning AMC (DecentAMC) method using model aggregation and lightweight design. Specifically, the model aggregation is realized by a central device (CD) for edge device (ED) model aggregation and multiple EDs for ED model training. The lightweight is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of fully connected layers are cut off. Simulation results show that the proposed method substantially reduces the storage and computational capacity requirements of the EDs and communication overhead. The training efficiency also shows remarkable improvement. Compared with convolution neural network (CNN), the space complexity (i.e., model parameters and output feature map) is decreased by about 94% and the time complexity (i.e., floating point operations) of S-CNN is decreased by about 96% while degrading the average correct classification probability by less than 1%. Compared with S-CNN-based CentAMC, without considering model weights uploading and downloading, the training efficiency of our proposed method is about N times of it, where N is the number of EDs. Considering the model weights uploading and downloading, the training efficiency of our proposed method can still be maintained at a high level (e.g., when the number of EDs is 12, the training efficency of the proposed AMC method is about 4 times that of S-CNN-based CentAMC in dataset D1=2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM and about 5 times that of S-CNN-based CentAMC in dataset D2=2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, PAM2, PAM4, PAM8, 16QAM), while the communication overhead is reduced more than 35%.

Original languageEnglish
JournalIEEE Transactions on Cognitive Communications and Networking
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Adaptation models
  • Automatic modulation classification (AMC)
  • centralized learning
  • Computational modeling
  • Convolution
  • convolutional neural network (CNN)
  • decentralized learning.
  • Feature extraction
  • Mathematical model
  • Modulation
  • Training

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Lightweight Automatic Modulation Classification Based on Decentralized Learning'. Together they form a unique fingerprint.

Cite this