MoE-AMC: Enhancing Automatic Modulation Classification Performance Using
  Mixture-of-Experts
Jiaxin Gao Qinglong Cao Yuntian Chen
Automatic Modulation Classification (AMC) plays a vital role in time series
analysis, such as signal classification and identification within wireless
communications. Deep learning-based AMC models have demonstrated significant
potential in this domain. However, current AMC models inadequately consider the
disparities in handling signals under conditions of low and high
Signal-to-Noise Ratio (SNR), resulting in an unevenness in their performance.
In this study, we propose MoE-AMC, a novel Mixture-of-Experts (MoE) based model
specifically crafted to address AMC in a well-balanced manner across varying
SNR conditions. Utilizing the MoE framework, MoE-AMC seamlessly combines the
strengths of LSRM (a Transformer-based model) for handling low SNR signals and
HSRM (a ResNet-based model) for high SNR signals. This integration empowers
MoE-AMC to achieve leading performance in modulation classification, showcasing
its efficacy in capturing distinctive signal features under diverse SNR
scenarios. We conducted experiments using the RML2018.01a dataset, where
MoE-AMC achieved an average classification accuracy of 71.76% across different
SNR levels, surpassing the performance of previous SOTA models by nearly 10%.
This study represents a pioneering application of MoE techniques in the realm
of AMC, offering a promising avenue for elevating signal classification
accuracy within wireless communication systems.