MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts
This work addresses a domain-specific problem in wireless communications by improving signal classification accuracy, representing an incremental advance through the novel application of mixture-of-experts techniques to this task.
The paper tackled the problem of uneven performance in automatic modulation classification under varying signal-to-noise ratio conditions by proposing MoE-AMC, a mixture-of-experts model that combines Transformer-based and ResNet-based components, achieving an average accuracy of 71.76% on the RML2018.01a dataset, which is nearly 10% higher than previous state-of-the-art models.
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.