MAMC—Optimal on Accuracy and Efficiency for Automatic Modulation Classification With Extended Signal Length

作者：Y. Zhang, Zinan Zhou, Yichao Cao, Guangyu Li, Xuanpeng Li · 发表于：IEEE Communications Letters · 年份：2024 · DOI：10.1109/lcomm.2024.3474519 · 被引用次数：22 · 研究领域：Wireless Signal Modulation Classification、Sensor Technology and Measurement Systems

In Automatic Modulation Classification (AMC), extended signal lengths offer a bounty of information, yet impede the model’s adaptability, introduce more noise interference, extend the training and inference time, and increase memory usage. To bridge the gap between these requirements, we propose a novel AMC framework, designated as the Mamba-based Automatic Modulation Classification (MAMC), which addresses the accuracy and efficiency requirements for long-sequence AMC. Specifically, we introduce the Selective State Space Model (Mamba), which enhances the model’s capabilities in long-term memory and information selection, and reduces computational complexity and spatial overhead. We further design a denoising unit to filter out effective semantic information to improve accuracy. Rigorous experimental evaluations on the publicly available dataset RML2016.10 and TorchSig affirm that MAMC delivers superior recognition accuracy while necessitating minimal computational time and memory occupancy. Codes are available onhttps://github.com/ZhangYezhuo/MAMC.