Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
arxiv(2024)
摘要
Automatic modulation recognition (AMR) is critical for determining the
modulation type of incoming signals. Integrating advanced deep learning
approaches enables rapid processing and minimal resource usage, essential for
IoT applications. We have introduced a novel method using Transformer networks
for efficient AMR, designed specifically to address the constraints on model
size prevalent in IoT environments. Our extensive experiments reveal that our
proposed method outperformed advanced deep learning techniques, achieving the
highest recognition accuracy.
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