关键词: 信号自动调制识别, Transformer, 多头自注意力机制, 硬件加速, 边缘计算

Abstract: With the rapid development and widespread adoption of technologies such as the Internet of Things (IoT),5G communications,wireless ad hoc networks,and unmanned swarm systems,automatic modulation recognition (AMR) has found extensive applications in wireless communications,radar signal processing,electronic warfare,and other domains,while progressively penetrating into edge intelligent terminal devices.Consequently,the development of light-weight intelligent modulation recognition algorithms and their implementation has emerged as one of the critical challenges to be addressed in the field of communications.Traditional signal modulation recognition algorithm models based on CNN and RNN fail to accurately capture the global characteristics of signals,thus exhibiting certain limitations in AMR tasks.In recent years,the Transformer technology,leveraging the global feature extraction capability of its built-in multi-head self-attention mechanism,has broken through the generalization constraints of DNN models and achieved significant breakthroughs in timeseries information processing.To address these challenges,this paper proposes an AMR algorithm model based on the Transformer structure.The model embeds a CNN-based Tokenization module into the Transformer,enabling it to combine the global information extraction ability of the Transformer and retain the local time series features inside the Token,thereby ensuring the recognition accuracy of the algorithm.At the same time,due to the small number of parameters of the model,it is suitable for deployment on edge device terminals.Evaluation results on the Zynq Ultrascale+MPSoC platform demonstrate that,compared to the software implementation running on a higher-frequency CPU platform,the FPGA-based hardware acceleration solution achieves a significant speedup of up to 2.47× while operating at a lower clock frequency.

Key words: automatic modulation recognition, Transformer, multi-head self-attention mechanism, hardware acceleration, edge computing