MR-Transformer: FPGA Accelerated Deep Learning Attention Model for Modulation Recognition

Modulation recognition has emerged as an intensive research topic to improve the communication efficiency in the future 6G network and plays an important role in the security of electromagnetic spectrum. Various pattern recognition methods have been proposed to enhance the performance of modulation recognition, especially deep learning models showing their emerging performance. In this work, we design a modulation recognition model based on an enhanced Transformer, namely MR-Transformer, which is accelerated on a Field Programmable Gate Array (FPGA). The design of MR-Transformer targets on high recognition accuracy, low power consumption, and high computation efficiency, which is suitable for modulation recognition at edge devices. MR-Transformer leverages attention mechanism to extract global features and correspondingly enhance the recognition accuracy. An improved matrix multiplication operation and enhanced Design Space Exploration (DSE) method are proposed in MR-Transformer to improve the computation efficiency and reduce resource consumption. We conduct comprehensive experiments to evaluate the performance of MR-Transformer on three platforms, i.e. Central Processing Unit (CPU), Graphics Processing Unit (GPU), and FPGA, based on two open-source datasets. According to the evaluation results, the MR-Transformer based on FPGA shows the best performance compared with the baseline models considering accuracy, power consumption, and computation efficiency.