Implementation of Deep Learning-based Automatic Modulation Classifier on FPGA SDR Platform

Electronics

Si-Min Li¹  Li-Juan Yu¹  Zhi-Ling Tang¹ 

[1] Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing, Guilin University of Electronic Technology, Guilin 541004, China;

关键词: wireless communication;    signal recognition;    cognitive radio;    neural networks;    reconfigurable hardware;   

DOI  :  10.3390/electronics7070122

来源: DOAJ

【 摘 要 】

Intelligent radios collect information by sensing signals within the radio spectrum, and the automatic modulation recognition (AMR) of signals is one of their most challenging tasks. Although the result of a modulation classification based on a deep neural network is better, the training of the neural network requires complicated calculations and expensive hardware. Therefore, in this paper, we propose a master–slave AMR architecture using the reconfigurability of field-programmable gate arrays (FPGAs). First, we discuss the method of building AMR, by using a stack convolution autoencoder (CAE), and analyze the principles of training and classification. Then, on the basis of the radiofrequency network-on-chip architecture, the constraint conditions of AMR in FPGA are proposed from the aspects of computing optimization and memory access optimization. The experimental results not only demonstrated that AMR-based CAEs worked correctly, but also showed that AMR based on neural networks could be implemented on FPGAs, with the potential for dynamic spectrum allocation and cognitive radio systems.

【 授权许可】

Unknown