Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder

• Published in: IEEE transactions on wireless communications Vol. 21; no. 1; pp. 370 - 382
• Main Authors: Ke, Ziqi, Vikalo, Haris
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Coders; Computational modeling; Computer architecture; denoising auto-encoder; Emitters; Feature extraction; Low cost; LSTM; Modulation; Modulation/technology classification; Neural networks; Noise reduction; Phase modulation; Radio frequency interference; Radio signals; Real time; Signal classification; Signal monitoring; Spectrum allocation; Task analysis; Wireless communication; Wireless sensor networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2021.3095855

Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation. They are rendered difficult due to a growing number of emitter types and varied effects of real-world channels upon the radio signal. Existing spectrum monitoring techniques are capable of acquiring massive amounts of radio and real-time spectrum data using compact sensors deployed in a variety of settings. However, state-of-the-art methods that use such data to classify emitter types and detect communication schemes struggle to achieve required levels of accuracy at a computational efficiency that would allow their implementation on low-cost computational platforms. In this paper, we present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals, and infer modulation or technology type using the learned features. The algorithm utilizes a compact neural network architecture readily implemented on a low-cost computational platform while exceeding state-of-the-art accuracy. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals, often demonstrating superior performance compared to state-of-the-art methods. Source codes are available at https://github.com/WuLoli/LSTMDAE .