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

• URL: http://arxiv.org/abs/2011.08295v1
• Date: Mon, 16 Nov 2020 21:41:31 GMT
• Title: Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder
• Authors: Ziqi Ke and Haris Vikalo
• Abstract summary: We present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals.
• Score: 29.590446724625693
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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