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

TL;DR

This paper introduces an LSTM auto-encoder framework that automatically extracts features from noisy radio signals to classify communication technology and modulation schemes with high accuracy and computational efficiency.

Contribution

It presents a novel LSTM denoising auto-encoder architecture that outperforms existing methods in radio signal classification while being suitable for low-cost platforms.

Findings

Achieves higher classification accuracy than state-of-the-art methods.

Operates efficiently on low-cost computational platforms.

Successfully classifies real-world and synthetic radio signals.

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.