2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPCOM-8.5 | ||
| Paper Title | Real-Time Radio Modulation Classification with an LSTM Auto-Encoder | ||
| Authors | Ziqi Ke, Haris Vikalo, University of Texas at Austin, United States | ||
| Session | SPCOM-8: Deep learning for communications | ||
| Location | Gather.Town | ||
| Session Time: | Friday, 11 June, 14:00 - 14:45 | ||
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 | ||
| Presentation | Poster | ||
| Topic | Signal Processing for Communications and Networking: [SPC-ML] Machine Learning for Communications | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Identifying modulation type of a received radio signal is a challenging problem encountered in many applications including radio interference mitigation and spectrum allocation. This problem is rendered challenging by the existence of a large number of modulation schemes and numerous sources of interference. Existing methods for monitoring spectrum readily collect large amounts of radio signals. However, existing state-of-the-art approaches to modulation classification struggle to reach desired levels of accuracy with computational efficiency practically feasible for implementation on low-cost computational platforms. To this end, we propose a learning framework based on an LSTM denoising auto-encoder designed to extract robust and stable features from the noisy received signals, and detect the underlying modulation scheme. The method uses a compact architecture that may be implemented on low-cost computational devices while achieving or exceeding state-of-the-art classification accuracy. Experimental results on realistic synthetic and over-the-air radio data show that the proposed framework reliably and efficiently classifies radio signals, and often significantly outperform state-of-the-art approaches. | ||