With the great capability of learning high-level representational features of unstructured data automatically, deep learning is acknowledged as a powerful machine learning algorithm for radio signal processing. For example, convolutional neural networks are leveraged to develop automatic modulation classification frameworks in [1]-[3]. CNNs are also used to design intelligent frameworks that can accurately classify waveform and radio signals in radar systems [4], [5]. Along with the recent advancements in computing hardware and artificial intelligence models, such as knowledge graph, Transformers, and large language models, and the development of future networks and the Internet-of-Things (IoT), it is expected that there are plenty of opportunities to skillfully leverage deep learning for model design and performance improvement.
In addition to the applications of deep learning in radio signal processing, I am happy to discuss and supervise any project ideas about the use of deep learning in other areas, such as cybersecurity and computer vision. More information: https://www.scss.tcd.ie/viet.pham
References
[1] T. Huynh-The, C.-H. Hua, Q.-V. Pham, and D.-S. Kim, “MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification,” IEEE Communications Letters, vol. 24, no. 4, pp. 811-815, Apr. 2020.
[2] T. Huynh-The, Q.-V. Pham, T.-V. Nguyen, T. T. Nguyen, D. B. da Costa, and D.-S. Kim, “RanNet: Learning Residual-Attention Structure in CNNs for Automatic Modulation Classification,” IEEE Wireless Communications Letters, vol. 11, no. 6, pp. 1243-1247, Jun. 2022.
[3] T. Huynh-The, T.-V. Nguyen, Q.-V. Pham, D. B. da Costa, and D.-S. Kim, “MIMO-OFDM Modulation Classification Using Three-Dimensional Convolutional Network,” IEEE Transactions on Vehicular Technology, vol. 71, no. 6, pp. 6738-6743, Jun. 2022.
[4] T. Huynh-The, C.-H. Hua, V.-S. Doan, Q.-V. Pham, and D.-S. Kim, “Accurate Deep CNN-based Waveform Recognition for Intelligent Radar Systems,” IEEE Communications Letters, vol. 25, no. 9, pp. 2938-2942, Sep. 2021.
[5] T. Huynh-The, V.-S. Doan, C.-H. Hua, Q.-V. Pham, T.-V. Nguyen, and D.-S. Kim, “Accurate LPI Radar Waveform Recognition with CWD-TFA for Deep Convolutional Network,” IEEE Wireless Communications Letters, vol. 10, no. 8, pp. 1638-1642, Aug. 2021.