Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems. - Nuwanthika Rajapaksha; Nandana Rajatheva; Matti Latva-aho (VTC Spring, 2020)

Deep Learning-based Power Control for Cell-Free Massive MIMO Networks. - Nuwanthika Rajapaksha; K. B. Shashika Manosha; Nandana Rajatheva; Matti Latva-aho (ICC, 2021)

Pervasive Artificial Intelligence in Next Generation Wireless: The Hexa-X Project Perspective. - Miltiadis C. Filippou; Vasiliki Lamprousi; Jafar Mohammadi; Mattia Merluzzi; Elif Ustundag Soykan; Tamás Borsos; Nandana Rajatheva; Nuwanthika Rajapaksha; Luc Le Magoarou; Pietro Piscione; András Benczúr; Quentin Lampin; et al. (AI6G@WCCI, 2022)

The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G. - Mattia Merluzzi; Tamás Borsos; Nandana Rajatheva; András A. Benczúr; Hamed Farhadi; Taha Yassine; Markus Dominik Mueck; Sokratis Barmpounakis; Emilio Calvanese Strinati; Dilin Dampahalage; Panagiotis Demestichas; Pietro Ducange; et al. (IEEE Access, 2023)

Unsupervised Learning-Based Joint Power Control and Fronthaul Capacity Allocation in Cell-Free Massive MIMO With Hardware Impairments. - Nuwanthika Rajapaksha; K. B. Shashika Manosha; Nandana Rajatheva; Matti Latva-aho (IEEE Wirel. Commun. Lett., 2023)

Jointly-Mapped Reflection Modulation with Reconfigurable Intelligent Surfaces. - Pasan Karunasena; Nandana Rajatheva; Nuwanthika Rajapaksha; Dilin Dampahalage; Dileepa Marasinghe; Matti Latva-aho (ICC, 2024)

Deep Learning-based Joint Pilot and Data Power Control in Cell-Free Massive MIMO Networks. - Nuwanthika Rajapaksha; Nandana Rajatheva; Matti Latva-aho (VTC Fall, 2024)

Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks With Asymmetric Loss. - Nuwanthika Rajapaksha; Jafar Mohammadi; Stefan Wesemann; Thorsten Wild; Nandana Rajatheva (IEEE Trans. Veh. Technol., 2024)