Accompanying code for On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals
Citation: G. C. F. Lee, A. Weiss, A. Lancho, Y. Polyanskiy and G. W. Wornell, "On Neural Architectures for Deep Learning-Based Source Separation of Co-Channel OFDM Signals," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096702.
ArXiv Preprint: [arXiv]
Link to download trained model weights: Dropbox Link
Work motivated by the MIT-USAF AIA RF Challenge for Single-Channel Source Separation
Click here for the Single-Channel RF Challenge Github Repository
Click here for details on the challenge setup
Research was sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
The authors acknowledge the MIT SuperCloud and Lincoln Laboratory Supercomputing Center for providing HPC resources that have contributed to the research results reported within this paper.
Alejandro Lancho has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101024432.