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DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

This simulator implements the experiments of the paper “DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems,” R. Wiesmayr, C. Dick, J. Hoydis, and C. Studer, Procs. Asilomar Conf. Signals, Syst., Comput., Oct. 2022, available at https://arxiv.org/abs/2212.07816

The simulations are implemented with NVIDIA Sionna Release v0.11 and own extensions.

Parts of the code are also based on

  • R. Wiesmayr, G. Marti, C. Dick, H. Song, and C. Studer “Bit Error and Block Error Rate Training for ML-Assisted Communication,” arXiv:2210.14103, 2022, available at https://arxiv.org/abs/2210.14103
  • C. Studer, S. Fateh, and D. Seethaler, “ASIC Implementation of Soft-Input Soft-Output MIMO Detection Using MMSE Parallel Interference Cancellation,” IEEE Journal of Solid-State Circuits, vol. 46, no. 7, pp. 1754–1765, July 2011, available at https://www.csl.cornell.edu/~studer/papers/11JSSC-mmsepic.pdf

If you are using this simulator (or parts of it) for a publication, you must cite the above-mentioned references and clearly mention this in your paper.

Running simulations

Please have your Python environment ready with NVIDIA Sionna v0.11, as the code was developed and tested for this version.

The main simulation script simulation_script.py is located in ./scr and contains multiple simulation parameters that can be modified at will. The script trains the specified signal processing models and evaluates a performance benchmark. At the end, bit error rate and block error rate curves are plotted and saved.

The ray-tracing channels utilized by our script can be downloaded from here.

Before running the simulations, the following directories have to be created:

  • ./data/weights/ for saving the trained model weights
  • ./results for the simulation results (BER and BLER curves), which are saved as .csv and .pickle files
  • If you want to use the ray-tracing channels, download and place them under ./data/channels/

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