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RIS-aided joint channel estimation and localization at mmWave

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RIS-aided joint channel estimation and localization at mmWave

Code to run to estimate the channels and localize the users for the paper "RIS-aided Joint Channel Estimation and Localization at mmWave under Hardware Impairments: A Dictionary Learning-based Approach".

Dataset

The data folder contains the information related to a ray-tracing dataset for an RIS-aided mmWave system operating in an indoor factory environment. The locations of the BS, RIS and MSs are in data/AP_pos.txt, data/RIS_pos.txt and data/UE_pos.txt, respectively. The information related to paths of the channels are contained in the data/Info_BM.txt, data/Info_BR.txt and data/Info_RM.txt for the BS-MS, BS-RIS and RIS-MS channels, respectively. Each path has the following information: Phase of the channel gain, delay, channel gain, azimuth AoA, elevation AoA, azimuth AoD, and elevation AoD.

Simulation

The main.py code can be run to estimate the channels via MOMP algorithm and localiza the users. All the other .py files are complementary functions.

Parameters to set for each case:

  • Only BS 8x8: scenario = 0, N_M_AP = 8, N_est = 5
  • Only RIS 16x16: scenario = 1, N_RIS = 16, N_M_AP = 16, N_est = 5
  • Only RIS 32x32: scenario = 1, N_RIS = 32, N_M_AP = 32, N_est = 5
  • BS and RIS 16x16: scenario = 2, N_RIS = 16, N_M_AP = 16, N_est = 20
  • BS and RIS 32x132: scenario = 2, N_RIS = 32, N_M_AP = 32, N_est = 20

Citation

If you use the dataset or the code, please cite our paper as:

@article{Bayraktar2024,
  title={{RIS}-aided joint channel estimation and localization at {mmWave} under hardware impairments: A dictionary learning-based approach},
  author={Bayraktar, Murat and and González-Prelcic, Nuria and Alexandropoulos, George C. and Chen, Hao},
  journal={IEEE Trans. Wireless Commun.},
  year={2024}
}

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