althehunter / ce-42

Implementation of CE-42 paper (in Matlab) - they are not mine

CE-42

Implementation of CE-42 paper (in Matlab) - THE CODES ARE NOT MINE

THIS IS THE ORIGINAL README FROM THE AUTHORS: This simulation code package is mainly used to reproduce the results of the following paper [1]:

[1] X. Wei and L. Dai, "Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?," IEEE Commun. Lett., vol. 26, no. 1, pp. 177-181, Jan. 2022

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If you use this simulation code package in any way, please cite the original paper [1] above.

The author in charge of this simulation code pacakge is: Xiuhong Wei (email: [email protected]).

Reference: We highly respect reproducible research, so we try to provide the simulation codes for our published papers (more information can be found at: http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.html).

Please note that the MATLAB R2020a is used for this simulation code package, and there may be some imcompatibility problems among different MATLAB versions.

Copyright reserved by the Broadband Communications and Signal Processing Laboratory (led by Dr. Linglong Dai), Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

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Abstract of the paper:

Extremely large-scale massive MIMO (XL-MIMO) is a promising technique for future 6G communications. However, existing far-field or near-field channel model mismatches the hybrid-field channel feature in the practical XL-MIMO system. Thus, existing far-field and near-field channel estimation schemes cannot be directly used to accurately estimate the hybrid-field XL-MIMO channel. To solve this problem, we propose an efficient hybrid-field channel estimation scheme by accurately modeling the XL-MIMO channel. Specifically, we firstly reveal the hybrid-field channel feature of the XL-MIMO channel, where different scatters may be in far-field or near-field region. Then, we propose a hybrid-field channel model to capture this feature, which contains both the far-field and near-field path components. Finally, we propose a hybrid-field channel estimation scheme, where the far-field and near-field path components are respectively estimated. Simulation results show that the proposed scheme performs better than existing schemes.

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How to use this simulation code package?

All simulation results can be generated by running the corresponding .m file.

For the simulation results of the NMSE performance comparison against the SNR (Fig. 3 in the paper) and the NMSE performance comparison against the adjustable parameter \gamma (Fig. 4 in the paper) can be obtained by running Main1.m and Main2.m in the folder.

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Enjoy the reproducible research!