Bayesian Massive MIMO Channel Estimation with Parameter Estimation using Low-Resolution ADCs

• We aim to recover massive MIMO channels from quantizated measurments produced by low-reolution ADCs, we adopt the AMP with built-in parameter estimation (AMP-PE) [1,2] to solve this problem.

• AMP-PE offers a much simpler way to estimate the distribution parameters, which allows us to directly work with true quantization noise models. {width=100%}

• This package contains code files to implement the approach described in the following paper.

@INPROCEEDINGS{CE_AMP_PE:2021,
    author={S. Huang and D. Qiu and T. D. Tran},
    booktitle={Proceedings of IEEE ICASSP},
    title={Bayesian Massive MIMO Channel Estimation with Parameter Estimation using Low-Resolution ADCs},
    pages={4830-4834},
    year={2021},
    month={June}
}

If you use this package and find it helpful, please cite the above paper. Thanks 😄

Summary

    ./src          -- This folder contains MATLAB files to recover the massive MIMO channel from quantized measurements.
    ./demo         -- This folder contains demo files to run experiments in the paper, detailed comments are within each demo file.

Usage

You can follow the following steps to run the program. Detailed comments are within each demo file.

Open MATLAB and type the following commands into the console:

    >> addpath(genpath('./'))
    >> noisy_channel_estimation_1bit
    >> noisy_channel_estimation_2bit
    >> noisy_channel_estimation_3bit

Reference

[1] S. Huang and T. D. Tran, "Sparse signal recovery using generalized approximate message passing with built-in parameter estimation," in Proceedings of IEEE ICASSP, March 2017, pp. 4321–4325.

[2] S. Huang, D. Qiu, and T. D. Tran, "Approximate Message Passing With Parameter Estimation for Heavily Quantized Measurements," in IEEE Transactions on Signal Processing, vol. 70, pp. 2062-2077, 2022.