文件列表:

narrowband_program (0, 2019-12-02)
narrowband_program\GEVD (0, 2019-12-02)
narrowband_program\GEVD\GEVD_method.m (1121, 2019-12-02)
narrowband_program\GEVD\gevd_algorithm.m (681, 2019-12-02)
narrowband_program\MO (0, 2019-12-02)
narrowband_program\MO\MMSE_cost.m (276, 2019-12-02)
narrowband_program\MO\MMSE_egrad.m (258, 2019-12-02)
narrowband_program\MO\MO_method.m (1066, 2019-12-02)
narrowband_program\MO\mo_algorithm.m (339, 2019-12-02)
narrowband_program\main_vs_SNR.asv (3426, 2019-12-02)
narrowband_program\main_vs_SNR.m (3606, 2019-12-02)
shared_APIs (0, 2019-12-02)
shared_APIs\Algorithms (0, 2019-12-02)
shared_APIs\Algorithms\MMSE_method.m (1094, 2019-12-02)
shared_APIs\Algorithms\MO_Alt (0, 2019-12-02)
shared_APIs\Algorithms\MO_Alt\MO_Alt_method.m (633, 2019-12-02)
shared_APIs\Algorithms\MO_Alt\conjugategradient.m (2989, 2019-12-02)
shared_APIs\Algorithms\MO_Alt\getCostGrad.m (143, 2019-12-02)
shared_APIs\Algorithms\MO_Alt\linesearch_adaptive.m (4467, 2019-12-02)
shared_APIs\Algorithms\MO_Alt\mergeOptions.m (936, 2019-12-02)
shared_APIs\Algorithms\MO_Alt\sig_manif.m (440, 2019-12-02)
shared_APIs\Algorithms\MO_Alt\stoppingcriterion.m (353, 2019-12-02)
shared_APIs\Algorithms\Mrate_method.m (292, 2019-12-02)
shared_APIs\Algorithms\OMP(MSE) (0, 2019-12-02)
shared_APIs\Algorithms\OMP(MSE)\MSEOMP.m (350, 2019-12-02)
shared_APIs\Algorithms\OMP(MSE)\OMP_method.asv (991, 2019-12-02)
shared_APIs\Algorithms\OMP(MSE)\OMP_method.m (898, 2019-12-02)
shared_APIs\Algorithms\Yuwei2016 (0, 2019-12-02)
shared_APIs\Algorithms\Yuwei2016\Yuwei_method.m (592, 2019-12-02)
shared_APIs\Algorithms\Yuwei2016\yuweiA1.asv (653, 2019-12-02)
shared_APIs\Algorithms\Yuwei2016\yuweiA1.m (652, 2019-12-02)
shared_APIs\Algorithms\Yuwei2016\yuweiA2.m (686, 2019-12-02)
shared_APIs\channel_generation.m (2126, 2019-12-02)
shared_APIs\complexcirclefactory.m (3558, 2019-12-02)
shared_APIs\metrics (0, 2019-12-02)
shared_APIs\metrics\get_ber.m (687, 2019-12-02)
shared_APIs\metrics\get_metric.m (475, 2019-12-02)
shared_APIs\metrics\get_mse.m (324, 2019-12-02)
... ...

# Hybrid-Beamforming-for-Millimeter-Wave-Systems-Using-the-MMSE-Criterion ## Introduction The Matlab Simulation codes for Hybrid Beamforming for Millimeter Wave Systems Using the MMSE Criterion. This paper on published in IEEE transactions on communications in Jan 2019. If these codes is help for your work, you can choose to cite the paper, not necessary. The pdf of this paper can be found in IEEE link: [https://ieeexplore.ieee.org/document/8616797](https://ieeexplore.ieee.org/document/8616797) Arxiv link: [https://arxiv.org/abs/1902.08343?context=cs.IT](https://arxiv.org/abs/1902.08343?context=cs.IT). Also, I recommend my rencent work that using deep learning to solve the HBF design problem. This work can be referred to IEEE link: [https://ieeexplore.ieee.org/document/8847377/](https://ieeexplore.ieee.org/document/8847377/) Arxiv link: [https://arxiv.org/abs/1904.03657](https://arxiv.org/abs/1904.03657) and all codes are openned at [this repo](https://github.com/TianLin0509/BF-design-with-DL). I only update the codes for narrowband cases, but the extension to broadband is straightforward. which can be referred to my another repository named "August_mmwave", [gateway](https://github.com/TianLin0509/August_mmwave). however, the later is written in a bad form so not easy to read. I don't have enough time now, so maybe it will be updated in the future. ## How to use This code is really is to use. First, you should totally add all packages to the path, so you can use these functions. Then, just run the ```main_vs_SNR.m``` file directly. ## Content This codes including several algorithms mentioned in my paper, all can easily cited by these convenient APIs. ## End I won't update it recently because of limitation of time. However, if you have any problems, you can directly contact me by e-mail: ```lint17@fudan.edu.cn```, I'm glad to help you. Besides, the broadband codes can be referred to [here](https://github.com/TianLin0509/August_mmwave).

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