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MOMP-paper

Scripts used to generate the results of the MOMP paper.

How it works

Folder src has the scripts used to process the data. Folder data has the channel data, the processed result and the saved figures.

Notation

The notation used for the hybrid vs analog architecture usage in the folder structure is AH for Analog user vs Hybrid access point and HH for Hybrid user vs Hybrid access point.

The data

The data generated by the ray-tracer is stored in the scenario folder. The scenario used for this paper is office-walls. The files corresponding to the generated rays are

AP_pos.txt

Contains the position of the two access points in the scenario.

UE_pos.txt

Contains the positions of the moving used in the scenario.

Info.txt

Contains the information of all the channel rays for every user position/access point pair

AP_selected.txt

Contains the index of the access point selected for communication for each used position.

Info_selected.txt

Contains the information of all the channel rays for every user position with its corresponding selected access point.

The channel decompositions scripts

These scripts run the channel estimation algorithms. These scripts are architecture specific, so we will use the character X to represent both A or H in the script name.

decompose_XH_office_dropT.py

Runs the channel decomposition for the SWOMP algorithm.

decompose_XH_office_general.py

Runs the channel decomposition for the MOMP and TD-OMP algorithms.

decompose_XH_office_general_dict.py

Runs the channel decomposition for the MOMP and TD-OMP algorithms for the different number of frames using a more flexible dictionary.

Figure generation scripts

These scripts are used to generate the figures in the paper. We describe them down below by order of appearance in the paper. Be aware that some scripts generate figures that were not included in the final version of the journal paper.

compare+.py

Generates a high level comparison between the three different algorithms.

angularerror.py

Generates angular error CDFs.

cherror.py

Generates the regularized mean square error for the channel tensor CDFs.

tdoferror.py

Generates the corrected second path delay error CDFs.

localize_Pt.py

Generates the localization results over different transmission power.

localize_AHvsHH.py

Generates the localization results for both architectures over different number of training frames.

About

Code used to generate the results for the multi-dimensional orthogonal matching pursuit journal paper

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