{"id":13800406,"url":"https://github.com/malrabeiah/Sub6-Preds-mmWave","last_synced_at":"2025-05-13T09:31:36.736Z","repository":{"id":41070640,"uuid":"186709611","full_name":"malrabeiah/Sub6-Preds-mmWave","owner":"malrabeiah","description":"Using sub-6 GHz channels to predict mmWave beams and link blockage.","archived":false,"fork":false,"pushed_at":"2021-12-06T03:01:04.000Z","size":30,"stargazers_count":34,"open_issues_count":3,"forks_count":19,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-11-18T15:53:16.836Z","etag":null,"topics":["5g","channel-mapping","deep-learning","matlab","mmwave"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/malrabeiah.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-05-14T22:29:27.000Z","updated_at":"2024-08-20T23:31:36.000Z","dependencies_parsed_at":"2022-08-28T21:10:51.770Z","dependency_job_id":null,"html_url":"https://github.com/malrabeiah/Sub6-Preds-mmWave","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/malrabeiah%2FSub6-Preds-mmWave","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/malrabeiah%2FSub6-Preds-mmWave/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/malrabeiah%2FSub6-Preds-mmWave/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/malrabeiah%2FSub6-Preds-mmWave/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/malrabeiah","download_url":"https://codeload.github.com/malrabeiah/Sub6-Preds-mmWave/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253913136,"owners_count":21983263,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["5g","channel-mapping","deep-learning","matlab","mmwave"],"created_at":"2024-08-04T00:01:12.350Z","updated_at":"2025-05-13T09:31:36.459Z","avatar_url":"https://github.com/malrabeiah.png","language":"MATLAB","funding_links":[],"categories":["Research"],"sub_categories":["Diameter"],"readme":"# Sub-6 Predicts mmWave Beam-forming Vectors:\nThis is an implementation of the Deep Learning (DL) solution that uses sub-6 GHz channels to predict top-n beams of mmWave users. With the approperiate modifications and given the right dataset, it could also be used to generate all the figures in [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://ieeexplore.ieee.org/document/9121328).\n\n# Requirements:\n\nEssential:\n\n1- MATLAB deep learning toolbox.\n\n2- [DeepMIMO dataset](https://deepmimo.net/)\n\nOptional:\n\n1- NVIDIA GPU card.\n\n2- CUDA toolkit.\n\n3- cuDNN package.\n\n# Running Instructions:\n\n1- Generate the datasets using scenarios [O1_28](https://deepmimo.net/scenarios/o1-scenario/) and [O1_3p5](https://deepmimo.net/scenarios/o1-scenario/) in the DeepMIMO dataset. Use the parameters listed in Table.1, Section VII-B of [the paper](https://ieeexplore.ieee.org/document/9121328).\n\n2- Prepare two MATLAB structures, one for sub-6GHz data and the other for 28GHz. Please refer to the comments at the beginning of main.m for more information on the data structures.\n\n3- Assign the paths to the two MATLAB structures to the two parameters: options.dataFile1 and options.dataFile2 in the beginning of main.m.\n\n4- Run main.m to get the figure 4-b in the paper.\n\nREMARK: Transmit power range is defined in tx_power in main.m.\n\n# Citation:\n\nIf you use these codes or a modified version of them, please cite the following work:\n```\n@ARTICLE{Alrabeiah2020,\n  author={Alrabeiah, Muhammad and Alkhateeb, Ahmed},\n  journal={IEEE Transactions on Communications}, \n  title={Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels}, \n  year={2020},\n  volume={68},\n  number={9},\n  pages={5504-5518},\n  doi={10.1109/TCOMM.2020.3003670}}\n```\n\n# License:\n\nThis code package is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmalrabeiah%2FSub6-Preds-mmWave","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmalrabeiah%2FSub6-Preds-mmWave","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmalrabeiah%2FSub6-Preds-mmWave/lists"}