{"id":"W3211218110","doi":"10.1109/access.2021.3121750","title":"A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Channel state information; Computer science; Cluster analysis; MIMO; Overhead (engineering); Channel (broadcasting); Convolutional neural network; Geospatial analysis; Data mining; Real-time computing; Pattern recognition (psychology); Artificial intelligence; Computer network; Remote sensing; Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001716597,0.00011116,0.0001428053,0.00007013691,0.00008770869,0.0001735245,0.000141679,0.00004996706,0.000007970499],"category_scores_gemma":[0.00008061514,0.0001197848,0.00001925442,0.00007436288,0.000008876089,0.0002953621,0.0000438777,0.0001080259,0.000002322947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002727528,"about_ca_system_score_gemma":0.00002245819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002321529,"about_ca_topic_score_gemma":0.00001383384,"domain_scores_codex":[0.9993113,0.00002864631,0.0001892532,0.0002235639,0.00009424186,0.0001529995],"domain_scores_gemma":[0.9995388,0.00007039554,0.00004823228,0.0001905733,0.000103953,0.00004804221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000681227,0.000007830929,0.00002000482,0.0004152192,0.00002197873,0.000007813587,0.00004071573,0.9670728,0.02563639,0.000004931213,0.0001503244,0.006615219],"study_design_scores_gemma":[0.0002255755,0.00001068258,0.000004611662,0.00006870037,0.00001929972,0.000007724868,0.00001891727,0.7952831,0.2040028,0.00006943146,0.0001719882,0.0001171414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01495901,0.0004414939,0.983515,0.00003336449,0.0003705233,0.0004129162,0.0000366011,0.0001681352,0.00006293866],"genre_scores_gemma":[0.9878912,0.00003703199,0.0113215,0.00003006393,0.0001144532,0.00022245,0.0003321865,0.00003270926,0.00001843177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9729322,"threshold_uncertainty_score":0.4884683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04212460741892725,"score_gpt":0.2937421506578472,"score_spread":0.2516175432389199,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}