{"id":"W3192028045","doi":"10.1109/tvt.2022.3197452","title":"Deep Learning Based Antenna-Time Domain Channel Extrapolation for Hybrid Mmwave Massive MIMO","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Telecommunications link; Precoding; MIMO; Computer science; Extrapolation; Base station; Overhead (engineering); Channel (broadcasting); Channel state information; Encoder; Electronic engineering; Real-time computing; Computer network; Engineering; Telecommunications; Mathematics; Wireless","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002055384,0.0002173408,0.0002284433,0.0007611754,0.0005351385,0.00001841104,0.0001704096,0.0001396458,0.0002134081],"category_scores_gemma":[0.00000750752,0.0002605634,0.0001682576,0.0004071974,0.00004589354,0.00007152808,0.000002545266,0.0006429564,0.00003164738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001865518,"about_ca_system_score_gemma":0.0000198131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002520424,"about_ca_topic_score_gemma":0.000003486256,"domain_scores_codex":[0.9987878,0.00005956572,0.0002896883,0.0003259206,0.0001803515,0.0003567321],"domain_scores_gemma":[0.9995026,0.00005837195,0.00005985601,0.0002548472,0.00007076713,0.00005353054],"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":[0.0000288967,0.00004718492,5.643998e-7,0.00002173976,0.00005410885,0.000009894258,0.0000660431,0.8065725,0.1846416,0.00001754945,0.00001612657,0.008523849],"study_design_scores_gemma":[0.000677651,0.0002070883,6.66692e-7,0.00001178772,0.00003629742,0.00002783802,0.0002089743,0.8079935,0.1887526,0.0006789737,0.001178033,0.0002266794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0565416,0.0001586813,0.9407896,0.0004950918,0.0003403062,0.0005363899,0.00004164425,0.001051773,0.00004487546],"genre_scores_gemma":[0.9914684,0.0000213882,0.007442405,0.00009619847,0.00002266942,0.0007184777,0.00004617175,0.00007655233,0.0001077095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9349268,"threshold_uncertainty_score":0.9999847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0099968016604623,"score_gpt":0.2022352480696881,"score_spread":0.1922384464092258,"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."}}