{"id":"W2996294337","doi":"10.1109/wcsp.2019.8928030","title":"Deep Learning for Compressed Sensing Based Channel Estimation in Millimeter Wave Massive MIMO","year":2019,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Matching pursuit; Channel (broadcasting); Compressed sensing; Computer science; MIMO; Artificial neural network; Algorithm; Extremely high frequency; Artificial intelligence; Electronic engineering; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001518943,0.0001433529,0.000177407,0.0001816762,0.00003740555,0.00003730767,0.00003905924,0.00007877331,0.00008960726],"category_scores_gemma":[0.00003523334,0.0001447181,0.00005973685,0.00009740783,0.000006391318,0.0001155874,0.00001162993,0.0001346601,0.00004803543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005706279,"about_ca_system_score_gemma":0.000006908943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001159742,"about_ca_topic_score_gemma":0.00001274602,"domain_scores_codex":[0.9992151,0.00002689555,0.00023896,0.0001809001,0.00009982655,0.000238283],"domain_scores_gemma":[0.9996133,0.0001316297,0.00003378424,0.0001181688,0.00005842487,0.00004464339],"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":[0.00001134896,0.000005285478,0.00002207642,0.000092075,0.00001061297,9.026161e-7,0.0002112227,0.944279,0.04782575,0.000007152106,0.00001810231,0.00751644],"study_design_scores_gemma":[0.0007501919,0.00002808821,0.00004887587,0.00005775167,0.000007686199,8.678098e-7,0.00009549398,0.903363,0.09528488,0.0001212011,0.00006525164,0.0001766819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1093486,0.00004595883,0.888597,0.00005009556,0.0001825754,0.0004187716,0.000001029038,0.0001733198,0.001182609],"genre_scores_gemma":[0.9098345,0.000003902497,0.08979657,0.0001142261,0.00001995616,0.00001245647,0.00005348921,0.00003796824,0.0001269458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8004858,"threshold_uncertainty_score":0.5901434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159215197886299,"score_gpt":0.2232645108405376,"score_spread":0.2016723588616746,"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."}}