{"id":"W4214926958","doi":"10.1109/tcomm.2022.3156065","title":"Efficient Channel Estimation for Wideband Millimeter Wave Massive MIMO Systems With Beam Squint","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wideband; Computer science; Precoding; Cramér–Rao bound; Channel (broadcasting); Estimator; Subcarrier; MIMO; Algorithm; Bandwidth (computing); Electronic engineering; Estimation theory; Orthogonal frequency-division multiplexing; Telecommunications; Mathematics; Statistics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005381277,0.0009207855,0.0007100587,0.0003027713,0.0004534629,0.0005742624,0.0004462994,0.0005016441,0.0008282717],"category_scores_gemma":[0.001782463,0.0004021221,0.0004215174,0.0004690755,0.0004485526,0.0009926732,0.0008801016,0.000998772,0.0004709438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003337748,"about_ca_system_score_gemma":0.001006318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0026327,"about_ca_topic_score_gemma":0.004075929,"domain_scores_codex":[0.9995022,0.000155076,0.00002244456,0.00009222819,0.0001540595,0.00007406893],"domain_scores_gemma":[0.9994172,0.0003007503,0.00008124567,0.00006545005,0.0001119021,0.00002354447],"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":[0.0002588008,0.0001027604,0.001674427,0.0002897649,0.0001011694,0.0002938294,0.0002169124,0.7103035,0.05184533,0.02104696,0.003813763,0.2100528],"study_design_scores_gemma":[0.00001162921,0.00006144055,0.0002996903,0.00001152958,0.00001274684,0.00006980124,0.00002849417,0.9915045,0.004647424,0.002548459,0.000788184,0.00001600593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01301035,0.0003669871,0.9851119,0.0001415553,0.00003853223,0.000020428,0.0000385974,0.0002831722,0.0009884011],"genre_scores_gemma":[0.7510124,0.001285249,0.2438747,0.0002544496,0.0001464726,0.0001428611,0.0002866653,0.00005006438,0.002947105],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0026327,"threshold_uncertainty_score":0.005234718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03915403437497746,"score_gpt":0.2397128761308895,"score_spread":0.2005588417559121,"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."}}