{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002103785,0.0001856726,0.0001821406,0.0002525727,0.000952358,0.00005444939,0.0003029441,0.00004672282,0.00004287305],"category_scores_gemma":[0.000003593056,0.0001877189,0.00009973444,0.0002805266,0.00005232656,0.00005834115,0.000006136013,0.0003348629,0.00001313396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002389471,"about_ca_system_score_gemma":0.00003330495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002774728,"about_ca_topic_score_gemma":0.00002244004,"domain_scores_codex":[0.9989446,0.00007804954,0.0003305105,0.0001988072,0.0002217432,0.0002262412],"domain_scores_gemma":[0.9985808,0.0002264703,0.00006762997,0.0009270918,0.0001177815,0.00008026547],"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":[0.00002696019,0.000136299,1.123194e-7,0.0000383148,0.00008625509,3.317325e-7,0.0009101832,0.9933267,0.003423675,0.00003351213,0.0001334447,0.00188419],"study_design_scores_gemma":[0.0005543945,0.0001693501,0.000001320464,0.00003970915,0.00008967779,0.00001629574,0.000691105,0.9833741,0.01391423,0.00004803496,0.0008739632,0.0002277895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009273434,0.0003267832,0.9875219,0.0003852229,0.0004649399,0.001036649,0.0002854851,0.0002885822,0.0004169908],"genre_scores_gemma":[0.9858896,0.0000651809,0.01116169,0.00006713151,0.00001454872,0.002447307,0.00008468605,0.00005986844,0.0002100114],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9766161,"threshold_uncertainty_score":0.7654955,"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."}}