{"id":"W2154394329","doi":"10.1109/tap.2007.901862","title":"Mobile Terminal Location for MIMO Communication Systems","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Base station; Multipath propagation; Computer science; Terminal (telecommunication); Position (finance); Context (archaeology); Cramér–Rao bound; Angle of arrival; Delay spread; MIMO; Mobile station; Mobile telephony; Direction of arrival; Square root; SIGNAL (programming language); Root mean square; Mean squared error; Set (abstract data type); Algorithm; Telecommunications; Estimation theory; Mobile radio; Mathematics; Statistics; Electrical engineering; Engineering; Geometry; Antenna (radio)","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":[0.0002284907,0.0001038337,0.00009501457,0.0001608068,0.0001896458,0.00004586912,0.00006418811,0.0001103998,0.000003998428],"category_scores_gemma":[0.000004179631,0.0001003137,0.00002758714,0.000193988,0.00004837434,0.0001520393,4.344701e-7,0.0001021825,0.0000065752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005109702,"about_ca_system_score_gemma":0.000007787819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001300875,"about_ca_topic_score_gemma":0.00002611444,"domain_scores_codex":[0.999436,0.00001214493,0.0002181189,0.0001123984,0.00008110763,0.0001402052],"domain_scores_gemma":[0.9996007,0.00005203953,0.00003476431,0.0001642456,0.0001178067,0.00003043726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003322854,0.0002611354,0.0000942166,0.001108718,0.000127247,0.000003577883,0.002697774,0.2953945,0.04279245,0.009133385,0.0007249813,0.6473297],"study_design_scores_gemma":[0.0007876293,0.0003673189,0.0002237525,0.0001734371,0.00005031829,0.00003177253,0.001587758,0.8507395,0.1412788,0.0003520215,0.004072306,0.0003354048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02244763,0.0006039854,0.9753698,0.00002118358,0.000321668,0.0006002876,0.00001241363,0.0004056076,0.0002174295],"genre_scores_gemma":[0.9977784,0.0004769358,0.001301116,0.00001976061,0.00002373419,0.0001778435,0.00001808315,0.00001981044,0.0001843783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9753307,"threshold_uncertainty_score":0.4090676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249238627671659,"score_gpt":0.2387459541325298,"score_spread":0.2262535678558132,"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."}}