{"id":"W2150470922","doi":"10.1109/ccece.2007.272","title":"Mobile Location in MIMO Communication Systems by Using Learning Machine","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Multilateration; Trilateration; MIMO; Multipath propagation; Computer science; Base station; Angle of arrival; Real-time computing; Wireless; Mobile station; Redundancy (engineering); Electronic engineering; Antenna (radio); Computer network; Telecommunications; Engineering; Beamforming","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005286963,0.0005089105,0.0005175923,0.0005341901,0.000290056,0.0006479779,0.0004878204,0.0006630098,0.0008625124],"category_scores_gemma":[0.001563722,0.0001954027,0.0003504207,0.0006011649,0.0004247684,0.0009388836,0.0004371575,0.0006251312,0.0005822923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003692824,"about_ca_system_score_gemma":0.0002677464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001988927,"about_ca_topic_score_gemma":0.001771907,"domain_scores_codex":[0.9995889,0.0001781507,0.00002049559,0.00008245054,0.00009615426,0.00003387211],"domain_scores_gemma":[0.9993793,0.0003693532,0.00007022136,0.00007248766,0.00009591797,0.00001273705],"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":[0.00008790417,0.00006628221,0.002125385,0.0001485509,0.00008521184,0.0001847598,0.0001220568,0.5836558,0.008117586,0.01610543,0.001423923,0.387877],"study_design_scores_gemma":[0.000005036627,0.00003630022,0.0003130712,0.000008772522,0.000008676895,0.00004873356,0.00001410861,0.9911506,0.001993208,0.005296314,0.001113414,0.00001176956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01609442,0.0006300848,0.9807771,0.0001509494,0.00005693444,0.00001811319,0.00002877976,0.0005888215,0.001654915],"genre_scores_gemma":[0.6708298,0.0009493067,0.3242213,0.0001326416,0.0001753742,0.0000723863,0.0001101818,0.00003960599,0.003469293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001988927,"threshold_uncertainty_score":0.003954768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00834866286312608,"score_gpt":0.2324130324113963,"score_spread":0.2240643695482702,"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."}}