{"id":"W2130313407","doi":"10.1109/icassp.2007.366236","title":"Enhancing Synchronized Cellular Forward Links by Exploiting Spatial-Temporal Signatures using Extended Subspace Approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Base station; Interference (communication); Channel (broadcasting); Transmission (telecommunications); Cellular network; Subspace topology; MIMO; Code division multiple access; Signature (topology); Electronic engineering; Exploit; Computer network; Telecommunications; Artificial intelligence; Engineering; Mathematics","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":[0.0003394643,0.0005279263,0.0003945769,0.0002572499,0.0002092114,0.0003912977,0.000344387,0.0005068985,0.001327425],"category_scores_gemma":[0.001183699,0.0002046288,0.0003267082,0.0003825818,0.0003402559,0.000904948,0.0007053041,0.0002580217,0.0002772228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717645,"about_ca_system_score_gemma":0.0003492355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009110915,"about_ca_topic_score_gemma":0.001280786,"domain_scores_codex":[0.9997903,0.00007372404,0.0000070936,0.00001938314,0.00007118163,0.00003830239],"domain_scores_gemma":[0.9994816,0.0002720118,0.00008058056,0.00006164571,0.00008259987,0.00002162466],"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":[0.0003051631,0.0001505835,0.001406623,0.00006146023,0.00005911903,0.0001519647,0.0001679137,0.78511,0.1070606,0.008393282,0.0003862095,0.09674704],"study_design_scores_gemma":[0.00003578234,0.0002415072,0.0005614104,0.000003534774,0.00001969222,0.00009699036,0.00003540137,0.9563696,0.03912265,0.002789763,0.0006995884,0.00002405279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1906969,0.0001350194,0.8057835,0.00006441712,0.00001983978,0.00002263688,0.00003551208,0.0005105779,0.002731586],"genre_scores_gemma":[0.8836692,0.0001599831,0.1141215,0.00003369251,0.0000186708,0.00003995212,0.00005268262,0.00003641921,0.001867857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001327425,"threshold_uncertainty_score":0.004440665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488469308312546,"score_gpt":0.2852444185040809,"score_spread":0.2603597254209555,"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."}}