{"id":"W2150202460","doi":"10.1109/icc.2006.255450","title":"Blind Channel Estimation and Multi-User Detection for Wireless CDMA Systems","year":2006,"lang":"en","type":"article","venue":"2006 IEEE International Conference on Communications","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Multiuser detection; Detector; Matching pursuit; Channel (broadcasting); Subspace topology; Code division multiple access; Single antenna interference cancellation; Algorithm; Interference (communication); Wireless; Artificial intelligence; Compressed sensing; Telecommunications","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":[0.0005271151,0.0001982781,0.0001871449,0.0003466131,0.0005434243,0.00068415,0.003083437,0.0001319399,0.000005573587],"category_scores_gemma":[0.00006513171,0.0002100706,0.00006310733,0.0003204892,0.0001836304,0.0006587946,0.0004587711,0.0003363998,0.00004532461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000160874,"about_ca_system_score_gemma":0.00009988498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004464762,"about_ca_topic_score_gemma":0.0006606241,"domain_scores_codex":[0.9982159,0.0002338625,0.0004771846,0.0003889675,0.0004137738,0.00027035],"domain_scores_gemma":[0.9963393,0.0005419811,0.0002634737,0.001870332,0.0009011184,0.00008377677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000458347,0.0004764989,0.00009722844,0.00002210348,0.0000532265,6.301199e-7,0.0001725814,0.01406338,0.003047863,0.9384592,0.001227373,0.04233404],"study_design_scores_gemma":[0.0008848825,0.00005307425,0.0007010546,0.000077767,0.000005943393,0.00000994938,0.00004985384,0.9898003,0.0007342299,0.003197982,0.004275308,0.000209693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00493809,0.0002010886,0.9832471,0.005861731,0.0005319084,0.0008071851,0.00005529107,0.0002104602,0.004147172],"genre_scores_gemma":[0.9646754,0.0003747581,0.03206008,0.00008250673,0.0001016627,0.0007641468,0.0001408539,0.00002114956,0.001779432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9757369,"threshold_uncertainty_score":0.8566431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1403340826500043,"score_gpt":0.3741060192535802,"score_spread":0.2337719366035759,"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."}}