{"id":"W2102887318","doi":"10.1109/milcom.2001.986016","title":"An improved decorrelator-based multiuser receiver","year":2002,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Additive white Gaussian noise; Detector; Multiuser detection; Decorrelation; Computer science; Code division multiple access; Synchronous CDMA; Algorithm; Interference (communication); Fading; Electronic engineering; White noise; Telecommunications; Decoding methods; Channel (broadcasting); Engineering","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":[0.0008489613,0.0006108379,0.0008032787,0.0005590199,0.0003318198,0.000741253,0.001229213,0.001179099,0.001943636],"category_scores_gemma":[0.001293958,0.0004107225,0.0004988617,0.0004669101,0.0002913029,0.0008984794,0.0005206636,0.0009678127,0.002065215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003643401,"about_ca_system_score_gemma":0.0006807412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004974448,"about_ca_topic_score_gemma":0.0009178635,"domain_scores_codex":[0.9992181,0.0001765374,0.00004167243,0.0001560965,0.0003393677,0.00006818111],"domain_scores_gemma":[0.9993169,0.0001608287,0.00005311706,0.0001224863,0.0003162201,0.00003052055],"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":[0.0007231153,0.0001969186,0.002039564,0.0003301817,0.0001918296,0.0003525082,0.0001953643,0.08418307,0.2525626,0.03477884,0.00642695,0.618019],"study_design_scores_gemma":[0.0001199178,0.0005326484,0.0009946044,0.00003842392,0.0001292287,0.001129022,0.00001543788,0.8606391,0.1002479,0.003635776,0.03242536,0.00009257996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005816732,0.0004707546,0.9912923,0.00007911406,0.00009674206,0.00003103519,0.00002666513,0.0008080014,0.001378628],"genre_scores_gemma":[0.09546227,0.0004335592,0.898369,0.0003121113,0.0001583081,0.00005610697,0.000142316,0.00007649951,0.004989841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001943636,"threshold_uncertainty_score":0.006502151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03510282079748333,"score_gpt":0.2804760730321271,"score_spread":0.2453732522346438,"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."}}