{"id":"W1998143234","doi":"10.3390/info6010003","title":"The Kalman Filtering Blind Adaptive Multi-user Detector Based on Tracking Algorithm of Signal Subspace","year":2015,"lang":"en","type":"article","venue":"Information","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Multiuser detection; Detector; Subspace topology; Code division multiple access; Kalman filter; Computer science; Interference (communication); Algorithm; Signal subspace; SIGNAL (programming language); Detection theory; Tracking (education); Control theory (sociology); Artificial intelligence; Telecommunications; Noise (video)","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":[0.0008977789,0.0005595042,0.001197676,0.0007569747,0.0005667299,0.0007890678,0.0009185524,0.001012019,0.001117192],"category_scores_gemma":[0.002108102,0.000321095,0.0007523585,0.001125018,0.0006745787,0.001770093,0.0006764518,0.001007623,0.0007203423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004949982,"about_ca_system_score_gemma":0.001040897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001908184,"about_ca_topic_score_gemma":0.001626715,"domain_scores_codex":[0.9989618,0.0002257602,0.00005604479,0.000203618,0.0004840525,0.0000685963],"domain_scores_gemma":[0.9992974,0.0002506269,0.00006983661,0.00006914579,0.0002851689,0.00002780272],"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":[0.0004558725,0.0001680589,0.003099982,0.0005405863,0.0002943841,0.0001786145,0.0002368214,0.1081595,0.09078749,0.0697219,0.005409951,0.7209469],"study_design_scores_gemma":[0.00005349816,0.0002513165,0.0008725717,0.00002408248,0.00006907737,0.0003585606,0.00002458906,0.9439697,0.03511922,0.01061165,0.008542205,0.0001035564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002048772,0.0004610747,0.9963761,0.00004028319,0.00005122842,0.00002402544,0.00001907251,0.000332106,0.0006473199],"genre_scores_gemma":[0.2637534,0.002100751,0.7276227,0.0002874703,0.000179423,0.0001966088,0.0002530827,0.00009138251,0.005515017],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001908184,"threshold_uncertainty_score":0.004747987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07483947875312898,"score_gpt":0.3031580228323746,"score_spread":0.2283185440792456,"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."}}