{"id":"W1942683519","doi":"10.1109/icassp.2001.940458","title":"Robust blind multiuser detection against CDMA signature mismatch","year":2001,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Multiuser detection; Detector; Computer science; Code division multiple access; Signature (topology); Channel (broadcasting); Second-order cone programming; Convex optimization; Algorithm; Robustness (evolution); Optimization problem; Distortion (music); Mathematics; Regular polygon; Bandwidth (computing); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001498383,0.0005805942,0.0009032105,0.0004016663,0.0002576006,0.000824219,0.0006032228,0.0008320541,0.0009153661],"category_scores_gemma":[0.004247886,0.0002563645,0.0003541434,0.0005192677,0.0006687454,0.0009271191,0.0008553593,0.0009303759,0.0005404812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003621153,"about_ca_system_score_gemma":0.001008403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006001382,"about_ca_topic_score_gemma":0.0003617612,"domain_scores_codex":[0.9987418,0.0004031137,0.00004762492,0.0001663588,0.0005540446,0.00008702178],"domain_scores_gemma":[0.9990395,0.0004092035,0.0001440489,0.000120049,0.0002400542,0.00004699384],"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":[0.0005118439,0.0001281966,0.0008139596,0.0001748881,0.00007952238,0.000163874,0.00008601404,0.6000015,0.0969369,0.06915082,0.002363246,0.2295891],"study_design_scores_gemma":[0.00001697101,0.00005974805,0.0001269918,0.000005537711,0.0000045859,0.00006661531,0.000006115977,0.9757087,0.01454434,0.008783802,0.0006634277,0.0000130169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0114949,0.0001211124,0.987125,0.00007288889,0.00001956429,0.00001713394,0.00002241378,0.0001841352,0.0009429394],"genre_scores_gemma":[0.5232426,0.0003373329,0.4728641,0.0001547356,0.00005990961,0.00009538123,0.0001384572,0.00005833613,0.003049168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001498383,"threshold_uncertainty_score":0.007924318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05410436714482784,"score_gpt":0.2792326870387533,"score_spread":0.2251283198939255,"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."}}