{"id":"W4231126871","doi":"10.1002/wcm.671","title":"Analysis of multi‐user detection of multi‐rate transmissions in multi‐cellular CDMA","year":2008,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Interference (communication); Single antenna interference cancellation; Multipath propagation; Code division multiple access; Multiuser detection; Additive white Gaussian noise; Rayleigh fading; Channel (broadcasting); Range (aeronautics); Word error rate; Electronic engineering; Real-time computing; Telecommunications; Algorithm; Speech recognition; Fading","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002189646,0.0007153265,0.000651728,0.0005124027,0.0003415872,0.001208127,0.0007935094,0.0008481143,0.001344315],"category_scores_gemma":[0.009723235,0.000632544,0.0004571353,0.0004105265,0.0008575668,0.0009992879,0.0009605633,0.0007517182,0.0003499683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009803921,"about_ca_system_score_gemma":0.0007862452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00149893,"about_ca_topic_score_gemma":0.0008871234,"domain_scores_codex":[0.9981433,0.0006883208,0.00003855928,0.0001422476,0.0008087184,0.0001788373],"domain_scores_gemma":[0.9938073,0.00443462,0.0006079011,0.0002730998,0.0007745401,0.0001025484],"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":[0.0001719877,0.00004371854,0.002302839,0.0001100161,0.0000930239,0.0002689008,0.0001713099,0.9114015,0.01576647,0.04313368,0.0003692742,0.02616738],"study_design_scores_gemma":[0.000002730098,0.00001833431,0.0002249657,0.00000466625,0.000006400822,0.00005041709,0.000005819572,0.996382,0.001539863,0.001649017,0.0001109589,0.000004780452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1006927,0.0003332297,0.8939007,0.000201444,0.00002218098,0.00002622787,0.00002594432,0.0002249264,0.004572765],"genre_scores_gemma":[0.9553983,0.0002705827,0.04140412,0.00008866917,0.00004144687,0.00004999584,0.00004040941,0.00005589554,0.002650632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002189646,"threshold_uncertainty_score":0.01158011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06013624592893185,"score_gpt":0.3204271840154197,"score_spread":0.2602909380864878,"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."}}