{"id":"W2138519663","doi":"10.1109/glocom.2005.1578495","title":"MMSE based turbo equalization for chip space-time block coded downlink CDMA","year":2005,"lang":"en","type":"article","venue":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Turbo; Turbo code; Telecommunications link; Equalization (audio); Electronic engineering; Fading; Block code; Decoding methods; Turbo equalizer; Code division multiple access; Multipath propagation; Algorithm; Computer network; Channel (broadcasting); Concatenated error correction code; 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":[0.0004307576,0.000333274,0.0003442997,0.0003113176,0.0003518226,0.0004197457,0.0004719572,0.0005428112,0.001560904],"category_scores_gemma":[0.00173923,0.0002244326,0.0002923295,0.0003585105,0.0003888762,0.0005821688,0.0003800919,0.0005011848,0.0006821459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003906521,"about_ca_system_score_gemma":0.0008652696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002416964,"about_ca_topic_score_gemma":0.005204862,"domain_scores_codex":[0.999671,0.00008559971,0.0000185662,0.00003191242,0.0001537268,0.00003925482],"domain_scores_gemma":[0.999356,0.0002859874,0.00005234869,0.00007248625,0.0002176603,0.000015635],"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":[0.0004807664,0.00009376176,0.001512266,0.0001693726,0.00009509988,0.0003364868,0.0001833264,0.5986806,0.1245107,0.03882224,0.003138277,0.2319771],"study_design_scores_gemma":[0.000008342167,0.00006278089,0.000168094,0.000007046804,0.00001312266,0.00007970334,0.000007677392,0.9755276,0.02197541,0.001226263,0.000912444,0.00001144491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03466134,0.0003049171,0.9611724,0.0001131562,0.000052592,0.00002972505,0.00003493847,0.0004793232,0.003151627],"genre_scores_gemma":[0.7596295,0.0005374177,0.2318184,0.0001420661,0.00006492152,0.00006128537,0.00009410627,0.00004618973,0.007606182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002416964,"threshold_uncertainty_score":0.005221725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302499572550069,"score_gpt":0.2806043566591221,"score_spread":0.2575793609336214,"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."}}