{"id":"W4413180892","doi":"10.1109/isscs66034.2025.11105696","title":"Blind Channel Equalization Using MCMA Algorithm with Adam Optimization","year":2025,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Blind equalization; Computer science; Channel (broadcasting); Algorithm; Equalization (audio); 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":[0.001340542,0.001238014,0.001271524,0.0006064452,0.0006473301,0.001099057,0.001365905,0.001588776,0.004243656],"category_scores_gemma":[0.00412657,0.0008478286,0.0009736685,0.0006825002,0.001048564,0.00126738,0.001328262,0.002470654,0.001605152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006743252,"about_ca_system_score_gemma":0.001756075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003861248,"about_ca_topic_score_gemma":0.004093797,"domain_scores_codex":[0.9991868,0.0003631909,0.00004046225,0.0001339888,0.0001943302,0.00008119837],"domain_scores_gemma":[0.9986713,0.0008337532,0.00008541956,0.0001079018,0.0002494713,0.00005207301],"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":[0.0001432889,0.00002956134,0.000545004,0.0001300705,0.0001092427,0.0001127249,0.00008415734,0.8785977,0.004609754,0.02353069,0.004075133,0.08803269],"study_design_scores_gemma":[0.000007371247,0.000007422311,0.0000324545,0.000004261624,0.000003873938,0.00001633129,0.000003247835,0.9949108,0.000872239,0.003222292,0.0009129081,0.000006875621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001825736,0.0001756597,0.9961947,0.0001332817,0.00004122578,0.00002322631,0.00003271713,0.0004976445,0.001075837],"genre_scores_gemma":[0.1611505,0.0005358002,0.8305917,0.0002705708,0.0001494473,0.0002465366,0.000286867,0.0003374399,0.006431148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004243656,"threshold_uncertainty_score":0.0141964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02407584099937322,"score_gpt":0.2960537328296441,"score_spread":0.2719778918302709,"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."}}