{"id":"W3144443531","doi":"10.1186/s13638-021-01929-z","title":"Autoencoder-bank based design for adaptive channel-blind robust transmission","year":2021,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Block Error Rate; Channel (broadcasting); Algorithm; Transmission (telecommunications); Autoencoder; Encoder; Partition (number theory); Artificial intelligence; Telecommunications; Deep learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001035707,0.0002206598,0.0002672132,0.0001912262,0.001408034,0.0004890643,0.001086548,0.0001173444,0.000006094687],"category_scores_gemma":[0.00002650505,0.0002122119,0.0001282559,0.0006034479,0.00008358783,0.0004501428,0.000134856,0.0005060781,0.000003006424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054632,"about_ca_system_score_gemma":0.0002731823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000149869,"about_ca_topic_score_gemma":0.000004156581,"domain_scores_codex":[0.9977104,0.0007286082,0.0005264114,0.0003861366,0.0003158725,0.0003325721],"domain_scores_gemma":[0.9963903,0.001516613,0.0003938677,0.001025543,0.0004667615,0.0002069603],"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":[0.0002059655,0.0004603466,0.00008560142,0.00002418524,0.00008714766,0.00001895304,0.0009440798,0.2723915,0.002105313,0.03371217,0.0006105061,0.6893542],"study_design_scores_gemma":[0.001088637,0.0001923608,0.0003135125,0.0003518155,0.00002098046,0.00006489715,0.00004953123,0.9857392,0.0008613405,0.002946864,0.008126986,0.0002438873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006276713,0.002501113,0.9874302,0.008496656,0.0002984473,0.0003144409,0.000002321714,0.00009023255,0.0002389426],"genre_scores_gemma":[0.722102,0.001411483,0.2757756,0.0003958402,0.0001823484,0.00004984057,0.00001370536,0.00002377847,0.00004547435],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7214743,"threshold_uncertainty_score":0.999892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1561164638622924,"score_gpt":0.3012492931573836,"score_spread":0.1451328292950912,"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."}}