{"id":"W4290996429","doi":"10.1109/icc45855.2022.9839170","title":"Intelligent Spectrum Sensing: An Unsupervised Learning Approach Based on Dimensionality Reduction","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cognitive radio; Artificial intelligence; Machine learning; Dimensionality reduction; Overhead (engineering); Principal component analysis; Unsupervised learning; Spectrum management; Support vector machine; Supervised learning; Reduction (mathematics); Artificial neural network; Wireless","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":[0.0007967406,0.0007096078,0.001106204,0.0008261821,0.0004264588,0.000782004,0.001129068,0.0006754408,0.0007333349],"category_scores_gemma":[0.00188612,0.0003728116,0.001001959,0.0007835795,0.0007518589,0.001250863,0.001075114,0.0009116972,0.0003215297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004670174,"about_ca_system_score_gemma":0.0007958437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001634425,"about_ca_topic_score_gemma":0.001814059,"domain_scores_codex":[0.9992123,0.0002628073,0.00004556656,0.0002143243,0.0002101751,0.00005480888],"domain_scores_gemma":[0.999324,0.0003014095,0.00009394567,0.0001215507,0.0001350425,0.0000240288],"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":[0.0001356903,0.000207163,0.001668331,0.0002100071,0.0002319649,0.0001309635,0.000309162,0.4993445,0.01709152,0.05140232,0.003818453,0.4254499],"study_design_scores_gemma":[0.000003900385,0.00002807667,0.0002305326,0.000004505769,0.00001019354,0.00003947033,0.00001038695,0.9897212,0.001197765,0.007892163,0.0008501678,0.00001165106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003565923,0.0001431265,0.9954636,0.00008569163,0.00001608465,0.00001991417,0.00001901372,0.0001743394,0.0005124033],"genre_scores_gemma":[0.3516034,0.0006575291,0.6441258,0.000224652,0.0001892164,0.0002391009,0.0002306767,0.0001080794,0.002621663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001634425,"threshold_uncertainty_score":0.004213572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1074008078944046,"score_gpt":0.3262060940322523,"score_spread":0.2188052861378477,"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."}}