{"id":"W2620481495","doi":"10.1007/s11277-017-4360-7","title":"Compressed Spectrum Sensing for Wavelet Based Cognitive Heterogeneous Network over Multipath Fading","year":2017,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Multipath propagation; Cognitive radio; Wideband; Compressed sensing; Fading; False alarm; Robustness (evolution); Wavelet; Electronic engineering; Telecommunications; Real-time computing; Algorithm; Channel (broadcasting); Wireless; Artificial intelligence","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004170354,0.0003021154,0.000385051,0.00008729425,0.004128008,0.0008895149,0.001906749,0.000110494,0.00001062283],"category_scores_gemma":[0.0001149043,0.0003292386,0.0002641721,0.0001560397,0.0003815239,0.0004087262,0.0007601682,0.0003905477,0.00001105423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001142547,"about_ca_system_score_gemma":0.0001258294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001733067,"about_ca_topic_score_gemma":0.0007639758,"domain_scores_codex":[0.9979587,0.0002094596,0.0003281678,0.0005581258,0.0002744544,0.0006710978],"domain_scores_gemma":[0.9957399,0.001418757,0.0003838588,0.002057779,0.0002304627,0.0001691848],"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":[0.0005729436,0.001192213,0.005772061,0.0001895487,0.00122495,0.0002360476,0.009243668,0.004703844,0.005187002,0.08684217,0.00274735,0.8820882],"study_design_scores_gemma":[0.001206364,0.00006593669,0.005350193,0.0002906807,0.00004428721,0.00003400741,0.00005810097,0.989631,0.0005170677,0.001089651,0.001293343,0.0004193715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08244585,0.0004931571,0.9090725,0.004448304,0.0003839929,0.0007434165,0.00007763554,0.0002469262,0.002088262],"genre_scores_gemma":[0.9484373,0.00006525524,0.05029914,0.0006926506,0.0003221855,0.00002461456,0.00006764063,0.00004016444,0.00005103982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9849272,"threshold_uncertainty_score":0.999916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04885107701876092,"score_gpt":0.3045857208026617,"score_spread":0.2557346437839008,"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."}}