{"id":"W1998710728","doi":"10.1109/tcomm.2014.2338856","title":"Performance Analysis of Relay-Based Cooperative Spectrum Sensing in Cognitive Radio Networks Over Non-Identical Nakagami-&lt;named-content content-type=\"math\" xlink:type=\"simple\"&gt; &lt;inline-formula&gt; &lt;tex-math notation=\"TeX\"&gt;$m$&lt;/tex-math&gt;&lt;/inline-formula&gt;&lt;/named-content&gt; Channels","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Nakagami distribution; Cognitive radio; Fading; Relay; False alarm; Computer science; Channel (broadcasting); Algorithm; Bandwidth (computing); Spectral efficiency; Topology (electrical circuits); Mathematics; Electronic engineering; Wireless; Telecommunications; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001636154,0.0008397668,0.0005890959,0.0004980604,0.0003731101,0.0009837699,0.0007844428,0.0007637765,0.0006801325],"category_scores_gemma":[0.004549017,0.0002163567,0.0004093649,0.0004674795,0.000983084,0.0006739923,0.0008822394,0.0004464006,0.0001451446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053746,"about_ca_system_score_gemma":0.0008017642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004014503,"about_ca_topic_score_gemma":0.00226331,"domain_scores_codex":[0.9990275,0.0003512296,0.00003658075,0.00016035,0.0002324477,0.000191866],"domain_scores_gemma":[0.9963659,0.002475623,0.0003834903,0.0001585515,0.0005388511,0.00007745907],"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":[0.0002969322,0.00004678482,0.00195074,0.0001378051,0.00007986094,0.0003076716,0.0002322848,0.9560393,0.01139918,0.0158965,0.0003296157,0.01328333],"study_design_scores_gemma":[0.000004560867,0.00008749778,0.0004577791,0.000006218173,0.00002052542,0.00006827676,0.00003935001,0.996601,0.001346268,0.001295479,0.00006422449,0.000008743392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5006014,0.001322266,0.4860709,0.0003298154,0.00004668493,0.00006048951,0.00009780288,0.0002391904,0.01123151],"genre_scores_gemma":[0.9968433,0.0001705112,0.00253049,0.00001759816,0.000006658966,0.00001162496,0.00001332628,0.000005469941,0.0004009149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004014503,"threshold_uncertainty_score":0.008652866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04041324042022099,"score_gpt":0.2738888044175049,"score_spread":0.2334755639972839,"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."}}