{"id":"W2040306673","doi":"10.1109/icc.2010.5501836","title":"An Optimal Algorithm for Wideband Spectrum Sensing in Cognitive Radio Systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cognitive radio; Narrowband; Wideband; Computer science; Interference (communication); Optimization problem; Throughput; Convex optimization; Mathematical optimization; Algorithm; Spectrum (functional analysis); Regular polygon; Electronic engineering; Computer network; Telecommunications; Wireless; Mathematics; Engineering; Channel (broadcasting)","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":[0.001054678,0.0008333073,0.0007987665,0.0005535711,0.0005106041,0.001142634,0.0009472349,0.001065132,0.002577457],"category_scores_gemma":[0.003417097,0.000443093,0.0003769878,0.0006448759,0.0007974147,0.001204737,0.001251704,0.001073153,0.0006611307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007323313,"about_ca_system_score_gemma":0.001395119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0018893,"about_ca_topic_score_gemma":0.001708705,"domain_scores_codex":[0.9992194,0.0002678498,0.00004334882,0.0001347148,0.0002416092,0.00009309821],"domain_scores_gemma":[0.9992825,0.0004439229,0.00005447468,0.00005316409,0.0001417412,0.00002420799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001271343,0.0000658184,0.0002402035,0.0001175212,0.000030926,0.00007159048,0.0001116115,0.6686575,0.004672366,0.08601233,0.003674979,0.236218],"study_design_scores_gemma":[0.0000174468,0.0000292513,0.0000338613,0.000008984982,0.000004637978,0.00003731979,0.00001000302,0.9801543,0.0005363756,0.01799477,0.001166151,0.000006951523],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001633891,0.0001397287,0.9965963,0.0000545311,0.00002599384,0.0000194498,0.000009300887,0.00009599172,0.00142481],"genre_scores_gemma":[0.2468961,0.000526075,0.7483343,0.0001471443,0.0001073676,0.0002907005,0.00009397213,0.00008851806,0.003515803],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002577457,"threshold_uncertainty_score":0.008622468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01313483825239668,"score_gpt":0.2576930912877867,"score_spread":0.24455825303539,"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."}}