{"id":"W2278575468","doi":"10.1109/vtcfall.2015.7391127","title":"SDR Implementation of Spectrum Sensing for Wideband Cognitive Radio","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Cognitive radio; Wideband; False alarm; Computer science; Bandwidth (computing); Software-defined radio; Algorithm; Radio spectrum; Energy (signal processing); Electronic engineering; Wireless; Artificial intelligence; Telecommunications; Mathematics; Engineering; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.000334589,0.0001077821,0.0001807118,0.00008983199,0.00006540358,0.00007280816,0.0001088454,0.00002869747,0.000007859371],"category_scores_gemma":[0.00004247044,0.0001002052,0.00006808145,0.0002410753,0.00003554786,0.0002864182,0.00005395191,0.00004684044,0.00000359547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004846332,"about_ca_system_score_gemma":0.0001101197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001653058,"about_ca_topic_score_gemma":0.0003007283,"domain_scores_codex":[0.9990265,0.00004215524,0.0002303481,0.0002603219,0.0001809958,0.0002596296],"domain_scores_gemma":[0.9992274,0.0002252,0.0001071584,0.0001363772,0.000205334,0.00009851433],"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":[0.0001171579,0.00007211211,0.002540254,0.00002964231,0.0001734091,0.00003099271,0.006491119,0.0001079553,0.002981293,0.05582503,0.004526242,0.9271048],"study_design_scores_gemma":[0.01036472,0.001780684,0.006945251,0.0002344283,0.0001414576,0.0002858899,0.00829289,0.4556644,0.4325055,0.08027453,0.002383507,0.001126686],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0837479,0.00009903518,0.9119759,0.0007425692,0.0002519186,0.0003065671,0.000003717895,0.00006296002,0.00280944],"genre_scores_gemma":[0.9662286,0.000007115649,0.03334618,0.0001835843,0.0001466276,0.000001431357,0.000005391407,0.000008755015,0.00007228843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9259781,"threshold_uncertainty_score":0.4086248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03141349387119555,"score_gpt":0.2972697334937614,"score_spread":0.2658562396225659,"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."}}