{"id":"W1968317326","doi":"10.1109/isit.2014.6875062","title":"A combined underlay and interweave strategy for cognitive radios","year":2014,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Underlay; Cognitive radio; Interference (communication); Computer science; Channel (broadcasting); Mathematical optimization; Gaussian; Convex optimization; Regular polygon; Computer network; Telecommunications; Wireless; Signal-to-noise ratio (imaging); Mathematics","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":[0.0002038207,0.0001190578,0.0001654171,0.00005617784,0.0001142348,0.0002078275,0.0001329342,0.00003946882,0.000007385463],"category_scores_gemma":[0.00005507803,0.0001023554,0.00004578541,0.0001125317,0.00005495149,0.0001894336,0.00007231974,0.00007087387,0.000005036815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001367216,"about_ca_system_score_gemma":0.00001904743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001164473,"about_ca_topic_score_gemma":0.00003970781,"domain_scores_codex":[0.9991711,0.00005059341,0.0001307738,0.0003244055,0.0000793978,0.0002437315],"domain_scores_gemma":[0.9990698,0.0005861333,0.00004090908,0.0001275595,0.00008606523,0.00008953087],"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":[0.00004035257,0.00004147995,0.000190635,0.00001215896,0.00004675674,0.000004223025,0.000354711,0.00001462879,0.0001851955,0.4693371,0.0006384086,0.5291343],"study_design_scores_gemma":[0.002083998,0.0009077925,0.002710324,0.00006093134,0.0000185423,0.00003686426,0.0001854896,0.9435986,0.001327783,0.04815162,0.0006171002,0.0003010123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02561341,0.00003569842,0.9594718,0.0006285355,0.0001189121,0.0002465394,0.00000119079,0.000108557,0.01377541],"genre_scores_gemma":[0.9913666,0.000008670817,0.007453802,0.0007429233,0.0001016819,0.000007559809,0.000002597142,0.000008222768,0.0003079053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9657533,"threshold_uncertainty_score":0.4173931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045170759523211,"score_gpt":0.2557573501780138,"score_spread":0.2353056425827817,"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."}}