{"id":"W2151468844","doi":"10.1109/icc.2009.5199391","title":"Rate and Power Adaptation for Increasing Spectrum Efficiency in Cognitive Radio Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computer science; Channel state information; Rayleigh fading; Quadrature amplitude modulation; Fading; Link adaptation; Cognitive radio; Transmitter; Bit error rate; Electronic engineering; Interference (communication); Channel (broadcasting); Computer network; Telecommunications; Wireless; Engineering","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":[0.001361939,0.0008558839,0.0005752759,0.0003730418,0.0003086472,0.0007664483,0.0009752366,0.0006888575,0.000654073],"category_scores_gemma":[0.004913904,0.0002838317,0.0003155864,0.0004026704,0.0009593607,0.0009610801,0.0007116124,0.0005951622,0.0001884158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005929373,"about_ca_system_score_gemma":0.0005344924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139463,"about_ca_topic_score_gemma":0.001021138,"domain_scores_codex":[0.9994599,0.0002166291,0.00001989676,0.0000683542,0.000141245,0.00009402801],"domain_scores_gemma":[0.9986612,0.0009208668,0.0001499712,0.0001032667,0.0001276239,0.00003705982],"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":[0.00008086333,0.00005948579,0.0004749874,0.00005410182,0.00003691965,0.00007243492,0.00009235191,0.9327744,0.007303112,0.02240381,0.0004115588,0.036236],"study_design_scores_gemma":[0.00001472029,0.00003889027,0.0001101874,0.000005340139,0.00001224589,0.00003332707,0.00001190429,0.9886135,0.001144527,0.009799549,0.000207305,0.000008602776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06325256,0.001055687,0.9308345,0.0002733311,0.00004643874,0.00003795175,0.00001436817,0.0001618298,0.004323416],"genre_scores_gemma":[0.9666185,0.0003443719,0.03216748,0.00007205932,0.00004190264,0.00004483658,0.000006185787,0.00002296007,0.000681709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001361939,"threshold_uncertainty_score":0.007202685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415777671751087,"score_gpt":0.2427030854336183,"score_spread":0.2285453087161075,"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."}}