{"id":"W2084665924","doi":"10.1109/vetecs.2010.5494050","title":"Interference Mitigation Using Power Control in Cognitive Radio Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; Interference (communication); Power control; Computer network; Transmitter power output; Transmitter; Radio resource management; Radio spectrum; Quality of service; Telecommunications; Electronic engineering; Power (physics); Engineering; Wireless; Wireless network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003083834,0.0001509009,0.0001902151,0.0001327763,0.00008707223,0.0002032153,0.0002546484,0.00009741992,0.00008497399],"category_scores_gemma":[0.00006551101,0.0001416925,0.00005470882,0.0004156753,0.00007609813,0.0004889236,0.00007218899,0.0004539039,0.000007941301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000349908,"about_ca_system_score_gemma":0.00005025953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006768324,"about_ca_topic_score_gemma":0.0004326478,"domain_scores_codex":[0.9988077,0.0000746526,0.0002387885,0.0003833692,0.0001314179,0.000364038],"domain_scores_gemma":[0.9992169,0.0002954854,0.00007360719,0.0002038308,0.0001172674,0.00009293504],"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":[0.0003630539,0.0008172627,0.1138176,0.00001948533,0.0002790345,0.0009158645,0.006927456,0.01478973,0.07347049,0.2560417,0.001010803,0.5315475],"study_design_scores_gemma":[0.000791051,0.0000444637,0.01427506,0.00006157773,0.000005575604,0.00006538053,0.00005204637,0.9828574,0.000452011,0.001160561,0.00002669385,0.0002081718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2887171,0.00003536514,0.7062367,0.0001711749,0.0005235485,0.0001450084,5.135694e-7,0.00006582969,0.00410478],"genre_scores_gemma":[0.9925383,0.000004339226,0.006604615,0.000672315,0.0001323389,0.000001805945,0.000001358564,0.000008819768,0.0000361398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9680677,"threshold_uncertainty_score":0.5778053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224758201080562,"score_gpt":0.2480718546137319,"score_spread":0.2358242726029263,"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."}}