{"id":"W1969403444","doi":"10.1002/ett.2853","title":"QoS‐based power allocation for cognitive radios with AMC and ARQ in Nakagami‐<i>m</i> fading channels","year":2014,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fading; Nakagami distribution; Computer science; Quality of service; Computer network; Automatic repeat request; Network packet; Hybrid automatic repeat request; Interference (communication); Link adaptation; Electronic engineering; Engineering; Telecommunications link; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001515619,0.000214025,0.0002087887,0.0005380183,0.0002836479,0.00003370156,0.0002687593,0.0001456802,0.000004700245],"category_scores_gemma":[0.00005710317,0.000226661,0.00003409717,0.0007391748,0.0001496225,0.0002438601,0.000006765563,0.0003510099,0.000002381051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001027262,"about_ca_system_score_gemma":0.00001326145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004786307,"about_ca_topic_score_gemma":0.00007896269,"domain_scores_codex":[0.9991052,0.00003567848,0.0002591574,0.0002387408,0.00008606581,0.0002751852],"domain_scores_gemma":[0.9988289,0.0004645985,0.0000622877,0.000535904,0.00008418083,0.0000240732],"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":[0.00002445806,0.0000581469,0.00004434753,0.00002891026,0.0000293385,1.513732e-7,0.0002172123,0.9395355,0.0003254382,0.0006113718,0.00001491502,0.05911019],"study_design_scores_gemma":[0.001163284,0.0001691511,0.0001405687,0.0002987796,0.00004022986,0.000004329859,0.001247486,0.9808694,0.01395591,0.0006283892,0.001086407,0.0003960689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02516844,0.0002884967,0.9706652,0.00100493,0.00006982371,0.0006295599,0.00001046209,0.001880432,0.0002826979],"genre_scores_gemma":[0.907428,0.000640662,0.09096465,0.00003193272,0.000005990581,0.000826064,0.00003432747,0.00005501823,0.00001339362],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8822595,"threshold_uncertainty_score":0.9242966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009056494665053668,"score_gpt":0.2298670071232176,"score_spread":0.220810512458164,"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."}}