{"id":"W2786430524","doi":"10.1109/pimrc.2017.8292774","title":"Optimizing power allocation in mission critical cognitive radio networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Cognitive radio; Computer science; Underlay; Heuristic; Resource allocation; Distributed computing; Power (physics); Interference (communication); Power budget; Spectral efficiency; Mathematical optimization; Computational complexity theory; Transmission (telecommunications); Computer network; Power control; Algorithm; Signal-to-noise ratio (imaging); Wireless; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007944059,0.000821499,0.0009242288,0.0004867779,0.0004302535,0.0009261721,0.0007689089,0.0009081937,0.000795863],"category_scores_gemma":[0.002335344,0.000476255,0.0002595378,0.0005483893,0.0008724988,0.0006967696,0.0006743682,0.0005700081,0.0001214439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008902276,"about_ca_system_score_gemma":0.001183138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003637366,"about_ca_topic_score_gemma":0.002669491,"domain_scores_codex":[0.9994819,0.000229081,0.00001529717,0.00007035921,0.0001002885,0.0001031976],"domain_scores_gemma":[0.999271,0.0005063763,0.00009178188,0.00002726201,0.00006306368,0.0000405948],"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":[0.00004008347,0.00001883949,0.000136275,0.00002870645,0.00001260562,0.00002670631,0.00001912077,0.9867585,0.0007598962,0.002563146,0.0002210488,0.009415005],"study_design_scores_gemma":[0.00001143951,0.00002425653,0.00005280276,0.000002842898,0.000004310674,0.00001134415,0.00001168821,0.9962425,0.0002864274,0.00318992,0.0001597022,0.000002773784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07038339,0.001024806,0.9239447,0.000305308,0.0000533941,0.0000565993,0.00003831877,0.0001733163,0.004020161],"genre_scores_gemma":[0.9277908,0.0003832083,0.07042468,0.0001010336,0.00002846341,0.00006543903,0.00002807683,0.00003156941,0.001146846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003637366,"threshold_uncertainty_score":0.007232428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193510203381597,"score_gpt":0.2919376825553672,"score_spread":0.2700025805215512,"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."}}