{"id":"W1547422865","doi":"10.5539/jmr.v7n3p208","title":"Optimization of Heterogeneous Network Performances Based on the Signal Interferences Noise Ration(SINR)","year":2015,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Blocking (statistics); Waypoint; Signal-to-interference-plus-noise ratio; Markov chain; Interference (communication); SIGNAL (programming language); Computer science; Noise (video); Markov process; Wireless; Wireless network; Mathematics; Computer network; Power (physics); Real-time computing; Telecommunications; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001268861,0.000966233,0.0007219576,0.0005190923,0.0003194012,0.00109412,0.0004562215,0.0006047797,0.0008026345],"category_scores_gemma":[0.002868277,0.0002324174,0.0004426166,0.0004330226,0.000727852,0.0008166785,0.0006969054,0.0004129118,0.0001661173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040448,"about_ca_system_score_gemma":0.000783126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001908728,"about_ca_topic_score_gemma":0.001208283,"domain_scores_codex":[0.9990482,0.0003559426,0.00003085852,0.0001481654,0.0002160631,0.0002008095],"domain_scores_gemma":[0.9988576,0.0006537075,0.0001958714,0.00007274297,0.0001580873,0.00006198286],"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":[0.00006037824,0.00003029617,0.0008283341,0.00002470985,0.00003556834,0.0000474804,0.00001531328,0.9844647,0.005451583,0.004334468,0.0001013139,0.004605895],"study_design_scores_gemma":[0.000004459383,0.00006424319,0.000473985,0.000004219764,0.00001567184,0.00001768328,0.00001648213,0.9954473,0.001905626,0.001915029,0.0001292405,0.000005966802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2573349,0.0005713555,0.7327202,0.0001966081,0.00003885914,0.00006279426,0.00007107051,0.0001502456,0.008853903],"genre_scores_gemma":[0.9872296,0.0001906773,0.01160249,0.00002321897,0.00001235297,0.000027905,0.00002676147,0.00002650967,0.0008603619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001908728,"threshold_uncertainty_score":0.007548988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09266344724103603,"score_gpt":0.3215154683263406,"score_spread":0.2288520210853046,"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."}}