{"id":"W2099120675","doi":"10.1109/glocom.2005.1577846","title":"Optimal packet scheduling over correlated Nakagami-m channels with different diversity-combining techniques","year":2005,"lang":"en","type":"article","venue":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nakagami distribution; Computer science; Fading; Network packet; Scheduling (production processes); Markov process; Bit error rate; Markov decision process; Markov chain; Transmission (telecommunications); Mathematical optimization; Channel (broadcasting); Algorithm; Mathematics; Computer network; Telecommunications; Statistics","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":[0.00168104,0.0009050071,0.001005792,0.0004271287,0.0004045858,0.001202367,0.0006597998,0.0006762245,0.0007940585],"category_scores_gemma":[0.003128041,0.0006176964,0.0004864621,0.0008930145,0.0008213079,0.001030285,0.0007064258,0.0005662336,0.0001299941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001645596,"about_ca_system_score_gemma":0.001705203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003434467,"about_ca_topic_score_gemma":0.002701708,"domain_scores_codex":[0.9992705,0.0003292619,0.0000236063,0.00007743436,0.0001248469,0.0001743511],"domain_scores_gemma":[0.9979483,0.001390816,0.0003048065,0.00007461631,0.0001814622,0.0001000145],"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":[0.00005326573,0.00002177723,0.0002333342,0.00002612996,0.00001748768,0.00002987201,0.00002032313,0.9907336,0.00120888,0.004659808,0.0001437393,0.002851757],"study_design_scores_gemma":[0.00001310326,0.00003127732,0.0001101729,0.000002051951,0.00000896183,0.000005939852,0.000008480157,0.9974746,0.0004775813,0.001811873,0.00005147904,0.000004362564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2089213,0.001266358,0.7848046,0.00048385,0.00008268092,0.00006894605,0.0001172562,0.0001269266,0.004128186],"genre_scores_gemma":[0.9624623,0.0005278343,0.03584804,0.00005416788,0.00004246515,0.00004413529,0.00003505716,0.00002861746,0.0009573855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003434467,"threshold_uncertainty_score":0.0119397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01482366636108302,"score_gpt":0.2399280109414401,"score_spread":0.2251043445803571,"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."}}