{"id":"W3164857921","doi":"10.1109/access.2021.3082862","title":"Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"National Research Council Canada","keywords":"Backhaul (telecommunications); Computer science; Reinforcement learning; Provisioning; Base station; Distributed computing; Maximization; Computer network; Cellular network; Leverage (statistics); Mathematical optimization; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002273701,0.001043581,0.00144874,0.0005728006,0.0004964574,0.001206617,0.001614644,0.001495489,0.001890595],"category_scores_gemma":[0.004130211,0.0004130944,0.0005186427,0.0006451193,0.001324127,0.001078046,0.001207626,0.001617925,0.0002200471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001683904,"about_ca_system_score_gemma":0.001770955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008723697,"about_ca_topic_score_gemma":0.005743738,"domain_scores_codex":[0.9992274,0.0003008526,0.00002334448,0.0001433264,0.0001541201,0.0001509224],"domain_scores_gemma":[0.9977858,0.001456221,0.0002586549,0.00007607842,0.00028065,0.000142576],"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":[0.00002301792,0.00003587627,0.0002834745,0.00002011435,0.00001706936,0.00003036182,0.0000178522,0.987483,0.0002253103,0.005248295,0.000265467,0.006350146],"study_design_scores_gemma":[0.000003118253,0.000007163829,0.00002081747,0.00000185711,0.000002250573,0.0000022131,0.000002081398,0.9985403,0.00002949923,0.001323834,0.00006577007,0.00000116777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02162232,0.0005586622,0.9735317,0.0003872878,0.00005252615,0.00005272494,0.00004432245,0.0001463761,0.00360407],"genre_scores_gemma":[0.9415787,0.0004083273,0.05384777,0.0001859483,0.00008887573,0.0001457631,0.00006037953,0.00003904516,0.003645123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008723697,"threshold_uncertainty_score":0.01734585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01936153507411426,"score_gpt":0.2737513067156453,"score_spread":0.254389771641531,"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."}}