{"id":"W4311414630","doi":"10.1109/lwc.2022.3210435","title":"Handling Interference in Integrated HAPS-Terrestrial Networks Through Radio Resource Management","year":2022,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Bottleneck; Radio resource management; Interference (communication); Resource management (computing); Wireless network; Subcarrier; Resource allocation; Wireless; Computer network; Distributed computing; Telecommunications; Orthogonal frequency-division multiplexing; Channel (broadcasting); Embedded system","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":[0.001043764,0.0005554539,0.0005412886,0.0002574884,0.0005064426,0.0007580948,0.0006832523,0.0005651076,0.0005934813],"category_scores_gemma":[0.001393936,0.0002287818,0.0002440145,0.0005153607,0.0007128929,0.0007508282,0.0007794159,0.0004842018,0.00008664808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007450325,"about_ca_system_score_gemma":0.0009002532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002930724,"about_ca_topic_score_gemma":0.003687715,"domain_scores_codex":[0.9995849,0.0001531788,0.000008892724,0.00006176084,0.00009140099,0.00009989728],"domain_scores_gemma":[0.9996659,0.0002108938,0.00004561769,0.00002095993,0.00003504274,0.00002160465],"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":[0.00008368665,0.00005484156,0.0006777275,0.00003024332,0.00002421714,0.00009828842,0.00005316876,0.9468746,0.003306259,0.01168199,0.0004608331,0.03665419],"study_design_scores_gemma":[0.000008337456,0.00003511613,0.00008875925,0.00000207792,0.000005754444,0.00001874259,0.00001320049,0.9955936,0.0006267807,0.003331362,0.0002730991,0.000003160632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05405511,0.0002999341,0.9428121,0.00009641701,0.0000306163,0.00003519109,0.0000138299,0.000100606,0.002556113],"genre_scores_gemma":[0.9219702,0.0001808828,0.07599398,0.00006123935,0.00003243053,0.00004722526,0.00001643139,0.00001786802,0.001679683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002930724,"threshold_uncertainty_score":0.005827367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02126205976264897,"score_gpt":0.2388482222757694,"score_spread":0.2175861625131204,"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."}}