{"id":"W4315630164","doi":"10.1109/globecom48099.2022.10001418","title":"Computation Offloading and Energy Harvesting Schemes for Sum Rate Maximization in Space-Air-Ground Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computation offloading; Lyapunov optimization; Energy consumption; Base station; Edge device; Distributed computing; Computation; Maximization; Real-time computing; Server; Cloud computing; Computer network; Edge computing; Enhanced Data Rates for GSM Evolution; Mathematical optimization; Telecommunications; Electrical engineering","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":[0.000493509,0.0007800834,0.0008454927,0.0002932962,0.0004537319,0.0008230528,0.0008369887,0.0004838818,0.001325228],"category_scores_gemma":[0.0008935291,0.0002537087,0.0004123442,0.0005749037,0.0006393535,0.0007924074,0.001019008,0.0006551286,0.0001884744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006968151,"about_ca_system_score_gemma":0.0007093945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003348989,"about_ca_topic_score_gemma":0.003372096,"domain_scores_codex":[0.9997498,0.0000837693,0.000009011324,0.00004413612,0.00005402861,0.00005923693],"domain_scores_gemma":[0.999726,0.0001535228,0.00003467166,0.00002152088,0.00004162297,0.00002265892],"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":[0.00003623229,0.00001909821,0.0001451814,0.00003200781,0.00001169907,0.00004183568,0.00003676278,0.9799212,0.00132896,0.006918818,0.0005715398,0.01093676],"study_design_scores_gemma":[0.00000143388,0.000007220411,0.00002153642,0.000001017525,0.000001598228,0.000003584939,0.000004914304,0.9986369,0.0001027994,0.001138769,0.00007917154,0.000001122991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03954469,0.0005531482,0.9542927,0.0002263417,0.00005542999,0.00003148057,0.00003650613,0.0001506281,0.005109083],"genre_scores_gemma":[0.9679676,0.0004448677,0.02896468,0.00006322884,0.00002913731,0.00005503826,0.00003491559,0.00003379429,0.002406548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003348989,"threshold_uncertainty_score":0.006658971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02403014575021357,"score_gpt":0.2495963729410661,"score_spread":0.2255662271908526,"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."}}