{"id":"W4405099163","doi":"10.22215/etd/2024-16272","title":"Enhanced Uplink Communications in 5G Cellular Connected UAV Networks Using Machine Learning","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Base station; Telecommunications link; MIMO; Computer science; Cellular network; Computer network; User equipment; Wireless network; Reinforcement learning; Wireless; Low latency (capital markets); Real-time computing; Channel (broadcasting); Telecommunications; Artificial intelligence","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":[0.0002970689,0.0003882652,0.0003615247,0.0001851779,0.0002213495,0.0006175846,0.0002871307,0.0003718192,0.001154592],"category_scores_gemma":[0.0008775585,0.0001254705,0.0002177025,0.0003089277,0.000222136,0.0005263694,0.0003556588,0.0004520234,0.0002252385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004962265,"about_ca_system_score_gemma":0.0004008846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003375453,"about_ca_topic_score_gemma":0.003708051,"domain_scores_codex":[0.9998336,0.00005373396,0.000005158049,0.00003067917,0.00003906065,0.00003776862],"domain_scores_gemma":[0.999769,0.0001321212,0.00002318244,0.00001361697,0.00005263319,0.000009267126],"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":[0.00004952349,0.00004074064,0.0009598007,0.00003996636,0.00002238197,0.00005122092,0.00003652793,0.916836,0.002558633,0.006622155,0.0009810257,0.07180206],"study_design_scores_gemma":[0.000001096729,0.00001400794,0.0001225296,0.000003240109,0.000002130235,0.000005094825,0.000006740432,0.9984718,0.0003560607,0.0008025107,0.0002134949,0.000001312449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2126514,0.002208588,0.7658638,0.000810993,0.0001506697,0.00004647139,0.000108352,0.000319422,0.01784011],"genre_scores_gemma":[0.9499435,0.001019852,0.04340767,0.00008263701,0.00006723771,0.00002709629,0.0000855761,0.00001819997,0.005348237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003375453,"threshold_uncertainty_score":0.006711602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189996682049649,"score_gpt":0.2505201316065869,"score_spread":0.2386201647860904,"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."}}