{"id":"W3095990579","doi":"10.1109/tits.2022.3183073","title":"Energy Efficiency Optimization in LoRa Networks—A Deep Learning Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial neural network; Computer science; Train; Artificial intelligence; Range (aeronautics); Deep learning; Backpropagation; Energy (signal processing); Efficient energy use; Phase (matter); Machine learning; Engineering; Mathematics","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":[0.0007225564,0.0006043948,0.0007277234,0.0003822207,0.0002588779,0.000805743,0.000814374,0.0007783166,0.001696807],"category_scores_gemma":[0.001367935,0.000385631,0.0003889704,0.0004205824,0.0005967754,0.0009348508,0.0008426873,0.0008507675,0.0002115541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008176768,"about_ca_system_score_gemma":0.0005916872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003586621,"about_ca_topic_score_gemma":0.003646494,"domain_scores_codex":[0.999821,0.00006441884,0.000007263886,0.00003271448,0.00003850016,0.00003612467],"domain_scores_gemma":[0.9996676,0.0001917856,0.00003482864,0.00001907908,0.00007145289,0.00001524665],"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":[0.00001502924,0.00001330909,0.0001598235,0.00002091322,0.00001226698,0.0000145659,0.000008287006,0.9881424,0.0005326717,0.003153208,0.0002078936,0.007719512],"study_design_scores_gemma":[6.644235e-7,0.000003902959,0.00001785465,0.000001419157,0.000001150395,0.000002023634,0.000001678072,0.9989935,0.00007102871,0.0008484473,0.00005755592,8.995445e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05495784,0.0009498798,0.9329661,0.0005919376,0.00005271061,0.00003825961,0.0000937926,0.000260143,0.01008938],"genre_scores_gemma":[0.9563087,0.0003981409,0.03862328,0.0001771945,0.00003968552,0.00007831199,0.00008246175,0.00004313438,0.004249157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003586621,"threshold_uncertainty_score":0.007131517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0110284470219963,"score_gpt":0.2065269920487251,"score_spread":0.1954985450267288,"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."}}