{"id":"W4210368370","doi":"10.1109/ojcoms.2022.3146886","title":"Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Baseband; Computer science; Radio access network; Telecommunications link; Cloud computing; Wireless; Wireless network; Resource allocation; Base station; Energy consumption; Cellular network; Real-time computing; Bandwidth (computing); Computer network; Distributed computing; Engineering; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001809386,0.0001302051,0.0001996773,0.0001131356,0.001250244,0.00009434661,0.00382339,0.00004443167,0.00002328942],"category_scores_gemma":[0.00005096939,0.0001199695,0.0002134354,0.0008163734,0.00007823515,0.000103522,0.0006435287,0.001262571,0.00000115728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006621641,"about_ca_system_score_gemma":0.0001076176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001807605,"about_ca_topic_score_gemma":0.00004457869,"domain_scores_codex":[0.9980626,0.0007655764,0.0005959249,0.0001017667,0.0002924702,0.0001817355],"domain_scores_gemma":[0.9976208,0.000443274,0.0003028352,0.00148106,0.0000997705,0.00005226185],"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":[0.00001135688,0.0002277463,0.0001126464,0.000005030971,0.00003295381,2.299698e-7,0.001537547,0.9927918,0.0003681822,0.00008797438,0.0009723464,0.003852213],"study_design_scores_gemma":[0.0006953014,0.0000519869,0.001210748,0.00008577105,0.00002505223,0.00001272475,0.001763696,0.971433,0.00003636073,0.00002520165,0.02455275,0.0001074359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.876803,0.007079458,0.07663506,0.0202244,0.001752919,0.001581759,0.00006029951,0.000352033,0.01551107],"genre_scores_gemma":[0.9950242,0.0002562391,0.004304355,0.0002156652,0.0000182933,0.00003367868,0.00002202566,0.0000326674,0.00009285773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1182212,"threshold_uncertainty_score":0.9615991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528143695497978,"score_gpt":0.2502648905098578,"score_spread":0.2349834535548781,"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."}}