{"id":"W3094597547","doi":"10.1109/mnet.011.2000301","title":"A Deep Learning Method for Predictive Channel Assignment in Beyond 5G Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Network","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thunder Bay Regional Research Institute; Lakehead University","funders":"","keywords":"Computer science; Relay; Deep learning; Computer network; Convolutional neural network; Node (physics); Relay channel; Channel (broadcasting); Network packet; Leverage (statistics); Spectral efficiency; Artificial intelligence; 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":[0.000525528,0.0008997225,0.0006613677,0.0003778259,0.0003215392,0.0006171042,0.001203017,0.0008009669,0.001329058],"category_scores_gemma":[0.001321092,0.0003544416,0.0003991914,0.0004634495,0.000444516,0.0009018024,0.000773414,0.00142413,0.0003245856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009023,"about_ca_system_score_gemma":0.001138292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01280902,"about_ca_topic_score_gemma":0.01312698,"domain_scores_codex":[0.9998031,0.00004234223,0.000008046765,0.00004862076,0.00004251124,0.00005529393],"domain_scores_gemma":[0.9996917,0.0001450312,0.00003293217,0.00002428226,0.00008223615,0.00002390473],"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":[0.00013087,0.000098177,0.00113513,0.00005222347,0.00004603228,0.00009102865,0.00004035856,0.8362665,0.00255824,0.004039848,0.003641073,0.1519005],"study_design_scores_gemma":[0.000001998919,0.000006698597,0.00003709896,0.000001929763,0.000002560051,0.000003321021,0.000002157311,0.9988468,0.0002492027,0.0007298507,0.0001169546,0.000001354026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06291891,0.001499187,0.9286415,0.0008776722,0.0001623519,0.00005211466,0.0002591046,0.001787708,0.003801484],"genre_scores_gemma":[0.9068372,0.0006092226,0.08555111,0.000408386,0.00009234749,0.00008023174,0.0005393153,0.00008050867,0.005801721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01280902,"threshold_uncertainty_score":0.02546889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744027244293631,"score_gpt":0.2550080964962027,"score_spread":0.2375678240532663,"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."}}