{"id":"W3197164486","doi":"10.1002/9781119675525.ch6","title":"Machine Learning for Resource Allocation in Mobile Broadband Networks","year":2021,"lang":"en","type":"other","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Wireless network; Radio resource management; Wireless; Resource allocation; Scalability; Computer network; Distributed computing; Wireless broadband; Multi-frequency network; Context (archaeology); Wireless WAN; Scheduling (production processes); Key distribution in wireless sensor networks; Telecommunications; 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":[0.001420899,0.0007806036,0.0008810209,0.0006246144,0.0004152419,0.001638639,0.0008083943,0.001027211,0.003084767],"category_scores_gemma":[0.004854012,0.000283582,0.0003526302,0.001426193,0.0007923764,0.001658814,0.0009275904,0.001950305,0.0009266832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029729,"about_ca_system_score_gemma":0.0008318602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0020689,"about_ca_topic_score_gemma":0.002200452,"domain_scores_codex":[0.9992562,0.0003755079,0.00002969708,0.00009699726,0.0001808667,0.00006078837],"domain_scores_gemma":[0.9986808,0.001026278,0.00006917679,0.00008536092,0.0001097983,0.000028607],"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":[0.00006188813,0.00008860925,0.0007446188,0.0003868839,0.00007821319,0.00007327156,0.00007082293,0.4687472,0.0007110534,0.2259456,0.01646069,0.2866312],"study_design_scores_gemma":[0.000005622558,0.00001589282,0.0001328479,0.00003910108,0.000006643828,0.00001949882,0.00001400323,0.8853684,0.0002922232,0.1070508,0.00704697,0.000007983831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005065907,0.02077087,0.9532599,0.003850104,0.0003666009,0.00005908761,0.0001296644,0.0004643901,0.01603349],"genre_scores_gemma":[0.5853589,0.03889626,0.3496313,0.001578016,0.0025607,0.000484384,0.0004604791,0.0002459948,0.020784],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.003084767,"threshold_uncertainty_score":0.01031959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006545977345138091,"score_gpt":0.220262644043446,"score_spread":0.2137166666983079,"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."}}