{"id":"W3209282794","doi":"10.1109/ccnc49033.2022.9700647","title":"Knowledge Transfer based Radio and Computation Resource Allocation for 5G RAN Slicing","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Slicing; Resource allocation; Resource management (computing); Computation; Key (lock); Task (project management); Convergence (economics); Throughput; Distributed computing; Resource (disambiguation); Radio access network; Radio resource management; Computer network; Base station; Wireless; Algorithm; Wireless network; Telecommunications; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001314627,0.0006458336,0.000826443,0.0006153581,0.000532549,0.0008295536,0.001086929,0.0007364861,0.002774923],"category_scores_gemma":[0.002927872,0.0002404346,0.000458145,0.0006525764,0.0008366816,0.00179513,0.001196281,0.0008984939,0.0004294935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013631,"about_ca_system_score_gemma":0.001575729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004306669,"about_ca_topic_score_gemma":0.003603587,"domain_scores_codex":[0.9991197,0.0002537055,0.00005109947,0.0002026827,0.0002174512,0.000155347],"domain_scores_gemma":[0.9988303,0.0005830251,0.0001257446,0.0001706719,0.0002110343,0.00007928313],"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":[0.0003108073,0.0002063689,0.0007599274,0.0001158686,0.00008189107,0.0001608368,0.000153438,0.6615256,0.01249322,0.01786929,0.002399827,0.3039229],"study_design_scores_gemma":[0.00001217403,0.0000357349,0.0001465065,0.000005660962,0.00001040952,0.00002853938,0.00001534183,0.9903029,0.002810861,0.005969374,0.0006540085,0.00000848173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01002151,0.0002814801,0.9874572,0.00009366895,0.0000292627,0.00004474335,0.00001834723,0.0003734323,0.001680425],"genre_scores_gemma":[0.8007951,0.0002790226,0.1961049,0.0001857391,0.00006785909,0.0001254277,0.00007585362,0.00006056454,0.002305391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004306669,"threshold_uncertainty_score":0.009283066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03668118402008315,"score_gpt":0.2819790226434453,"score_spread":0.2452978386233621,"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."}}