{"id":"W2792733010","doi":"10.48550/arxiv.1803.05641","title":"Resource Allocation in NOMA based Fog Radio Access Networks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Enhanced Data Rates for GSM Evolution; Mobile broadband; Quality of service; Cellular network; Wireless network; Radio resource management; Resource allocation; Wireless; Edge computing; Mobile edge computing; Radio access network; Noma; Base station; Distributed computing; Telecommunications; Server; Mobile station; Telecommunications link","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":[0.0003631669,0.0004606037,0.0004482399,0.0003744006,0.00116097,0.0009994588,0.0009860956,0.0005295163,0.0009259193],"category_scores_gemma":[0.0006639713,0.000207474,0.0003099071,0.0004233386,0.000626607,0.0009156631,0.0007497197,0.0003816181,0.0002042168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007757376,"about_ca_system_score_gemma":0.0008703027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007342337,"about_ca_topic_score_gemma":0.009483536,"domain_scores_codex":[0.9996114,0.0001070334,0.00001322556,0.00006484564,0.00007294825,0.0001305538],"domain_scores_gemma":[0.9998097,0.00006113444,0.00002925393,0.0000228032,0.00005147069,0.00002568071],"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":[0.0006004922,0.0001984527,0.003662168,0.0002263796,0.0001753742,0.001277275,0.0004502746,0.69358,0.02718742,0.08019322,0.009738291,0.1827106],"study_design_scores_gemma":[0.00001586146,0.00008480344,0.000618853,0.00001387135,0.00002869346,0.0001909042,0.0001251512,0.9779808,0.00191483,0.01412139,0.004881565,0.00002324587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1742635,0.005191585,0.7923595,0.0007090443,0.0004231964,0.0001413888,0.0001442202,0.0005344,0.02623319],"genre_scores_gemma":[0.973794,0.0006271709,0.02330297,0.000167408,0.00005780414,0.00004856668,0.00003123398,0.00001261312,0.001958205],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007342337,"threshold_uncertainty_score":0.0145992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06187820385391815,"score_gpt":0.1970272847473636,"score_spread":0.1351490808934454,"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."}}