{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000143697,0.0002904118,0.0002924384,0.0004481773,0.00006932041,0.00005209015,0.001937771,0.0005190659,0.00003011078],"category_scores_gemma":[0.0000450938,0.0003924835,0.00008638427,0.0007114062,0.0001709643,0.0002423458,0.001021694,0.0008726558,0.0000220519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005051171,"about_ca_system_score_gemma":0.0000417691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003921326,"about_ca_topic_score_gemma":0.0001328597,"domain_scores_codex":[0.9988515,0.00007400572,0.0002272558,0.0004967457,0.00004798168,0.0003025458],"domain_scores_gemma":[0.9979586,0.0001313684,0.0001301099,0.001652548,0.00007011143,0.00005728766],"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":[0.00001715193,0.00002199559,0.002930513,0.00005697483,0.00002381859,0.0000185846,0.00002097526,0.9930265,0.00001347416,0.002258538,0.0004101439,0.001201343],"study_design_scores_gemma":[0.0003502886,0.00001145868,0.002600031,0.0001599481,0.00001561681,4.80725e-7,0.00005117799,0.9908423,0.0005137396,0.003574063,0.001493939,0.0003869536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1997893,0.0002835027,0.7953967,0.00007482634,0.0001925665,0.0003297598,0.000006981998,0.001386702,0.00253965],"genre_scores_gemma":[0.9979553,0.000606297,0.001116142,0.00002941564,0.0000462205,0.000006170746,0.00008216248,0.00005457897,0.0001037239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.798166,"threshold_uncertainty_score":0.9998527,"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."}}