{"id":"W2419144360","doi":"10.1109/wts.2016.7482052","title":"Resource management in OFDMA heterogeneous network","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kensington Health","funders":"European Social Fund; National Technical University of Athens; European Commission","keywords":"Orthogonal frequency-division multiple access; Computer science; Channel state information; Resource allocation; Resource management (computing); Interference (communication); Frequency-division multiple access; Radio resource management; Heterogeneous network; Computer network; Orthogonal frequency-division multiplexing; Channel (broadcasting); Distributed computing; Telecommunications; Wireless network; Wireless","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":[0.000509432,0.0002854077,0.0003381236,0.000193297,0.0004734218,0.000640668,0.0005225577,0.0003175036,0.0008194161],"category_scores_gemma":[0.0008880859,0.0000992195,0.0002052083,0.0002854131,0.0003977382,0.0007155249,0.0005007147,0.0002199425,0.00009844294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006245361,"about_ca_system_score_gemma":0.0004422724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003558537,"about_ca_topic_score_gemma":0.002158847,"domain_scores_codex":[0.9997585,0.00007816584,0.000006225946,0.00004462965,0.00004489407,0.00006749862],"domain_scores_gemma":[0.9997024,0.0001446019,0.00005307404,0.00002882433,0.00004115584,0.00002997968],"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":[0.00008094894,0.00005444739,0.0009512312,0.00002173728,0.00003260682,0.000215173,0.00003130971,0.9741677,0.0049412,0.006717799,0.0003254924,0.01246039],"study_design_scores_gemma":[0.000007676656,0.00003994513,0.0003918883,0.000001271905,0.000007920008,0.000031721,0.00002079191,0.9969304,0.0007065401,0.001600431,0.0002576233,0.000003736305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5052409,0.0008452921,0.4842606,0.0002432913,0.00007648505,0.00006279661,0.000100144,0.0001744108,0.008995994],"genre_scores_gemma":[0.9920765,0.00008398351,0.007113029,0.00002250253,0.000008822543,0.00001689478,0.00001587018,0.000006468983,0.000655895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003558537,"threshold_uncertainty_score":0.007075667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004552831958689042,"score_gpt":0.1827461767716473,"score_spread":0.1781933448129582,"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."}}