{"id":"W2186519857","doi":"10.1109/tbc.2015.2492458","title":"Wireless Resource Virtualization With Device-to-Device Communication Underlaying LTE Network","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Virtualization; Computer network; Heuristic; Wireless; Underlay; Wireless network; Interference (communication); Integer programming; Cellular network; Distributed computing; Signal-to-noise ratio (imaging); Algorithm; Telecommunications; Channel (broadcasting); Cloud computing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003174217,0.0002618859,0.0002364566,0.0001773773,0.0003346134,0.0000867082,0.0002069147,0.0001117416,0.000007709213],"category_scores_gemma":[0.00001393688,0.0002835821,0.00003607475,0.001014658,0.00002857384,0.000426668,0.000002822671,0.0003073364,0.00005666227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003315393,"about_ca_system_score_gemma":0.00003640971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004151655,"about_ca_topic_score_gemma":0.0001882523,"domain_scores_codex":[0.998583,0.0001170577,0.0003975693,0.000277111,0.0002620576,0.0003632397],"domain_scores_gemma":[0.998852,0.0001835036,0.0001005678,0.0004815149,0.000180592,0.0002018564],"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":[0.00003865453,0.00001929956,0.00003229832,0.00003925971,0.00003261223,0.000001603227,0.001157619,0.982352,0.000511081,0.00003899666,0.0001531992,0.01562334],"study_design_scores_gemma":[0.0008497775,0.0001243596,0.0000193047,0.0008089049,0.00006358175,0.00003629668,0.001692922,0.989705,0.003700379,0.00002835799,0.002438128,0.000532981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01835036,0.0001280119,0.9766557,0.00005270717,0.0002586339,0.0004417998,0.00000539077,0.0008952041,0.003212187],"genre_scores_gemma":[0.9692497,0.00001676001,0.03009471,0.0001290811,0.0001020559,0.00009147697,0.00001747682,0.0001255459,0.0001731521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9508994,"threshold_uncertainty_score":0.9999616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03456701346363553,"score_gpt":0.2506732028725306,"score_spread":0.2161061894088951,"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."}}