{"id":"W2560891425","doi":"10.1109/tmc.2016.2645686","title":"Decoupled Uplink-Downlink User Association in Multi-Tier Full-Duplex Cellular Networks: A Two-Sided Matching Game","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Telecommunications link; Cellular network; Base station; Karush–Kuhn–Tucker conditions; Computer network; Association scheme; Mathematical optimization; Provisioning; Optimization problem; Duplex (building); Distributed computing; Algorithm; Mathematics","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":[0.000620451,0.0004262962,0.0004728349,0.0003718615,0.0002760239,0.0001055989,0.0005128145,0.0002929893,0.0001492859],"category_scores_gemma":[0.00003293326,0.0004164698,0.0002270306,0.0006266805,0.00004393344,0.0002726662,0.00001176634,0.0008472336,0.0001879859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001084552,"about_ca_system_score_gemma":0.0000409083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001304772,"about_ca_topic_score_gemma":0.0006156886,"domain_scores_codex":[0.9973901,0.0001816055,0.0008798923,0.0004783745,0.0002985517,0.0007714901],"domain_scores_gemma":[0.9972891,0.001430715,0.0001807234,0.0008191172,0.0001268867,0.0001534996],"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":[0.00001487069,0.0001621879,0.0002057252,0.00002183177,0.00008616781,0.00000484444,0.0005235271,0.9436431,0.03409021,0.00001109678,0.00004091177,0.02119554],"study_design_scores_gemma":[0.002080403,0.00003934664,0.0004539607,0.0004089388,0.00003914346,0.000005404876,0.000120586,0.9898333,0.006258319,0.000004807959,0.0002593395,0.0004964671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4790397,0.0001340446,0.5192731,0.00005481974,0.0005732088,0.0003838903,0.00001001165,0.0005167158,0.00001444515],"genre_scores_gemma":[0.9875851,0.0002107356,0.0113936,0.00006033294,0.000121327,0.0002070932,0.00001322209,0.0001401142,0.0002685212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5085453,"threshold_uncertainty_score":0.9998287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314218950799188,"score_gpt":0.2428662495530611,"score_spread":0.2297240600450693,"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."}}