{"id":"W4379528744","doi":"10.1109/tvt.2023.3283306","title":"Joint Mode Selection and Resource Allocation for D2D and Femtocell Users in Dense Heterogeneous Networks with Full Frequency Reuse","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Femtocell; Computer science; Resource allocation; Iterative method; Convex optimization; Power control; Computational complexity theory; Overhead (engineering); Throughput; Frequency allocation; Heterogeneous network; Transmitter power output; Heuristic; Telecommunications link; Wireless network; Channel (broadcasting); Algorithm; Power (physics); Computer network; Wireless; Mathematics; Transmitter; Regular polygon; Base station; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0000855674,0.0001682925,0.0001825231,0.0005695104,0.0001015537,0.00001568109,0.00006738429,0.0002934417,8.843019e-7],"category_scores_gemma":[0.000006577897,0.000180747,0.00002215127,0.0007068156,0.00004869455,0.00009484251,0.000001688065,0.0002443853,0.00000186538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000117323,"about_ca_system_score_gemma":0.000008801441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002203699,"about_ca_topic_score_gemma":0.0006036723,"domain_scores_codex":[0.9991618,0.00002069616,0.000216127,0.0002916343,0.00005822651,0.0002515124],"domain_scores_gemma":[0.9995977,0.0000332056,0.00003749187,0.0002493628,0.00004384177,0.00003840208],"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":[0.00003400552,0.00001374162,0.00004554599,0.00004924118,0.00002975807,0.000007212824,0.00008608476,0.9765284,0.02021813,0.00002787034,0.000009740515,0.002950242],"study_design_scores_gemma":[0.0006355167,0.0002197004,0.00001724493,0.00006390512,0.000028872,0.0001229115,0.00009872528,0.9704969,0.02782433,0.0002331162,0.00006986225,0.000188934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3779736,0.0001044211,0.6207517,0.0001028574,0.00004262062,0.0004263928,0.000004151212,0.000590629,0.000003602822],"genre_scores_gemma":[0.9882722,0.0002626265,0.01096392,0.00001114886,0.00001145039,0.0003890035,0.00000963317,0.00006112102,0.00001893323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6102986,"threshold_uncertainty_score":0.737065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008600613991823172,"score_gpt":0.2070125604506704,"score_spread":0.1984119464588472,"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."}}