{"id":"W2966510405","doi":"10.1109/twc.2019.2931977","title":"Using Bender’s Decomposition for Optimal Power Control and Routing in Multihop D2D Cellular Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Power control; Telecommunications link; Mathematical optimization; Benchmark (surveying); Base station; Underlay; Routing (electronic design automation); Interference (communication); Signal-to-noise ratio (imaging); Noise (video); Disjoint sets; Power (physics); Computer network; Mathematics; 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":[0.0001767339,0.0001880194,0.0002721373,0.000225224,0.0002213463,0.00005680852,0.0002262422,0.0001331546,0.000004309825],"category_scores_gemma":[0.000002193304,0.0002288344,0.00006172225,0.000209526,0.00003992494,0.0003259083,0.000002774881,0.0002600894,0.000007693871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334104,"about_ca_system_score_gemma":0.00001888134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000705194,"about_ca_topic_score_gemma":0.00006008379,"domain_scores_codex":[0.9989575,0.00009027592,0.0004248707,0.0002029712,0.00008482869,0.0002395129],"domain_scores_gemma":[0.9987483,0.0003173258,0.00007793328,0.0007155334,0.00008318557,0.00005771649],"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":[0.00001651583,0.00006230282,0.00005298864,0.000060961,0.00003822188,2.278065e-7,0.0004429658,0.9533542,0.04499752,0.0003554611,0.00000143828,0.0006172205],"study_design_scores_gemma":[0.001305917,0.00003230114,0.00002224856,0.0001923812,0.00003223539,0.000007307473,0.0006281194,0.9945974,0.002918399,0.000007017805,0.00003361025,0.0002230392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1259965,0.0002660314,0.8718579,0.00003228653,0.0003433825,0.001139509,0.00007304014,0.0001783656,0.0001130185],"genre_scores_gemma":[0.978269,0.00009693606,0.02132672,0.0000110652,0.00001124447,0.0001736296,0.00002122024,0.00006456538,0.00002565497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8522725,"threshold_uncertainty_score":0.9331595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02132601644260979,"score_gpt":0.2707239226339188,"score_spread":0.249397906191309,"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."}}