{"id":"W2292895141","doi":"10.1109/glocom.2015.7417366","title":"A Local Search Algorithm for Resource Allocation for Underlaying Device-to-Device Communications","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University; Queen's University","funders":"","keywords":"Computer science; Resource allocation; Telecommunications link; Heuristic; Mathematical optimization; Resource management (computing); Algorithm; Cellular network; Greedy algorithm; Local search (optimization); Resource (disambiguation); Interference (communication); Distributed computing; Computer network; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009940895,0.0003918283,0.000458371,0.0001710136,0.0005700537,0.0002100538,0.003058877,0.0002488407,0.000005663901],"category_scores_gemma":[0.0002221365,0.000471038,0.0001236502,0.0008801856,0.0002554395,0.0005275289,0.0004942633,0.000311223,0.0001316021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001263006,"about_ca_system_score_gemma":0.0004334617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000292427,"about_ca_topic_score_gemma":0.001683798,"domain_scores_codex":[0.9975605,0.0002596429,0.0008026034,0.0004282694,0.000307338,0.0006416552],"domain_scores_gemma":[0.9930991,0.0005377191,0.0001627654,0.003940512,0.00179421,0.0004656984],"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":[0.00008675628,0.0004301265,0.0001871864,0.0002894116,0.0003314303,5.39324e-7,0.002503726,0.5311049,0.0005576764,0.05317347,0.05410791,0.3572268],"study_design_scores_gemma":[0.001014346,0.00009168574,0.00002693638,0.0001352738,0.00006595355,0.00001019341,0.002927144,0.8742985,0.0001717315,0.001436495,0.119345,0.0004767286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001838677,0.001426163,0.9840174,0.003009894,0.000277591,0.002652375,0.0005462701,0.0006519243,0.007234512],"genre_scores_gemma":[0.5515615,0.0001939303,0.4452336,0.0002662351,0.0000716667,0.001470954,0.0009667121,0.00007417463,0.0001612278],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5513777,"threshold_uncertainty_score":0.9997742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1407071307850266,"score_gpt":0.3676536604089858,"score_spread":0.2269465296239591,"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."}}