{"id":"W4254167297","doi":"10.1109/glocom.2014.7417366","title":"A Local Search Algorithm for Resource Allocation for Underlaying Device-to-Device Communications","year":2014,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University; Queen's University","funders":"","keywords":"Computer science; Resource allocation; Telecommunications link; Heuristic; Resource management (computing); Mathematical optimization; Algorithm; Cellular network; Local search (optimization); Resource (disambiguation); Greedy algorithm; Interference (communication); Block (permutation group theory); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000874506,0.0006840269,0.001109901,0.0006087988,0.0005178765,0.0007053834,0.0009412828,0.0008981515,0.003352316],"category_scores_gemma":[0.00221698,0.00037972,0.0004731655,0.0007761451,0.00060215,0.0009697293,0.0008361955,0.0008201005,0.0006716967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007459205,"about_ca_system_score_gemma":0.001261701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003733928,"about_ca_topic_score_gemma":0.004223371,"domain_scores_codex":[0.999649,0.0001453641,0.00001424743,0.00005948263,0.0000802298,0.00005164704],"domain_scores_gemma":[0.999177,0.0005797704,0.00007790143,0.00003376484,0.00009822114,0.00003318859],"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":[0.00008099759,0.00004190786,0.0002305666,0.00004474882,0.00002849273,0.00004025998,0.00004740131,0.9477755,0.001423649,0.005910727,0.001521582,0.04285417],"study_design_scores_gemma":[0.00001356575,0.00001884811,0.00002369174,0.000002675214,0.000002862754,0.000008809928,0.000007105076,0.998602,0.0001737243,0.000931003,0.0002133252,0.000002410883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009590033,0.0002308528,0.9879063,0.0001183685,0.00001848943,0.00004150731,0.00002749039,0.0003185592,0.001748366],"genre_scores_gemma":[0.4857915,0.0002906538,0.5081491,0.0002111557,0.00004531988,0.0004844149,0.0001626756,0.0001555692,0.004709575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003733928,"threshold_uncertainty_score":0.01121461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07882798558398227,"score_gpt":0.3457145059726765,"score_spread":0.2668865203886942,"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."}}