{"id":"W2001178040","doi":"10.1109/tcomm.2013.020413.120457","title":"Delay-Optimal Distributed Scheduling in Multi-User Multi-Relay Cellular Wireless Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal; University of Manitoba","funders":"","keywords":"Computer science; Relay; Scheduling (production processes); Computer network; Time division multiple access; RSS; Base station; Telecommunications link; Wireless; Transmission (telecommunications); Markov process; Wireless network; Real-time computing; Distributed computing; Mathematical optimization; Telecommunications; 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":[0.0003819843,0.0002974122,0.0002896255,0.0002859549,0.001000957,0.0003016176,0.003471311,0.0001809689,0.00008348675],"category_scores_gemma":[0.00001441212,0.000317211,0.000153386,0.001399764,0.0002002862,0.0008442242,0.00008804916,0.001188636,0.0002394779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001973106,"about_ca_system_score_gemma":0.00008754737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001413213,"about_ca_topic_score_gemma":0.0009773945,"domain_scores_codex":[0.9976602,0.000598409,0.000629303,0.0004446879,0.0001974953,0.0004699604],"domain_scores_gemma":[0.9951463,0.000507186,0.0001457895,0.003707722,0.0003041568,0.0001888136],"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":[0.000007083882,0.001702552,0.0002129274,0.000006217386,0.00007160063,0.000002934219,0.001154981,0.947853,0.002340328,0.004812858,0.0001515485,0.04168396],"study_design_scores_gemma":[0.0009106508,0.00003136496,0.0008828791,0.00008370174,0.00001233162,0.000004819645,0.0001152045,0.9951904,0.000914644,0.00001325361,0.001499297,0.0003414078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0107423,0.0007613439,0.9846805,0.002451641,0.0002624588,0.0006319358,0.00001151072,0.0003401176,0.0001181533],"genre_scores_gemma":[0.8254053,0.002092725,0.1713561,0.000264277,0.00001066344,0.0004609183,0.0000287727,0.00002667313,0.0003546334],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.814663,"threshold_uncertainty_score":0.999928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05484083326797545,"score_gpt":0.2892029471408835,"score_spread":0.2343621138729081,"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."}}