{"id":"W2130885995","doi":"10.1109/glocom.2010.5683464","title":"Resource Allocation and Scheduling in Multi-Cell OFDMA Decode-and-Forward Relaying Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Orthogonal frequency-division multiple access; Subcarrier; Mathematical optimization; Scheduling (production processes); Optimization problem; Relay; Resource allocation; Frequency-division multiple access; Convex optimization; Orthogonal frequency-division multiplexing; Base station; Power (physics); Algorithm; Computer network; Regular polygon; 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":[],"consensus_categories":[],"category_scores_codex":[0.000573337,0.00009718337,0.0001055698,0.0000891307,0.000187211,0.0001656651,0.0003702414,0.00007566919,0.00001186865],"category_scores_gemma":[0.00005607391,0.00009127826,0.00001503792,0.0002759904,0.00004074564,0.0003006034,0.0004683435,0.0003806301,0.000003243807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001115976,"about_ca_system_score_gemma":0.00001762081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001410737,"about_ca_topic_score_gemma":0.0004225911,"domain_scores_codex":[0.9992053,0.00007972483,0.0001968167,0.000272317,0.00007307254,0.0001727812],"domain_scores_gemma":[0.9992595,0.0001701543,0.00005324329,0.0003882692,0.00004880645,0.00007997314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001076175,0.0001498066,0.04882405,0.00001908815,0.00001177015,0.000003614759,0.003166659,0.01373088,0.01423693,0.09906492,0.0001621999,0.8206193],"study_design_scores_gemma":[0.0003917741,0.000008852452,0.008219101,0.00002317455,0.000001650426,0.00000353577,0.00004984643,0.988644,0.0003473287,0.00003818476,0.002151013,0.0001215362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3034103,0.001062664,0.6918693,0.002061319,0.00008574749,0.0001236448,6.163676e-8,0.00008822487,0.001298681],"genre_scores_gemma":[0.8820379,0.0009877083,0.1161856,0.0004676678,0.00002120102,0.0000126165,0.000001024155,0.000006164082,0.0002801425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9749131,"threshold_uncertainty_score":0.3722219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0284432962349636,"score_gpt":0.2718793036861287,"score_spread":0.2434360074511651,"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."}}