{"id":"W2145752182","doi":"10.1109/mwc.2007.4396949","title":"Scheduling schemes for multimedia service in wireless OFDM systems","year":2007,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Innovation, Science and Economic Development Canada","funders":"","keywords":"Computer science; Link adaptation; Computer network; Orthogonal frequency-division multiplexing; Scheduling (production processes); Quality of service; Time division multiple access; Wireless broadband; Fairness measure; Fading; WiMAX; Maximum throughput scheduling; Proportionally fair; Wireless; Network packet; Spectral efficiency; Round-robin scheduling; Real-time computing; Throughput; Wireless network; Dynamic priority scheduling; Channel (broadcasting); Telecommunications","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":[0.000507944,0.0002755308,0.0003598256,0.0002730014,0.0002188238,0.00005780122,0.0009872771,0.0002258019,0.000002764701],"category_scores_gemma":[0.00003180102,0.0003365582,0.00006390709,0.0009583251,0.00008424102,0.0003651128,0.00009372843,0.0004214302,0.0000236726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002568097,"about_ca_system_score_gemma":0.00003910768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005800577,"about_ca_topic_score_gemma":0.0009040327,"domain_scores_codex":[0.9982541,0.00005199551,0.0007031582,0.0002550005,0.0001811357,0.0005546386],"domain_scores_gemma":[0.9972659,0.0007882274,0.0001272294,0.001433014,0.0002596854,0.0001259417],"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":[0.00002172536,0.0001072957,0.001059842,0.0002330091,0.00004829466,0.000001517058,0.0006241297,0.9599729,0.01175296,0.005247141,0.00006761524,0.02086358],"study_design_scores_gemma":[0.0007503764,0.000008355604,0.0003046528,0.0002705378,0.00001569459,0.000003677992,0.0004485513,0.9917946,0.003214497,0.0000555371,0.002774436,0.0003590141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2313603,0.001525154,0.7639285,0.0001399664,0.0006479676,0.0009554835,0.0000305392,0.0006539444,0.0007581058],"genre_scores_gemma":[0.9164499,0.0008593476,0.08178744,0.00005331586,0.0001488345,0.0003811663,0.0001710487,0.0001151348,0.00003379515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6850896,"threshold_uncertainty_score":0.9999086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864770822023927,"score_gpt":0.2826422684051674,"score_spread":0.2539945601849281,"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."}}