{"id":"W2550786979","doi":"10.1109/tvt.2016.2628387","title":"Low-Complexity QoS-Aware Coordinated Scheduling for Heterogeneous Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Scheduling (production processes); Quality of service; Computer science; Femtocell; Computer network; Beamforming; Distributed computing; Engineering; Base station; 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":[],"consensus_categories":[],"category_scores_codex":[0.00006974328,0.0002525991,0.0002817303,0.0003336403,0.0001688748,0.00001299226,0.0002102845,0.0004643693,0.00002890492],"category_scores_gemma":[0.000008747819,0.0002240343,0.0001199249,0.0004275372,0.0001205683,0.0001188227,0.000001569952,0.0002292818,0.00003384588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969534,"about_ca_system_score_gemma":0.00001098843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001965201,"about_ca_topic_score_gemma":0.00002637975,"domain_scores_codex":[0.99883,0.00001840475,0.0003143741,0.0003391865,0.00007107373,0.0004269145],"domain_scores_gemma":[0.9992582,0.00006360831,0.00005134739,0.0004437957,0.0001210068,0.00006203157],"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":[0.0000188979,0.0000355747,0.000002812661,0.00003896825,0.00006430819,0.000006583202,0.000005939016,0.9560139,0.02214956,0.0001183531,0.0000181497,0.021527],"study_design_scores_gemma":[0.0009930126,0.0001157259,0.000001150782,0.0001467365,0.00003778164,0.00005689414,0.00002164775,0.6659993,0.3315004,0.000418975,0.0003677176,0.000340622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02369425,0.0001291774,0.9721847,0.0002109727,0.0009347153,0.0005871651,0.00004612431,0.002200044,0.00001292043],"genre_scores_gemma":[0.9880919,0.00007433631,0.01123646,0.0000217498,0.00005762146,0.0003584223,0.000006399784,0.00009411456,0.00005892346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9643977,"threshold_uncertainty_score":0.9135855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125024489020847,"score_gpt":0.225545903284526,"score_spread":0.2130434543824412,"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."}}