{"id":"W3082613602","doi":"10.1109/tvt.2020.3021012","title":"Joint Scheduling and Precoding for mmWave and Sub-6GHz Dual-Mode Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"China Scholarship Council; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Precoding; Computer science; Scheduling (production processes); Transmission (telecommunications); Physical layer; Upper and lower bounds; Electronic engineering; Joint (building); Channel (broadcasting); Computer network; Wireless; Engineering; Telecommunications; MIMO; 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":[0.0007409909,0.0008308697,0.000659554,0.0003362492,0.0004034698,0.0008260963,0.0007401973,0.000483187,0.001343323],"category_scores_gemma":[0.002020851,0.0004237969,0.0003594094,0.0005598526,0.0004872411,0.001060622,0.001100474,0.0008476288,0.0003053792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006036372,"about_ca_system_score_gemma":0.001354055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002465365,"about_ca_topic_score_gemma":0.004216339,"domain_scores_codex":[0.9993963,0.0001935077,0.00002931711,0.00009707842,0.0001430663,0.0001407148],"domain_scores_gemma":[0.9993011,0.0002958666,0.0001267527,0.00009612447,0.0001288042,0.000051394],"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":[0.0003714877,0.0001134998,0.001197791,0.0001523371,0.00006335946,0.0002029127,0.0002059246,0.7789321,0.02028804,0.04266376,0.003200736,0.1526081],"study_design_scores_gemma":[0.00001056803,0.00004460322,0.0001094182,0.000005074184,0.000007214415,0.00003708356,0.00002431018,0.9910609,0.002074947,0.005957714,0.0006605575,0.000007714976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02623339,0.0004099124,0.9703321,0.0001792027,0.00005145311,0.00002715909,0.00004703908,0.0001310866,0.002588599],"genre_scores_gemma":[0.7877808,0.0005816743,0.2072258,0.0001364253,0.00009310987,0.0001180082,0.000127865,0.00003949565,0.003896953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002465365,"threshold_uncertainty_score":0.004902005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293028265975224,"score_gpt":0.2183636207545873,"score_spread":0.1954333380948351,"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."}}