{"id":"W4318586163","doi":"10.1109/jsac.2023.3240710","title":"Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Deutsche Forschungsgemeinschaft","keywords":"Computer science; Quality of service; Leverage (statistics); Bottleneck; Virtual reality; Wireless network; Artificial intelligence; Real-time computing; Wireless; Computer network; Telecommunications; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005248839,0.0005532356,0.0006048736,0.0002254073,0.0002607924,0.0005191205,0.00119546,0.0005218894,0.002059002],"category_scores_gemma":[0.001723758,0.0002165837,0.0003351309,0.0002367677,0.0004308094,0.0009185296,0.001007437,0.0009596856,0.0005406082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004071669,"about_ca_system_score_gemma":0.0005110243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451783,"about_ca_topic_score_gemma":0.002565396,"domain_scores_codex":[0.9996437,0.00008013149,0.00001904595,0.00008177439,0.0001206271,0.00005469587],"domain_scores_gemma":[0.9995384,0.0001667858,0.00004740411,0.00007685147,0.0001212288,0.00004934416],"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":[0.0006213502,0.0002626039,0.001852413,0.0001471155,0.00006823173,0.0004233166,0.0002916117,0.5170097,0.08481245,0.01003913,0.004570302,0.3799017],"study_design_scores_gemma":[0.00001114824,0.00004930274,0.00009096155,0.000003629901,0.000004583088,0.0000548065,0.00001047444,0.9930199,0.004905492,0.001254305,0.0005869252,0.000008573809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04326875,0.0003309443,0.9516937,0.0001681404,0.00005524192,0.00006078675,0.00005762083,0.001577772,0.002786995],"genre_scores_gemma":[0.8767905,0.0001846661,0.1206001,0.0001601201,0.00003818931,0.00006782849,0.0001206658,0.00009163183,0.001946451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002451783,"threshold_uncertainty_score":0.006888092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09754646114303962,"score_gpt":0.3647560459801093,"score_spread":0.2672095848370697,"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."}}