{"id":"W3088382980","doi":"10.1109/twc.2020.3024741","title":"Exploiting Diversity for Ultra-Reliable and Low-Latency Wireless Control","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canada Research Chairs; Nokia","keywords":"Computer science; Computer network; Wireless network; Wireless; Channel state information; Channel (broadcasting); Cooperative diversity; Latency (audio); Reliability (semiconductor); Spectral efficiency; Diversity gain; Transmission (telecommunications); Low latency (capital markets); Wireless sensor network; Distributed computing; Telecommunications; Fading","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":[0.0005470766,0.0003440783,0.000286085,0.0002713491,0.0004422726,0.0005472964,0.0005992482,0.0004135104,0.0005624268],"category_scores_gemma":[0.001362662,0.0001715228,0.0001844341,0.0003380107,0.0006949287,0.0008681886,0.0007708274,0.000725239,0.0001024496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004731669,"about_ca_system_score_gemma":0.0004182485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004752283,"about_ca_topic_score_gemma":0.000714888,"domain_scores_codex":[0.9996911,0.00008995129,0.00001078708,0.00003847343,0.0001268439,0.00004284546],"domain_scores_gemma":[0.9994235,0.0003550312,0.00007412647,0.00005962697,0.00006584689,0.00002185973],"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":[0.0001502412,0.00006741247,0.0006958068,0.0002032462,0.00006018625,0.0002387501,0.0002716661,0.6066473,0.07263721,0.1944283,0.001221103,0.1233787],"study_design_scores_gemma":[0.00001923936,0.0001160781,0.0001543295,0.00001389446,0.00001085211,0.00008603108,0.00002441926,0.9514713,0.004413382,0.04136069,0.002313538,0.00001621004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02775244,0.001088751,0.968061,0.0001974113,0.00003859253,0.00001501971,0.000009087176,0.00009162863,0.002746002],"genre_scores_gemma":[0.9478993,0.0006061685,0.05026804,0.0000818562,0.00008213466,0.00005272785,0.00001190904,0.00001545601,0.0009822805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0005992482,"threshold_uncertainty_score":0.003433108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05786902949113065,"score_gpt":0.267657473113518,"score_spread":0.2097884436223874,"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."}}