{"id":"W3134701519","doi":"10.1109/jsyst.2021.3053550","title":"Achieving 5G NR mmWave Indoor Coverage Under Integrated Access Backhaul","year":2021,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backhaul (telecommunications); Subcarrier; Computer science; Computer network; Channel (broadcasting); Electronic engineering; Base station; Engineering; Orthogonal frequency-division multiplexing","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":[0.0001082793,0.0005842355,0.000210662,0.0001810669,0.000335106,0.0003929643,0.0004269866,0.0002621001,0.001072272],"category_scores_gemma":[0.0002862855,0.0001036214,0.0001704434,0.0002790662,0.0002327833,0.0005903133,0.0006168722,0.0003200895,0.0003092489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003391078,"about_ca_system_score_gemma":0.0003626288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782926,"about_ca_topic_score_gemma":0.004534913,"domain_scores_codex":[0.9997457,0.00003517968,0.000005817208,0.00004563952,0.0000770232,0.00009069884],"domain_scores_gemma":[0.9998518,0.00002870557,0.00002844521,0.00003244149,0.00004364797,0.00001485586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004391577,0.0001499191,0.00826905,0.0003165778,0.0000982607,0.0009876549,0.000285389,0.2112954,0.4356016,0.03937201,0.004409869,0.2987752],"study_design_scores_gemma":[0.00003719641,0.000560686,0.004155872,0.00002179186,0.00007175966,0.0008529837,0.0001743052,0.8476654,0.1286125,0.004917447,0.01289233,0.0000375901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1986212,0.0005806462,0.7871094,0.0001344885,0.00005058204,0.00004186262,0.0001093095,0.0008224103,0.01253004],"genre_scores_gemma":[0.956235,0.0001873676,0.04214029,0.00005846628,0.0000312135,0.00002043243,0.00006116821,0.00001646226,0.001249539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001782926,"threshold_uncertainty_score":0.003587067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03947265644349193,"score_gpt":0.2626067823520702,"score_spread":0.2231341259085783,"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."}}