{"id":"W2919806614","doi":"10.1109/glocomw.2018.8644436","title":"New MMSE Downlink Channel Estimation for Sub-6 GHz Non-Line-of-Sight Backhaul","year":2018,"lang":"en","type":"article","venue":"","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Minimum mean square error; Estimator; Backhaul (telecommunications); Non-line-of-sight propagation; Power delay profile; Computer science; Telecommunications link; Algorithm; Mean squared error; Channel (broadcasting); Wireless; Electronic engineering; Mathematics; Telecommunications; Statistics; Delay spread; Fading; Engineering","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":[0.0005875695,0.0006192927,0.0006420378,0.0004678326,0.0003046856,0.0006189399,0.0006339977,0.0004406502,0.00111235],"category_scores_gemma":[0.001808743,0.0003975631,0.0004327624,0.0003954626,0.0002490881,0.0007903046,0.0005823708,0.0006925694,0.0007466058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003375746,"about_ca_system_score_gemma":0.0009298013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002350176,"about_ca_topic_score_gemma":0.005354901,"domain_scores_codex":[0.9995322,0.00008655295,0.00002849943,0.0000701893,0.0002431773,0.00003943615],"domain_scores_gemma":[0.9993904,0.0001487984,0.00006227326,0.00007856281,0.000298876,0.00002109337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004818715,0.0001382824,0.005514998,0.0002704844,0.000262093,0.0002331746,0.0001972803,0.321059,0.1130963,0.01407405,0.004778098,0.5398943],"study_design_scores_gemma":[0.00001439749,0.0000689599,0.0009625954,0.00001114119,0.00002915066,0.0001388728,0.00002412997,0.9748251,0.02048112,0.0009494312,0.002470904,0.00002419331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00534683,0.0001199468,0.9937339,0.00002416409,0.00002134654,0.00001044513,0.00003320018,0.0002753853,0.0004348393],"genre_scores_gemma":[0.190977,0.0004340923,0.8046199,0.00005850368,0.0000648252,0.00006238741,0.0003439931,0.00007160354,0.003367701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002350176,"threshold_uncertainty_score":0.004672945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02274478607902579,"score_gpt":0.2573699289322006,"score_spread":0.2346251428531748,"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."}}