{"id":"W2779167347","doi":"10.1109/twc.2017.2787539","title":"Doppler Spread Estimation in MIMO Frequency-Selective Fading Channels","year":2017,"lang":"en","type":"preprint","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Air Force Office of Scientific Research; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Fading; Estimator; Algorithm; Statistics; MIMO; Channel (broadcasting); Cramér–Rao bound; Mathematics; Mean squared error; Autocorrelation; Computer science; Channel state information; Telecommunications; Wireless","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":[0.000515331,0.0005518568,0.0005337882,0.0005246031,0.000228337,0.0005236354,0.0003646491,0.0004956661,0.0003717634],"category_scores_gemma":[0.002805222,0.0002797185,0.0002503204,0.0004772214,0.0003933211,0.0006725262,0.0005926348,0.0005086607,0.0001470161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000341832,"about_ca_system_score_gemma":0.0006516172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002374538,"about_ca_topic_score_gemma":0.002177475,"domain_scores_codex":[0.9996137,0.0001008915,0.0000172147,0.0000716804,0.0001565884,0.00003997396],"domain_scores_gemma":[0.9992245,0.0005089485,0.0000907557,0.00004615687,0.0001155517,0.00001401222],"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":[0.00008566862,0.00001834002,0.001060874,0.0001092375,0.0000295029,0.0001298322,0.00006228252,0.8934146,0.01675246,0.007632511,0.0004394374,0.08026528],"study_design_scores_gemma":[0.00000561053,0.00002393012,0.0004652349,0.000008629046,0.00000661251,0.00005816393,0.00001176745,0.9930386,0.003698105,0.002317938,0.0003543818,0.00001104711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03421665,0.001112587,0.9635111,0.00009174992,0.00002377928,0.00001198224,0.00003472836,0.000179157,0.0008183027],"genre_scores_gemma":[0.8702888,0.001646692,0.1266131,0.00006878079,0.0000605536,0.00004220756,0.00009235223,0.00002506374,0.001162222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002374538,"threshold_uncertainty_score":0.004721403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03956178649774776,"score_gpt":0.3090031926703882,"score_spread":0.2694414061726404,"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."}}