{"id":"W2125917316","doi":"10.1109/twc.2008.070044","title":"MIMO-OFDM Channel Estimation in the Presence of Frequency Offsets","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Orthogonal frequency-division multiplexing; Estimator; Minimum mean square error; MIMO-OFDM; MIMO; Frequency offset; Mean squared error; Carrier frequency offset; Mathematics; Computer science; Algorithm; Channel (broadcasting); Control theory (sociology); Statistics; Telecommunications","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":[0.0006474603,0.0005556118,0.0005878587,0.0002101302,0.0002196715,0.0003982675,0.0003118672,0.0007029035,0.0003280866],"category_scores_gemma":[0.003736545,0.0003181706,0.0002154357,0.0003370411,0.0005402646,0.0007222979,0.0004358316,0.0004200501,0.0001626215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002212547,"about_ca_system_score_gemma":0.0006743601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000870319,"about_ca_topic_score_gemma":0.001238478,"domain_scores_codex":[0.9994968,0.0002241414,0.00001774977,0.00007000112,0.0001372647,0.00005406266],"domain_scores_gemma":[0.9988168,0.0008221195,0.0001261029,0.00007038808,0.0001451411,0.00001949489],"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":[0.0002844391,0.00005104217,0.001753332,0.0002724025,0.00004529752,0.0002607408,0.0001307778,0.8246937,0.02444953,0.014767,0.00083373,0.1324579],"study_design_scores_gemma":[0.00001383417,0.00006976911,0.0003749014,0.000006913125,0.00001318717,0.00006745726,0.00001241744,0.9913751,0.00581333,0.001934047,0.0003103036,0.000008686545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02274158,0.0003527577,0.9761941,0.00007150602,0.0000221645,0.0000129156,0.00001550505,0.00008111763,0.0005082674],"genre_scores_gemma":[0.7763371,0.0007805288,0.2219098,0.00005527675,0.0001015217,0.00006561672,0.00004975593,0.00001557229,0.000684871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000870319,"threshold_uncertainty_score":0.003424108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03454678212018999,"score_gpt":0.2778795635779697,"score_spread":0.2433327814577797,"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."}}