{"id":"W2609937295","doi":"10.1049/iet-com.2016.1439","title":"Sparse inverse fast Fourier transform‐based channel estimation for millimetre‐wave vector orthogonal frequency division multiplexing systems","year":2017,"lang":"en","type":"article","venue":"IET Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Fourier transform; Computer science; Inverse; Discrete Fourier transform (general); Division (mathematics); Orthogonal frequency-division multiplexing; Channel (broadcasting); Algorithm; Multiplexing; Fractional Fourier transform; Telecommunications; Mathematics; Fourier analysis; Mathematical analysis; Arithmetic","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003572526,0.0002690888,0.0002961891,0.0001954198,0.001324562,0.000186875,0.00171655,0.0001676437,0.000008548909],"category_scores_gemma":[0.0002629831,0.0003010347,0.0001474254,0.0001186278,0.0002347829,0.0007334258,0.0001783091,0.0003540797,0.00001752494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001873551,"about_ca_system_score_gemma":0.00004714708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001021482,"about_ca_topic_score_gemma":0.0003264413,"domain_scores_codex":[0.9986621,0.00008739147,0.0005196619,0.0002152388,0.0002031916,0.0003124273],"domain_scores_gemma":[0.9945519,0.0004376204,0.0002442466,0.004417856,0.0002307691,0.0001175864],"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":[0.00007130731,0.0007651438,0.001486217,0.001059052,0.0004300076,0.000004117913,0.003585431,0.6951136,0.02484738,0.05547475,0.002711252,0.2144517],"study_design_scores_gemma":[0.0005805315,0.00003457537,0.001252279,0.0002385696,0.00002547004,0.000001925312,0.00007620922,0.988743,0.003233623,0.002635626,0.002844864,0.0003333146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004114227,0.000739678,0.989025,0.001098415,0.0002290092,0.001313084,0.000360341,0.0009261362,0.002194085],"genre_scores_gemma":[0.7439309,0.0003350582,0.2542066,0.00002480723,0.00003071731,0.0008826284,0.0004896611,0.000067852,0.00003177394],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7398167,"threshold_uncertainty_score":0.9999756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07225413859019222,"score_gpt":0.3071558590065547,"score_spread":0.2349017204163625,"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."}}