{"id":"W2129559363","doi":"10.1155/2010/403936","title":"Superimposed Training-Based Joint CFO and Channel Estimation for CP-OFDM Modulated Two-Way Relay Networks","year":2010,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Deutsche Forschungsgemeinschaft","keywords":"Estimator; Carrier frequency offset; Computer science; Joint (building); Mean squared error; Algorithm; Minimum mean square error; Relay; Channel (broadcasting); Orthogonal frequency-division multiplexing; Frequency offset; Estimation; Statistics; Telecommunications; Mathematics","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":[0.001096527,0.0008932746,0.0007881018,0.0004236412,0.0003804983,0.0007681414,0.000973656,0.001094678,0.0005100724],"category_scores_gemma":[0.006863574,0.0005103764,0.000428641,0.0008719453,0.0008151857,0.00130098,0.0008383488,0.0008751529,0.0001523752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005391887,"about_ca_system_score_gemma":0.0009639403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003324412,"about_ca_topic_score_gemma":0.003331806,"domain_scores_codex":[0.9992005,0.0003020127,0.00003462734,0.0001097358,0.0002612562,0.0000918423],"domain_scores_gemma":[0.9972796,0.001878615,0.0002462319,0.0002336828,0.0003102627,0.00005153706],"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":[0.0001963151,0.00004877926,0.0009482688,0.0001412062,0.00006912814,0.0002642347,0.000131576,0.8858244,0.014358,0.01397495,0.0005245128,0.08351852],"study_design_scores_gemma":[0.000005800386,0.00003654758,0.0001384224,0.000005345768,0.00001293341,0.00006067508,0.000005650695,0.9963377,0.001935658,0.00127374,0.0001797069,0.000007863872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02276459,0.0004771285,0.9756414,0.00007621924,0.00002792205,0.00001980442,0.00001866172,0.000113709,0.0008604886],"genre_scores_gemma":[0.8831192,0.0007649274,0.1147257,0.00004984833,0.00006271743,0.00007338846,0.00005487314,0.00002301779,0.001126349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003324412,"threshold_uncertainty_score":0.006610155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07578830487838042,"score_gpt":0.3039097662474574,"score_spread":0.228121461369077,"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."}}