{"id":"W2120694247","doi":"10.1109/icc.2007.465","title":"Linear Precoding for Multiuser MIMO-OFDM Systems","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Precoding; Orthogonal frequency-division multiplexing; MIMO-OFDM; Subcarrier; MIMO; Computer science; Telecommunications link; Interpolation (computer graphics); Algorithm; Transmitter; Decoding methods; Telecommunications; Beamforming; Frame (networking); Channel (broadcasting)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002371576,0.0001123209,0.0001331584,0.00007799285,0.00004025154,0.00001824951,0.00006880408,0.00008320036,0.0000117529],"category_scores_gemma":[0.00004071003,0.0001094205,0.00003669693,0.0001012801,0.000005645864,0.0001668468,0.000008209267,0.00005069287,0.00003639278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007813822,"about_ca_system_score_gemma":0.00000344785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001125511,"about_ca_topic_score_gemma":0.00002360651,"domain_scores_codex":[0.9992785,0.000004247268,0.0002783891,0.0001253811,0.00005889781,0.0002545295],"domain_scores_gemma":[0.9995891,0.0001076135,0.00002727407,0.0001528805,0.00006361362,0.00005946662],"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":[0.000006605904,0.00000673244,0.0001222788,0.0002029099,0.00002087977,8.97263e-7,0.0001108577,0.9886881,0.006967864,0.00145829,0.0007663153,0.001648291],"study_design_scores_gemma":[0.0003042167,0.00001530088,0.00002132485,0.00004567726,0.000007311421,0.000004727528,0.0002096724,0.9723924,0.01510622,0.00001987552,0.01168339,0.000189853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002457755,0.0002197313,0.987544,0.000004980263,0.00101245,0.000586674,0.00000554145,0.0006906834,0.007478201],"genre_scores_gemma":[0.8376429,0.00001266458,0.1591313,0.00001168148,0.000426659,0.00006151474,0.00001734141,0.00007531914,0.002620661],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8351852,"threshold_uncertainty_score":0.4462039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779680663438277,"score_gpt":0.2567304415093601,"score_spread":0.2389336348749774,"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."}}