{"id":"W2147934714","doi":"10.1109/wcnc.2008.173","title":"User Assignment for MIMO-OFDM Systems with Multiuser Linear Precoding","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Precoding; Orthogonal frequency-division multiplexing; Telecommunications link; Computer science; MIMO; MIMO-OFDM; Zero-forcing precoding; Context (archaeology); Channel (broadcasting); Multi-user MIMO; Algorithm; Multiplexing; Electronic engineering; Telecommunications; Engineering","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":[0.00006548627,0.0001610263,0.0001863819,0.00005718043,0.00008077378,0.00001539174,0.00007465448,0.00006914151,0.00001636786],"category_scores_gemma":[0.00001275274,0.0001309651,0.00003088985,0.00009331206,0.00001338957,0.0002340468,0.0000093914,0.00005793458,0.00002675069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128307,"about_ca_system_score_gemma":0.00001090892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001549201,"about_ca_topic_score_gemma":0.00001111338,"domain_scores_codex":[0.9992353,0.000009878755,0.0002318434,0.0001758833,0.0001074431,0.0002396231],"domain_scores_gemma":[0.9995676,0.00005728323,0.00003724307,0.000202121,0.0000695359,0.00006618946],"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":[0.00001029366,0.00001139361,0.0007797793,0.0001141177,0.00003796333,0.000002521428,0.0001564317,0.9962444,0.001026073,0.0001316119,0.00141358,0.00007183361],"study_design_scores_gemma":[0.0009288284,0.00006311945,0.00006755401,0.00009065893,0.00001410312,0.00003284532,0.0001582073,0.9786251,0.006270713,0.000001595298,0.01345654,0.0002907044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007151411,0.0001402065,0.9881622,0.000007564675,0.000375273,0.0008974108,0.000008199979,0.0005578122,0.002699893],"genre_scores_gemma":[0.8575055,0.00003457897,0.1352434,0.00001251774,0.0001921567,0.0003277026,0.00001851062,0.00009399909,0.006571678],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8529189,"threshold_uncertainty_score":0.5340599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968149246035934,"score_gpt":0.2223093930419893,"score_spread":0.2026279005816299,"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."}}