{"id":"W2131404560","doi":"10.4236/ijcns.2010.34043","title":"User Selection and Precoding Schemes Based on Partial Channel Information for Broadcast MIMO Systems","year":2010,"lang":"en","type":"article","venue":"International Journal of Communications Network and System Sciences","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Precoding; MIMO; Zero-forcing precoding; Computer science; Channel (broadcasting); Transmitter; Multi-user MIMO; Computer network; Selection (genetic algorithm); Ergodic theory; Channel state information; Channel capacity; Base station; Limit (mathematics); Wireless; Telecommunications; Mathematics; Artificial intelligence","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":[0.0008448634,0.00008821012,0.0001365245,0.0002051034,0.0002647072,0.0002655622,0.0003723853,0.00005595923,9.769791e-7],"category_scores_gemma":[0.0000643891,0.00007702611,0.0000310307,0.0001509351,0.0000704898,0.0008347382,0.00003079035,0.0001429406,7.800557e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004602705,"about_ca_system_score_gemma":0.00003533917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005906363,"about_ca_topic_score_gemma":0.00001125655,"domain_scores_codex":[0.9991145,0.00004286551,0.0004618516,0.0000642096,0.0002055834,0.0001110518],"domain_scores_gemma":[0.9989007,0.0002403276,0.0002921821,0.0001272898,0.0003881265,0.00005140594],"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":[0.00002346905,0.00001066786,0.001064849,0.00005363506,0.0000353655,1.270465e-7,0.000178047,0.9758802,0.0001873631,0.01760274,0.0002536511,0.004709903],"study_design_scores_gemma":[0.0003210263,0.0000597516,0.0001314979,0.0002707267,0.00001061672,0.00006954294,0.0003405111,0.986826,0.00007804655,0.00004606191,0.01176704,0.00007918023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04362852,0.001022425,0.9484501,0.0004250131,0.003984313,0.0004935057,0.00001856303,0.00009433029,0.001883229],"genre_scores_gemma":[0.9813799,0.0001261315,0.01808333,0.00001542691,0.0003451171,0.00003021421,0.000006877632,0.000006562962,0.000006403762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9377514,"threshold_uncertainty_score":0.3141034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768322440036905,"score_gpt":0.2696997994614554,"score_spread":0.2520165750610863,"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."}}