{"id":"W4310891292","doi":"10.1029/2022rs007573","title":"Precoded Large Scale Multi‐User‐MIMO System Using Likelihood Ascent Search for Signal Detection","year":2022,"lang":"en","type":"article","venue":"Radio Science","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"User equipment; Precoding; Computer science; MIMO; Base station; Dirty paper coding; Bit error rate; Spectral efficiency; Multiuser detection; Interference (communication); Multi-user; Algorithm; Real-time computing; Electronic engineering; Telecommunications; Computer network; Engineering; Decoding methods; Code division multiple access; Beamforming","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.005237865,0.0001451979,0.0001862414,0.0004283648,0.003186557,0.0004842226,0.004465918,0.00003907172,0.00001500996],"category_scores_gemma":[0.00003869575,0.0001538291,0.00009047324,0.002941964,0.0002090884,0.0009257181,0.00225321,0.0004243382,0.00001353388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001380008,"about_ca_system_score_gemma":0.0007436406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006787964,"about_ca_topic_score_gemma":0.0000347848,"domain_scores_codex":[0.9959573,0.0004406013,0.0003163053,0.000778073,0.001491819,0.001015877],"domain_scores_gemma":[0.9977444,0.0002110291,0.0001172231,0.001294049,0.0003607988,0.0002724954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001426658,0.001389934,0.004344106,0.0003064919,0.00004776359,0.00002200599,0.009687552,0.346906,0.4251462,0.02269554,0.0004202272,0.1888916],"study_design_scores_gemma":[0.000563261,0.0001152205,0.0008094466,0.00002449315,0.000002814094,0.00005430026,0.0004559478,0.9700197,0.02667453,0.00001898054,0.00108471,0.0001765813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1260955,0.0002120868,0.8719101,0.0001855627,0.0004586277,0.0008169584,0.00001177339,0.000218989,0.00009043992],"genre_scores_gemma":[0.9224182,0.000006297909,0.07713889,0.0000377436,0.0000677623,0.0002128189,0.00000193993,0.00001569796,0.0001006934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7963227,"threshold_uncertainty_score":0.9981111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0403247701968735,"score_gpt":0.3129605468592646,"score_spread":0.2726357766623911,"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."}}