{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004566155,0.0004715361,0.000722989,0.0002510718,0.000368972,0.0006652232,0.0005175203,0.0004805734,0.001289284],"category_scores_gemma":[0.001149399,0.0001883869,0.0003223879,0.0004629719,0.0003903143,0.000583348,0.0005571245,0.0005227555,0.0003556228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005286388,"about_ca_system_score_gemma":0.001215859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0027598,"about_ca_topic_score_gemma":0.003602603,"domain_scores_codex":[0.9995932,0.0001385169,0.00001584632,0.0000635117,0.0001408706,0.00004806695],"domain_scores_gemma":[0.9995273,0.0002466144,0.0000511772,0.00004356511,0.0001068806,0.00002433186],"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":[0.0007787202,0.0002765564,0.002010648,0.0002051336,0.0001109953,0.0004258321,0.0002104786,0.6880377,0.05627308,0.01817839,0.002544645,0.2309478],"study_design_scores_gemma":[0.00001262566,0.00006317567,0.0000730982,0.000003432668,0.000004660043,0.00003285375,0.000007133834,0.9962806,0.002874745,0.0003730103,0.0002684202,0.000006268555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07821839,0.0003421771,0.9174274,0.0002592862,0.00006847061,0.00005521037,0.00005472767,0.0005076741,0.003066788],"genre_scores_gemma":[0.8246346,0.0001576281,0.1715689,0.00009874383,0.00002650544,0.00008055026,0.00009064164,0.00001174135,0.003330725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0027598,"threshold_uncertainty_score":0.005487502,"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."}}