{"id":"W2784187198","doi":"10.1109/glocom.2017.8254815","title":"Quantized Hybrid Precoding for Massive Multiuser MIMO with Insertion Loss","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Precoding; MIMO; Insertion loss; Computer science; Zero-forcing precoding; Control theory (sociology); Electronic engineering; Computer network; Engineering; Electrical engineering; Artificial intelligence; 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":[0.00006088275,0.0001217744,0.0001436943,0.00004037732,0.0001792258,0.00007909349,0.0001236056,0.00003690456,0.00001515723],"category_scores_gemma":[0.00006476392,0.0001030904,0.00002988383,0.00001938046,0.00002026745,0.0005187583,0.00001468922,0.00004636181,0.00001529288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005226618,"about_ca_system_score_gemma":0.000006959684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000189263,"about_ca_topic_score_gemma":0.00007433104,"domain_scores_codex":[0.9994614,0.000005376025,0.0001486479,0.0001509702,0.00005682279,0.000176761],"domain_scores_gemma":[0.9994422,0.00003350697,0.00007745671,0.0003268675,0.0000796684,0.0000403455],"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":[0.0001830767,0.00002635699,0.004538569,0.0003226469,0.0001179297,0.000009297046,0.0002285273,0.9670914,0.01982716,0.0012842,0.0009690826,0.00540171],"study_design_scores_gemma":[0.002239719,0.00005268614,0.001085771,0.0001222154,0.00002257783,0.000008546801,0.0000660983,0.9169818,0.07778377,0.0001224058,0.001194107,0.0003203076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08236849,0.00002073663,0.9142296,0.00005004235,0.0002797133,0.0005447197,0.00000857423,0.0003102852,0.002187811],"genre_scores_gemma":[0.9218753,0.00001332377,0.07714565,0.000007976417,0.00006891383,0.0001089363,0.00001473191,0.00004621323,0.0007189353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8395068,"threshold_uncertainty_score":0.4203902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01830317133241163,"score_gpt":0.2518496052949116,"score_spread":0.2335464339624999,"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."}}