{"id":"W2786217313","doi":"10.1109/pimrc.2017.8292406","title":"Joint design of beam selection and precoding for mmWave MU-MIMO systems with lens antenna array","year":2017,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Precoding; MIMO; Computer science; Telecommunications link; Zero-forcing precoding; Antenna (radio); Electronic engineering; Optimization problem; Transmitter; Multi-user MIMO; Algorithm; Telecommunications; Beamforming; Engineering; 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":[0.0001774365,0.00008754447,0.0001452516,0.00004666361,0.0001174875,0.00006486208,0.00003589754,0.00004093055,0.000005109372],"category_scores_gemma":[0.00002818624,0.00007169214,0.00001977468,0.00001762391,0.00001190441,0.0001572517,0.000006746573,0.0000451023,0.000001111363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001888267,"about_ca_system_score_gemma":0.00001115662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001941555,"about_ca_topic_score_gemma":0.00001933168,"domain_scores_codex":[0.9995338,0.00000790654,0.000159047,0.0001180052,0.00005901648,0.0001222578],"domain_scores_gemma":[0.9996558,0.00002092898,0.00006066689,0.0001298475,0.0001013783,0.0000313786],"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":[0.00001975519,0.000007694955,0.0001684042,0.0003172623,0.00006209783,2.994437e-7,0.0001758875,0.06551491,0.9327627,0.00005845067,0.00006180468,0.0008506622],"study_design_scores_gemma":[0.0002648139,0.00006780422,0.0000982761,0.00008282254,0.00001878649,0.00001220904,0.00005795612,0.64699,0.3522511,0.00003377742,0.00003509212,0.00008736379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06016996,0.00007559492,0.9381784,0.00001300897,0.0001147064,0.0003228954,0.000003354414,0.00007791835,0.001044175],"genre_scores_gemma":[0.9709101,0.00006677317,0.02870354,0.000005248261,0.00004083729,0.0000265805,0.000001675368,0.00002093909,0.000224356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9107401,"threshold_uncertainty_score":0.2923521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07161936468517434,"score_gpt":0.2314078395480323,"score_spread":0.159788474862858,"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."}}