{"id":"W4307804888","doi":"10.36227/techrxiv.21387768.v1","title":"Robust and Efficient Millimeter-Wave Massive MIMO Hybrid Precoding Architecture based on the Perceiver Neural Network","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Precoding; Computer science; MIMO; Context (archaeology); Convolutional neural network; Deep learning; Artificial neural network; Artificial intelligence; Electronic engineering; Channel (broadcasting); Algorithm; Engineering; Telecommunications","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004310773,0.0005497897,0.0003993951,0.0001885609,0.000344823,0.0001909231,0.0003031346,0.000138136,0.001652049],"category_scores_gemma":[0.00003984421,0.0004095854,0.0002417142,0.0001238351,0.00005536175,0.00001925171,0.0005099387,0.00164048,0.000009167084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001596061,"about_ca_system_score_gemma":0.00003269327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001796548,"about_ca_topic_score_gemma":0.000007367237,"domain_scores_codex":[0.9977782,0.0002128705,0.0004103866,0.0006667786,0.0004128059,0.0005189151],"domain_scores_gemma":[0.9986978,0.0003554057,0.00009752366,0.0006609096,0.00004704567,0.0001413517],"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":[0.0000206605,0.00001552635,0.00001497419,0.0001261819,0.00006747045,0.0000169841,0.0003205258,0.9945997,0.0003148605,0.00001202286,0.001275788,0.003215283],"study_design_scores_gemma":[0.0002200004,0.00004926788,0.00009028863,0.000125513,0.00006702136,0.00001096458,0.00009504662,0.9969972,0.001221038,0.0001460004,0.0004871899,0.000490498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3565421,0.0005168857,0.6321205,0.0007564446,0.001547837,0.001188972,0.0001053559,0.0004542079,0.006767727],"genre_scores_gemma":[0.9903529,0.00006971233,0.00771392,0.0009300404,0.0003045479,0.0001777343,0.0001224725,0.0001122248,0.0002164397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6338108,"threshold_uncertainty_score":0.9998356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0393269905936787,"score_gpt":0.2093432928310175,"score_spread":0.1700163022373388,"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."}}