{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002064733,0.0005545457,0.0003406051,0.0001423626,0.0001524072,0.0003818286,0.0006741674,0.000365471,0.001405106],"category_scores_gemma":[0.0004892868,0.0002294008,0.0002850743,0.000210749,0.0002790225,0.0005876861,0.0004972713,0.000638932,0.0004759423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002891598,"about_ca_system_score_gemma":0.0004350697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001828559,"about_ca_topic_score_gemma":0.004329008,"domain_scores_codex":[0.9998643,0.00002129514,0.000005895849,0.00003421228,0.00005371046,0.0000205172],"domain_scores_gemma":[0.9998505,0.00003889003,0.00002081415,0.00002333147,0.00005354804,0.0000128717],"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":[0.0002699842,0.0001519348,0.001676367,0.0002006343,0.0001623989,0.0003299516,0.0001351012,0.448197,0.1560028,0.01383277,0.005324588,0.3737164],"study_design_scores_gemma":[0.00001239545,0.0001030342,0.0002690369,0.000008667762,0.00001748571,0.00009813019,0.000009246191,0.9754146,0.02075723,0.001269751,0.002027488,0.00001283207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03851526,0.0003709983,0.9539927,0.0002006375,0.00009257247,0.00003908805,0.0001068884,0.001062198,0.005619757],"genre_scores_gemma":[0.72637,0.0005079173,0.2630316,0.0003069369,0.00006884166,0.0001061986,0.0003431127,0.00007252333,0.009192857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001828559,"threshold_uncertainty_score":0.004700601,"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."}}