{"id":"W4388040677","doi":"10.1109/pimrc56721.2023.10293760","title":"New Machine Learning Approach for Low Overhead Multi-Beam Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Codebook; Beam (structure); Overhead (engineering); Artificial neural network; Probabilistic logic; Beam diameter; Beam search; Wireless; Artificial intelligence; Deep learning; Power (physics); Machine learning; Algorithm; Optics; Telecommunications; Laser beams","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":[0.0001416516,0.00009874572,0.00009426887,0.0001021509,0.00006493348,0.00002653373,0.00005027888,0.00005849946,0.00006794676],"category_scores_gemma":[0.00002732477,0.00009456973,0.00005396033,0.0001463885,0.000003158915,0.0000845816,0.00001639682,0.000114187,0.0000576995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002592858,"about_ca_system_score_gemma":0.000007942234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000207055,"about_ca_topic_score_gemma":0.000004305207,"domain_scores_codex":[0.9994201,0.000006500296,0.0001555952,0.0001428412,0.00008728341,0.0001876508],"domain_scores_gemma":[0.9997879,0.00002032211,0.00001271668,0.00008691187,0.00002043633,0.00007165777],"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":[0.000004736325,0.000007890433,0.0001420136,0.00008759341,0.00002152171,1.825297e-7,0.0001679311,0.9514117,0.03095267,0.0000197255,0.003143425,0.01404066],"study_design_scores_gemma":[0.000509152,0.00002057389,0.00008130388,0.000008170266,0.000007452585,0.000001113517,0.00003984997,0.9765503,0.02075243,0.0000213249,0.001909067,0.00009926119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008912911,0.00008554454,0.9868708,0.0000161839,0.0001984306,0.0002454142,0.00001023361,0.001501973,0.002158517],"genre_scores_gemma":[0.876179,0.0001211374,0.1118118,0.00004262871,0.0002099066,0.00006217758,0.0004054254,0.00006956667,0.01109829],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8750589,"threshold_uncertainty_score":0.3856442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03634444610508614,"score_gpt":0.2370340748517077,"score_spread":0.2006896287466216,"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."}}