{"id":"W4387883605","doi":"10.1109/icc45041.2023.10279804","title":"Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); University of Ottawa","funders":"","keywords":"Beamforming; Computer science; Transmission (telecommunications); Beam (structure); Overhead (engineering); Selection algorithm; Efficient energy use; Power (physics); Selection (genetic algorithm); Electronic engineering; Real-time computing; Engineering; Telecommunications; Electrical engineering; Artificial intelligence","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":[0.0001828073,0.0001207721,0.0001171101,0.0001167549,0.0001803072,0.00004181214,0.00004329549,0.00006027918,0.00006568765],"category_scores_gemma":[0.00003432748,0.0001259815,0.00006565333,0.0003095608,0.000006633828,0.00005991472,0.00001899277,0.0001006065,0.00001953337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007421168,"about_ca_system_score_gemma":0.00001152971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009153087,"about_ca_topic_score_gemma":0.000008444495,"domain_scores_codex":[0.9991783,0.00001502912,0.0001878372,0.0001622555,0.0001035449,0.0003530067],"domain_scores_gemma":[0.9997232,0.00007429591,0.00001840035,0.00006678124,0.0000554155,0.00006191411],"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":[0.000003316788,0.000003785673,0.00002385407,0.00003422283,0.00001401739,3.858784e-7,0.00006540663,0.9268519,0.068562,0.000122041,0.0003002527,0.004018793],"study_design_scores_gemma":[0.0001481231,0.00002323721,0.000006240969,0.00002420307,0.00001510563,0.000002956899,0.0001065945,0.9608815,0.03610253,0.0001013558,0.002433571,0.0001546037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1956408,0.00005914531,0.8022833,0.000009705934,0.0003978514,0.0001048876,0.000001553881,0.0008112984,0.0006915337],"genre_scores_gemma":[0.9894726,0.00003260617,0.009322721,0.00005117582,0.0003066407,0.0000269002,0.00003104896,0.00005495464,0.0007013866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7938318,"threshold_uncertainty_score":0.5137379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02805668492653982,"score_gpt":0.2548761798292128,"score_spread":0.226819494902673,"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."}}