{"id":"W3010766834","doi":"10.1109/icc40277.2020.9149321","title":"RSSI-Based Hybrid Beamforming Design with Deep Learning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Channel state information; Beamforming; Computer science; Overhead (engineering); MIMO; Convex optimization; Spectral efficiency; Latency (audio); Channel (broadcasting); Optimization problem; Electronic engineering; Computer engineering; Wireless; Regular polygon; Algorithm; Telecommunications; Engineering; Mathematics","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":[0.0004387345,0.001025868,0.0006076849,0.0002834087,0.0001709024,0.0006062452,0.000929633,0.0008594192,0.003071939],"category_scores_gemma":[0.0007588135,0.0004717109,0.0004519458,0.0004803849,0.0004330848,0.0007649022,0.0007467477,0.0008374311,0.00129469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003651198,"about_ca_system_score_gemma":0.0005899174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000968851,"about_ca_topic_score_gemma":0.002088355,"domain_scores_codex":[0.9997149,0.00007034395,0.00001419058,0.00005714768,0.0001018985,0.00004153041],"domain_scores_gemma":[0.9996972,0.0001004583,0.00003717214,0.00002664863,0.0001186881,0.00001983017],"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":[0.0001445536,0.00007984789,0.0005370115,0.0001547598,0.00010075,0.00008080437,0.00005604323,0.7458858,0.02668444,0.0134873,0.002891381,0.2098973],"study_design_scores_gemma":[0.000006535971,0.00004141368,0.00004966122,0.00000630436,0.000008116649,0.00002255928,0.000003883422,0.995128,0.002172345,0.001804763,0.0007496644,0.00000672978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002577461,0.0000819229,0.9955172,0.00005220294,0.00001553842,0.00001349871,0.00003027464,0.0002127481,0.001499095],"genre_scores_gemma":[0.5128849,0.0004638876,0.4771051,0.0003533671,0.00008475983,0.0002663099,0.0002908152,0.0001337747,0.008417204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003071939,"threshold_uncertainty_score":0.01027662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03598736308312769,"score_gpt":0.2127482581967746,"score_spread":0.1767608951136469,"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."}}