{"id":"W3040368914","doi":"10.1109/twc.2021.3080672","title":"Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Codebook; Beamforming; Deep learning; MIMO; Channel state information; Precoding; Unsupervised learning; Synchronization (alternating current); Spectral efficiency","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.00009075888,0.0002049306,0.0002371841,0.0001455279,0.0006678224,0.00005493884,0.000388632,0.00008837735,0.00003933483],"category_scores_gemma":[0.00001513834,0.0002571549,0.0001477292,0.0003712654,0.00005958489,0.0002970456,0.000004478633,0.0004253662,0.00003278489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001837869,"about_ca_system_score_gemma":0.00004168067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007410366,"about_ca_topic_score_gemma":0.0001282015,"domain_scores_codex":[0.9989374,0.00008396315,0.0003758438,0.0002168315,0.0001079349,0.0002779775],"domain_scores_gemma":[0.9980953,0.0004242759,0.0000586438,0.001091456,0.000241195,0.00008912422],"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":[0.000005953137,0.00007437998,0.000003454761,0.00005780833,0.00007629605,0.000001140434,0.0003956097,0.9498655,0.01009752,0.0002867175,0.00002195379,0.03911371],"study_design_scores_gemma":[0.000576526,0.00002667158,0.000004272827,0.0001007,0.00006015605,0.00001645446,0.0007403223,0.9221526,0.07198086,0.0000923258,0.003954994,0.0002941319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003080028,0.0004778851,0.9938743,0.000227461,0.0003546516,0.0004069784,0.00004888384,0.000609062,0.0009208121],"genre_scores_gemma":[0.9361875,0.001259724,0.06125847,0.00003665276,0.00002797258,0.0005902561,0.0001281987,0.00009367054,0.0004175573],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9331075,"threshold_uncertainty_score":0.9999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01892477748231792,"score_gpt":0.2488282261060518,"score_spread":0.2299034486237339,"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."}}